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Review

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

by
Xin Lyu
1,2,3,
Xiaobing Li
1,2,*,
Dongliang Dang
1,2,
Huashun Dou
1,2,
Kai Wang
1,2 and
Anru Lou
3
1
School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Earth Surface Process and Resource Ecology, Beijing Normal University, Beijing 100875, China
3
College of Life Sciences, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(5), 1096; https://doi.org/10.3390/rs14051096
Submission received: 28 December 2021 / Revised: 11 February 2022 / Accepted: 21 February 2022 / Published: 23 February 2022
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Abstract

:
In recent years, the application of unmanned aerial vehicle (UAV) remote sensing in grassland ecosystem monitoring has increased, and the application directions have diversified. However, there have been few research reviews specifically for grassland ecosystems at present. Therefore, it is necessary to systematically and comprehensively summarize the application of UAV remote sensing in grassland ecosystem monitoring. In this paper, we first analyzed the application trend of UAV remote sensing in grassland ecosystem monitoring and introduced common UAV platforms and remote sensing sensors. Then, the application scenarios of UAV remote sensing in grassland ecosystem monitoring were reviewed from five aspects: grassland vegetation monitoring, grassland animal surveys, soil physical and chemical monitoring, grassland degradation monitoring and environmental disturbance monitoring. Finally, the current limitations and future development directions were summarized. The results will be helpful to improve the understanding of the application scenarios of UAV remote sensing in grassland ecosystem monitoring and to provide a scientific reference for ecological remote sensing research.

Graphical Abstract

1. Introduction

Grassland ecosystems are an important part of terrestrial ecosystems, and accurate monitoring of grassland resources is an important foundation for regional grassland management and sustainable development [1,2,3]. Traditional grassland ecosystem monitoring is mainly based on field surveys. With the development of science and technology, the means of grassland ecosystem monitoring are increasingly enriched and developing towards a large range, high spatiotemporal resolution and high-precision directions [4,5]. At present, satellite remote sensing technology has become an important means of monitoring regional grassland ecosystems, but the images obtained by satellite remote sensing tend to have low spatial resolution. In addition, due to the limitation of satellite transit time, the revisit cycle is long. Thus, there are many challenges in the fine and deep application of remote sensing technology in regional grassland ecosystem monitoring [6,7].
Unmanned aerial vehicles (UAVs) are more flexible, easy to operate and remotely controlled, allowing them to go into areas that are inaccessible to humans [8,9]. UAVs equipped with remote sensing sensors and combined with positioning technology can acquire remote sensing, high-definition images of a large area, and their spatial resolution can be accurate to centimeters or even millimeters [10]. A combination of UAV technology and remote sensing can give full play to their advantages and can provide new ideas for many studies and practices. Due to its advantages of flexibility and high temporal and spatial resolution, UAV remote sensing technology has been gradually applied in many fields, such as geography, ecology and environmental science [11,12,13,14]. It has experienced exponential growth over the past decade in terms of the number of scientific publications and industry applications [15,16] and has gradually become a research hotspot in many disciplines.
In recent years, UAV remote sensing has been increasingly applied to grassland ecosystem monitoring, but few studies have reviewed its application progress [17]. This study aimed to systematically summarize the application progress of UAV remote sensing in grassland ecosystem monitoring, hoping to provide a scientific reference for ecological remote sensing research. This research first analyzed the development trend of UAV remote sensing in grassland ecosystem monitoring and then examined common UAV platforms and carried remote sensing sensors. On this basis, we systematically reviewed the application scenarios of UAV remote sensing in grassland ecosystem monitoring from five aspects: grassland vegetation monitoring, grassland animal investigation, soil physical and chemical monitoring, grassland degradation monitoring and environmental disturbance monitoring. Finally, the current limitations and future directions are discussed.

2. UAV Remote Sensing Technology and Grassland Ecosystem

2.1. Development Trends and Research Hotspots

This paper’s workflow is shown in Figure 1, which summarizes all the work steps/sections and subsections. Firstly, literature retrieval, screening and analysis were carried out using keywords. Then, combined with selected papers, extensive and in-depth reading was carried out to introduce UAV platforms and sensors, and the application scenarios of UAV remote sensing in grassland ecosystem monitoring were summarized and analyzed from five aspects.
Combined with relevant research [8,15,18,19], a literature measurement method was adopted to select research articles from the core database of ISI Web of Science, a commonly used and reliable database for scientific publications, and we conducted two rounds of progressive retrieval. In the first round, the keyword was "UAV remote sensing", while in the second round, the keyword was "grassland", with a retrieval period from 2000 to 2020. The results of the literature analysis (Figure 2a) showed that after 2010, especially after 2015, UAV remote sensing and related research on grasslands showed an exponential growth trend.
Keywords allow for a high generalization and summary of research content. Keyword analysis of relevant literature is helpful to quickly find research hotspots. Therefore, this study used VOS viewer software to analyze the keywords of 125 related research papers from 2000 to 2020. The VOS viewer software can generate visual and attractive network graph using ISI Web of Science data directly and has a sufficient toolkit to analyze the resulting graphs [20,21]. The results (Figure 2b,c) showed that UAV remote sensing was widely applied in grassland ecosystem monitoring, focusing on grassland vegetation monitoring, and key hotspots included coverage, vegetation height, biomass, vegetation index, etc. "LiDAR" was the keyword with a high frequency, indicating that the UAV platform carrying radar sensors had an important application in grassland vegetation monitoring. In addition, some studies also involved forest ecosystems, and these studies mainly focused on the monitoring accuracy of UAV remote sensing in different ecosystems.

2.2. UAV Platforms and Sensors

At present, there are many kinds of unmanned aerial vehicles (UAVs), with significant differences in load, flight time, flight altitude and other parameters; thus, there are different classifications according to different standards. The figures of main UAV platforms and sensors can be found in relevant research [22,23,24,25,26]. Combined with relevant research progress at home and abroad, it can be seen that multirotor UAVs are the most commonly used types [8], mainly including quad-rotor, hexa-rotor and octo-rotor UAVs, among which octo-rotor UAVs are the most widely used. The overall payload of the UAV ranges from 2.5 kg to 6.0 kg, with a maximum payload of 50 kg [23,27,28,29,30,31]. The flight altitude of UAVs varies from 10 to 100 meters, and some scholars have studied the impact of flight altitude on modelling accuracy [32,33].
For different application targets, UAVs can carry a variety of sensors, and commonly used sensors mainly include high-resolution cameras, multispectral sensors, hyperspectral sensors, thermal infrared sensors and light detection and ranging (LiDAR) [34,35,36,37]. Overall, different sensors have their own advantages and disadvantages; thus, there is no clear standard for the selection of sensor types, which should be determined according to the research topic.

3. Application of UAV Remote Sensing in Grassland Ecosystem Monitoring

UAVs equipped with high-resolution sensors, thermal infrared sensors, LiDAR, multispectral sensors, hyperspectral sensors and other sensors can provide rich monitoring data for grassland ecosystem management and constantly show broad application prospects in research and practice [38,39,40]. Based on relevant research at home and abroad, we reviewed the application scenarios of UAV remote sensing in grassland ecosystem monitoring from five aspects: grassland vegetation monitoring, grassland animal surveys, soil physical and chemical monitoring, grassland degradation monitoring and environmental disturbance monitoring.

3.1. Grassland Vegetation Monitoring

3.1.1. Vegetation Species Survey

The survey of grassland vegetation species is an important basis for the health evaluation and scientific management of grassland ecosystems, but there is still no effective and reproducible monitoring method for grassland vegetation species composition at the regional scale. Chinese and international scholars have carried out a large number of studies on grassland vegetation monitoring based on MODIS, Landsat, SPOT and other satellite images [41,42]. However, limited by spatial and spectral resolution, it is very difficult to distinguish grassland vegetation species composition using satellite remote sensing images [43,44]. Although the stent-mounted hyperspectral imager can effectively distinguish grassland vegetation species composition, the monitoring area is very limited [44]. While using UAVs with a high-resolution sensor, it is possible to obtain high spatial resolution images of centimeters in a larger range, capture spectral differences between different vegetation species and effectively improve the accuracy of species identification on smaller vegetation units, which provides technical support for the fine monitoring of grassland vegetation species composition [35].
Some scholars have carried out many studies on grassland vegetation species by using UAVs with high-resolution cameras. For example, Lu and He used UAVs equipped with improved digital cameras to conduct an experiment in the Koffler Scientific Reserve (KSR) located in Southern Ontario, Canada. They monitored spatiotemporal changes in the composition of vegetation species during the growing season (from April to December). The results showed that the images obtained by UAV remote sensing technology had high spatial resolution, and the overall accuracy of species classification was approximately 85%, which could provide a reference method for relevant studies [23]. Meng et al. constructed a machine learning algorithm for grassland vegetation community classification by using UAV technology and combining high-resolution satellite images and topographic indices in alpine meadows of the Qinghai-Tibet Plateau and achieved spatial mapping of the Kobresia pygmaea community [27].
Some scholars have also used UAVs equipped with hyperspectral imagers to monitor the species composition of grassland vegetation. For example, Schmidt et al. identified the spatial distribution of Calluna grassland (in Oranienbaum Heath near Dessau in the Elbe-Mulde lowland in Saxony-Anhalt, Germany) based on the hyperspectral remote sensing technology of UAVs and field survey samples [35]. Yang and Du. used a hyperspectral imaging system based on UAVs to collect images in the desert steppe of Gegentala, Inner Mongolia, China. They increased the spectral differences of species by spectral transformation and constructed the classification characteristics of desert steppe species by using the spectral transformation vegetation index. The final overall classification accuracy and Kappa coefficient were 87% and 0.8, respectively. This study demonstrated the feasibility of UAV hyperspectral remote sensing technology combined with a vegetation index to identify desert steppe vegetation species and provided a reference method for grassland ecological management [28].

3.1.2. Vegetation Parameter Inversion

UAV remote sensing technology can effectively collect high spatiotemporal resolution image data, which are beneficial to extracting the grassland biomass, quality and spatial distribution pattern of grassland types. In recent years, UAV remote sensing technology has been widely used in grassland vegetation parameter retrieval.
Grassland aboveground biomass (AGB) is an important productivity index, and accurate estimation of grassland AGB can provide a reference for regional resource exploitation and sustainable development of the social economy. For example, Sun et al. took natural pastures on the northern slope of the Tianshan Mountains as the research area and used high-resolution and multispectral images obtained by a multirotor UAV and measured data on the ground to establish estimation models of AGB and various vegetation indices by regression analysis, which could provide methods and a basis for the monitoring of natural grassland ecosystems [29].
Fractional vegetation cover (FVC) is a quantitative index reflecting vegetation growth. For example, Meng et al. conducted experiments on the Eastern Qinghai-Tibet Plateau and used unmanned aerial vehicles instead of the traditional sampling method to collect grassland FVC observation data. Then, based on the single-factor parameter model and multifactor parameter/nonparameter model, this paper proposed a suitable method to extract the FVC dataset in the growing season of grassland and analyzed the dynamic change in grassland FVC in the study area [45].
The three-dimensional vertical structure of grassland has an important role in indicating and evaluating grassland ecosystems. In recent years, UAV LiDAR technology has been increasingly applied to the monitoring of grassland vegetation structure parameters, and there have been many comprehensive studies with other vegetation parameters. For example, Wang et al. studied the application ability of UAV LiDAR technology in modelling canopy height and coverage of grassland and used the extracted average canopy height, maximum canopy height and FVC to estimate the AGB [33]. The research results of Zhang et al. in Hulunbuir, Inner Mongolia, also showed that UAV LiDAR data could effectively estimate the canopy height, FVC and other parameters of grasslands, and grassland AGB could be retrieved with higher accuracy through multiparameter joint inversion [32].

3.1.3. Forage Quality Assessment

Forage is the material basis for the survival and development of animal husbandry. Timely mastering of forage quality information is crucial to meet the needs of animal feeding and to ensure the healthy and sustainable development of animal husbandry. Compared with traditional methods, remote sensing technology can be used to assess the quality of grassland without damage, and it is a promising tool. Many studies have shown that remote sensing data can be used to invert common forage quality indices, such as crude protein (CP), nitrogen (N) and neutral detergent fiber (NDF), with high accuracy [46,47].
In recent years, there have been an increasing number of studies on the application of multispectral and hyperspectral imaging systems carried by UAVs in regard to the quality evaluation of pastures. The results showed that UAV remote sensing technology can be used to establish digital models of the growth, yield and quality of all kinds of grassland plants, which can not only improve the accuracy of grassland yield assessments, but also help to realize the efficient production and accurate management of grasslands [48,49,50]. For example, Oliveira et al. obtained visible light (RGB) and hyperspectral images of grassland by UAV remote sensing technology and established yield and quality prediction models of grassland in different harvest periods by using a machine learning algorithm, which was of great help to determine the optimal fertilization strategy and harvest time [51]. Wijesingha et al. carried out research in eight grasslands in Northern Hesse, Germany. They acquired spectral images of grassland by hyperspectral remote sensing based on UAV platforms and established model relationships between the spectral reflectance of grassland with different vegetation compositions and mowing methods and two pasture quality parameters of crude protein and acid detergent fiber. This study provided a promising tool for accurate estimation of grassland forage quality [31].
At present, the linear regression model is the most commonly used modelling method for grassland quality assessment models, but in recent years, machine learning algorithms such as random forest and artificial neural networks have been applied in the estimation of forage quality indices, and they show higher simulation ability than linear regression models [31,52,53]. On the whole, the quality assessment method of grassland grass is developing from the traditional regression statistical model to the machine learning model.

3.1.4. Biodiversity Monitoring

Biodiversity conservation is one of the popular topics in multidisciplinary research. Most traditional remote sensing monitoring methods of biodiversity construct a relationship model between remote sensing data and biodiversity survey data, which is based on statistical principles; therefore, the results are still uncertain [34].
UAV remote sensing technology can provide important technical support for biodiversity monitoring and conservation. The index information can be extracted from the image data acquired by the UAV hyperspectral image acquisition system, and combined with the ground observation data, regional biodiversity can be better reflected to promote the monitoring and protection of biodiversity. For example, Gholizadeh et al. at the University of Nebraska used AISAKESTREL10 airborne hyperspectral imaging to study the relationship between spectral diversity (expressed in coefficients of variation) and α diversity (in terms of species richness and the Shannon index) in a large area steppe restoration experiment in the Wood River, Nebraska, USA. The results showed that airborne spectral diversity can provide a good alternative for α diversity in grasslands and provide strong technical support for the application of UAV remote sensing in grassland biodiversity assessments [54].
On the whole, using UAV remote sensing to obtain higher-resolution image data is conducive to more accurate inversion of biodiversity indicators, thus providing richer decision-making information for grassland biodiversity monitoring and conservation. In fact, there are many species indices of grassland vegetation, among which the most widely used indices mainly include the number of species (such as species richness), heterogeneity or diversity (such as Shannon index) and species uniformity (such as Pielou’s J index) [55,56,57]. Therefore, it is necessary to deepen the research of UAV remote sensing in monitoring grassland biodiversity in the future and realize the effective monitoring of more vegetation species indices.

3.2. Grassland Animal Surveys

3.2.1. Wildlife Protection

The protection of wild animals is of great importance for promoting the sustainable development of socioeconomic–natural complex systems, and the effective management of endangered and invasive species also depends on accurate wildlife surveys. Timely and accurate wildlife surveys are conducive to mastering the status of wildlife resources, knowing their species, quantity and distribution and providing a basis for wildlife protection.
In recent years, UAV remote sensing technology has been gradually applied to grassland wildlife investigation [58,59], and its effectiveness has been widely confirmed [60]. For example, Shao et al. took Maduo County, the source of the Yellow River, as the research area, carried out aerial surveys in the winter and spring of 2017 by using UAVs, and established a UAV image interpretation mark library of Tibetan wild donkey, Tibetan gazelles, rock sheep and other wild animals. By using the human–computer interaction interpretation method, combined with ground synchronous survey verification and statistical data verification, the population numbers of wild ass, gazelle and rock sheep in Maduo County were estimated, which provided an effective and reliable technical method for wildlife investigation in the future [61]. Images taken by UAV remote sensing often need much postprocessing work. If UAV remote sensing is to become an effective tool for wildlife surveys, it is necessary to further improve the ability of massive data processing and automatic target recognition. Therefore, the research and development of automatic processing technology is very important. For example, Gonzalez et al. used a combination of UAVs, thermal imagers and artificial intelligence image processing to develop a system for automatic detection of wildlife based on UAV images that can help decision-makers better understand wildlife population distribution [62].
Overall, machine learning theories and methods have played an important role in wildlife surveys by UAVs in recent years. For example, Norouzzadeh et al. used AlexNet, a deep residual network (ResNet) and other deep neural network models to carry out research on wild animal recognition and classification, and the accuracy of recognizing whether wild animals exist in images reached 96.6% [63]. In the future, with the development of technology and the deepening of research, machine learning theory and methods will help UAV remote sensing achieve more breakthroughs in grassland wildlife surveys.

3.2.2. Pasture Livestock Management

In recent years, the application of UAV remote sensing in livestock investigation has also deepened. UAV remote sensing can effectively identify livestock and track and photograph livestock movements to achieve continuous monitoring of livestock activities. For example, Sun et al. conducted experiments in a pasture on the Qinghai-Tibet Plateau. The results showed that a method based on UAV remote sensing could effectively monitor the behavior of each yak and population indicators, such as yak density and dispersion index, and could realize the spatial dynamic monitoring of yaks in pastures with high frequency, resolution and efficiency [64].
In addition to traditional research on livestock identification, some scholars have carried out research and made progress in real-time livestock monitoring. For example, Wang et al. used the TensorFlow platform to build a livestock intelligent recognition model based on the deep learning method of Mask RCNN and a livestock weight estimation model based on simple linear regression, and they adopted the browser/server (B/S) architecture to build a real-time livestock monitoring system based on UAVs. This system realized UAV video streaming, real-time push–pull function, livestock automatic identification, weight estimation and other functions and then analyzed the intelligent recognition model and livestock weight estimation precision of the model. The results showed that deep learning could better meet the identification and weight estimation needs of common grazing animals such as cattle and sheep in multiscale multiangle UAV images [65].
UAV remote sensing has great application potential in pasture management [66], and it can provide a new solution for grazing intensity assessment. As the most direct influencing factor of grazing activities in grasslands, grazing intensity is an important monitoring index of grassland sustainable management. Grazing intensity refers to the number of grazing livestock per unit grassland area in a certain period of time. Some scholars have used satellite remote sensing to explore regional-scale grazing intensity simulation methods [67,68]. However, due to the limitation of spatial and temporal resolution, accurate monitoring of grazing intensity at the regional scale is still a challenge. In practice, the movement of herds in pastures is usually uneven, and direct observation or indirect methods (such as statistics of animal excrement) are often unable to accurately understand the spatial and temporal distribution of livestock [58]. UAV remote sensing can obtain high-spatial and temporal resolution images, which are suitable for continuous monitoring of livestock activities and can provide an important basis for accurate assessment of grassland grazing intensity. However, on the whole, relevant research on UAV remote sensing has mainly been focused on livestock identification. Although some scholars have conducted research on livestock behavior monitoring, few studies have linked the distribution of grassland livestock with grassland resources. To balance the ecological protection and economic development of grasslands and promote the sustainable management of grasslands, it is necessary to use UAV remote sensing technology to study the relationship between livestock distribution and grassland resources in the future and to accurately evaluate the grazing intensity of grasslands. Therefore, it is necessary to strengthen the research in this area in the future.

3.2.3. Songbird Nest Location

The breeding population of songbirds is often regarded as one of the important indicators of grassland ecosystem health evaluation [69]. In reality, nests are often hidden, and traditional survey methods have high labor costs and are highly invasive to vegetation; therefore, it has always been challenging to accurately locate nests [70]. UAVs are safer and less costly than manned aircraft and have significant advantages over on-site investigations in terms of monitoring efficiency [71].
A combination of UAV platforms and sensor technology can provide a better solution for grassland songbird surveys [72,73]. Some scholars have used UAV remote sensing technology to carry out relevant research. For example, Scholten et al. conducted a study at the Pierce Cedar Creek Institute in the United States, which showed that using UAVs equipped with thermal infrared imagers to locate the nests of Spizella pusilla was 28% faster than traditional nest search technology and less aggressive [74].
UAV remote sensing technology can provide an important application basis for monitoring grassland songbird breeding, but at the same time, it also faces many challenges. For example, the investigation accuracy of the current UAV remote sensing method in grassland songbird density areas needs to be improved. Combined with existing studies [75], it is possible to effectively improve the accuracy of grassland songbird surveys if UAV bioacoustic monitoring is combined with current remote sensing recognition. In addition, there are relatively few studies on remote sensing monitoring of grassland songbirds using UAVs at home and abroad; therefore, it is necessary to carry out further research in the future to provide a scientific basis for grassland ecosystem management.

3.3. Soil Physical and Chemical Monitoring

3.3.1. Soil Moisture Content Monitoring

Soil water content is an important soil physical parameter that is the carrier of soil nutrient circulation and flow and plays a key role in limiting the growth of grassland plants. Traditional ground observations cannot meet the needs of studying continuous dynamic changes in large regions. Satellite remote sensing is an important means to obtain the spatial and temporal distribution information of regional soil moisture [76]. In recent years, advanced scatterometer (ASCAT), soil moisture and ocean salinity (SMOS), soil moisture active and passive (SMAP) and other global soil moisture products based on microwave remote sensing have been widely used, but there are some shortcomings, such as coarse spatial resolution, spatial discontinuity and large RMSE. Therefore, at present, continuous and accurate monitoring of soil moisture content at the regional scale is still facing severe challenges, and the emergence of UAV remote sensing provides new ideas for this.
As a new technology, UAV remote sensing can provide an important basis for soil moisture content monitoring. Although the soil moisture content cannot be directly monitored by UAV remote sensing, it is feasible to establish a model relationship between soil moisture content and surface temperature, vegetation index and other indicators [77]. For example, the research carried out by Zhang et al. on the Tibetan Plateau showed that aerial photogrammetry based on UAVs and multitype cameras performs well in grassland patch pattern investigations and the estimation of surface soil water distribution [78]. Lu et al. took the Loess Plateau of China as an example to quantitatively reveal the relationship between the visible images captured by UAVs and surface soil moisture. The results also showed that there was a significant correlation between surface soil moisture and the brightness of UAV visible images, and surface soil moisture could be estimated based on the brightness of UAV visible images combined with vegetation coverage [79]. Overall, UAV remote sensing is still in an exploratory stage in monitoring soil moisture content in grasslands, and most studies have focused on the estimation of soil moisture content on the surface. Therefore, in the future, more in-depth research should be carried out from two aspects, simulation accuracy and soil depth, to provide effective technical support for practical application.

3.3.2. Soil Nutrient Content Monitoring

The traditional methods of soil nutrient content determination are mainly field sampling and indoor analysis. Although the accuracy is high, it is difficult to meet the needs of monitoring soil dynamic quality. The development of UAV remote sensing technology provides a new method for the rapid acquisition of soil information, which can provide effective technical support for the monitoring of soil nutrient content. Some scholars have carried out research on monitoring grassland soil nutrient content by using UAVs. For example, Sankey et al. conducted experiments in the desert steppe of Severetta National Wildlife Refuge in the United States, and the research results showed that by combining these UAVs with ground-based LiDAR remote sensing data, the plant–soil–nutrient dynamics of grassland ecosystems could be monitored at a fine spatial scale [80].
In summary, the current research on monitoring soil nutrient content with UAV remote sensing mainly focuses on farmland ecosystems, and applications in grassland ecosystems are relatively limited [81]. Most relevant studies have used UAV remote sensing to monitor the soil nutrient content in the grassland surface layer, and the content inversion of soil organic matter is the main method. Research on the inversion of other soil nutrient indices needs to be strengthened. In the future, while improving the accuracy of simulation, it is necessary to explore a variety of UAV remote sensing monitoring methods for soil nutrient content and expand the effective monitoring of soil depth, which will provide more important technical support for the development of traditional soil physical and chemical testing analysis for regional remote sensing monitoring.

3.4. Grassland Degradation Monitoring

3.4.1. Vegetation Degradation Monitoring

Grassland degradation leads to a significant reduction in productivity and deterioration of ecosystem functions, which poses severe challenges to grassland management and sustainable development. Vegetation degradation is the most direct feature in the process of grassland degradation and succession. There is not only degradation of total vegetation, such as biomass and productivity, but also degradation of vegetation structure, such as reduction of plant height and increase of weeds [82,83]. The spatial and temporal resolution of conventional multispectral satellite remote sensing images is relatively low and can only provide information about the characteristics of total vegetation. Although hyperspectral remote sensing satellites can further obtain more in-depth data information and monitor grassland vegetation structure, they are limited in regional coverage and shooting on demand, which cannot meet the monitoring needs of a larger range.
In recent years, with the continuous improvement of UAV performance, the application field has been constantly expanding. Monitoring grasslands with hyperspectral remote sensing imagers carried by UAVs is becoming an important technical means for grassland degradation research. For example, Pi et al. carried out experimental work in a Gegentala grassland in Inner Mongolia and constructed a low-altitude UAV hyperspectral remote sensing platform to collect remote sensing images of degraded grassland. By constructing the grassland degradation indicator feature-three-dimensional-convolutional neural network (GDIF-3D-CNN) classification model and parameter optimization, the accuracy and efficiency of classification were further improved, and the high-precision classification of plant populations in desertification degradation was realized, providing key quantitative data for grassland degradation studies. Compared with hyperspectral satellite remote sensing, UAV hyperspectral remote sensing monitoring technology is more flexible and mobile. This study provides the technical basis for fine monitoring of grassland degradation, which is an important direction of future related research [31].

3.4.2. Monitoring of the Shrub Encroachment Process on Grassland

Shrub encroachment on grassland is one of the core processes of land desertification. The shrub encroachment process has changed the original structure and function of grassland ecosystems, has seriously threatened the sustainable development of grassland and animal husbandry production and has received extensive attention from scholars at home and abroad [84,85,86].
Due to the high cost and time consumption of traditional surface surveys, remote sensing technology has gradually become an important means for monitoring shrub encroachment processes on grasslands at the regional scale. Limited by spatial and temporal resolution and spectral resolution, satellite remote sensing has difficulty meeting the needs of fine grassland monitoring. In recent years, some scholars have explored the monitoring methods of the shrub encroachment process on grassland using UAV remote sensing and have made preliminary progress. For example, Zhao et al. used UAVs to obtain multispectral and LiDAR data, extracted individual shrubs based on the threshold values of the normalized vegetation index (NDVI) and canopy height model (CHM), extracted volume characteristics as the predictors of shrub AGB and compared the ability of estimating shrub AGB with different data sources and multiple regression methods. Finally, a method suitable for predicting shrub AGB (R2 = 0.91, RMSE = 79.98 g) was determined [87]. The research carried out by Prošek et al. in the Western Czech Republic (Central Europe) also showed that the fusion of multispectral and structure-from-motion photogrammetry information obtained by UAVs was a feasible mapping method for shrub species. This indicated that UAV remote sensing could provide important technical support for monitoring the shrub encroachment process on grasslands [88]. With the development of technology and the deepening of research, it will help to improve the understanding of grassland ecological structure, process and function.

3.4.3. Soil Salinization Monitoring

Soil salinization is an important factor limiting grassland productivity and improving pasture quality. The development of satellite remote sensing technology provides the possibility for regional monitoring of soil salinization. Compared with multispectral images, hyperspectral images are more widely used due to their advantages of multiple bands and high spectral resolution. For example, Pang et al. used genetic algorithm modelling based on hyperspectral data to improve the prediction accuracy of soil salt content. However, the low spatial and temporal resolution and vulnerability to environmental interference limit the further application of spaceborne or airborne remote sensing images [89].
The development of UAV remote sensing provides a simple and economical method for monitoring soil salinization. Some scholars have indirectly obtained soil salinization information by measuring the salt stress degree of plants by using UAV remote sensing [90,91]. At the same time, some scholars have directly evaluated the potential of UAV remote sensing in soil salinity estimation. For example, Hu et al. carried out research in Aksu, Western Xinjiang, China. They used a UAV platform equipped with a hyperspectral sensor to collect data, combined GF-2 satellite data and measured data to conduct random forest modelling and realized spatial mapping of soil salt content in regions with different vegetation coverages. This indicated that the use of UAV hyperspectral remote sensing systems could effectively measure, monitor and evaluate soil salt content and provide scientific support for sufficient management [92].
On the whole, UAV remote sensing has been widely used in monitoring the soil salinization of farmland ecosystems, but its application in grassland ecosystems is still in a preliminary exploratory stage [90]. In addition, relevant studies have mainly focused on the monitoring of surface soil salt content by using UAV remote sensing, and the soil depth is generally 0–20 cm. It is believed that UAV remote sensing will help to further solve the challenges of remote sensing technology in deeper soil monitoring, thus promoting the overall progress of remote sensing science and technology.

3.5. Environmental Disturbance Monitoring

3.5.1. Grassland Rodent Monitoring

Rodent infestation in grasslands is a serious hazard to grassland ecosystem health, and increased pika activity is often considered an important cause of grassland degradation. Pika holes are usually small, and traditional survey methods have difficulty obtaining effective statistics on the ground and lack spatial information. In addition, most satellite remote sensing cannot meet the requirements of high spatial and temporal resolution for grassland biological disaster monitoring. The appearance of UAV remote sensing provides a new technical means for monitoring grassland rodents.
Some scholars have studied the monitoring of rodent pests in grasslands by using UAV remote sensing. For example, Zhang et al. realized the dynamic monitoring of pika holes and bare patches in alpine meadows on the Tibetan Plateau by using UAV remote sensing. With the deepening of research, interpretation methods have gradually changed from manual visual interpretation to human–computer interactive interpretation [93]. For example, Tang et al. carried out experiments on the Tibetan Plateau using UAVs to collect high spatial resolution remote sensing images and compared the performance of two image classification methods based on FCLS decision tree classification (FDC) and object-oriented classification (OBC) in pika-hole information extraction. The results could provide a basis for pika control and ecosystem management on the Tibetan Plateau [94]. At present, UAV remote sensing monitoring of grassland rodent pests is developing towards automatic identification. For example, Yi et al. developed a tool for monitoring and analyzing small-scale fragmented landscapes, which could automatically identify pika holes and conduct manual correction, providing an important technical basis for pika disaster research. Relevant studies have shown that UAV remote sensing technology can effectively improve the monitoring efficiency of grassland rodents [95].
At present, there have been many studies on the monitoring of rodent pests in grasslands using UAV remote sensing, but few studies on other grassland biological disasters have been conducted. It is necessary to further strengthen this research in the future. With the continuous development of UAV remote sensing technology, the monitoring and application of grassland biological disasters will be expanded to provide more abundant decision-making information for understanding the relationship between biological disasters and related ecological processes, as well as scientific management of grassland ecosystems.

3.5.2. Fire Monitoring and Early Warning

Grassland fires are a serious natural disaster, threatening the safety of animals and plants, destroying the ecological environment and easily causing irreparable losses to the economy. Fire monitoring is one of the main reasons for grassland ecological monitoring, which plays an important role in grassland ecological construction and social stability and has become an important task in protecting grassland resources. Remote sensing technology has become a widely used fire monitoring method because grassland fires often spread rapidly and occur in sparsely populated areas where roads are difficult to reach. Due to the limitation of temporal and spatial resolution of single-source remote sensing data, people are often unable to monitor fire in a timely and effective manner. Multisource satellite data fusion is often used in practical fire monitoring [96,97].
UAV remote sensing can provide high-resolution images in time. Through the thermal infrared camera imaging system carried by UAVs, high-resolution images of land surface temperature can also be obtained, compensating for the lack of high spatiotemporal resolution thermal infrared images. UAV remote sensing is applied to monitoring prairie fires and early warnings and can not only help to grasp the dynamic change in fire, but also provide fire spreading simulations using pixels based on the images of the initial field, which helps us to timely and accurately predict risk, understand the situation and scientifically guide disaster relief; therefore, it has important applications in prairie fire early warning and fire monitoring [98,99].
On the whole, UAV remote sensing is currently widely used in monitoring forest fires [36,37,100], but there are few studies on the direct application of UAV remote sensing to monitor grassland fires. Therefore, it is necessary to strengthen the application of UAV remote sensing in grassland fire monitoring and early warning in the future.

4. Summary and Prospects

4.1. Limitations and Challenges of UAV Applications

In recent years, the application of UAV remote sensing in grassland ecosystem monitoring has been developing and improving, but there are still some limitations and challenges, mainly in regard to the following aspects:
(1)
UAV remote sensing technology has certain limitations. In terms of platforms, the endurance of UAVs is relatively limited, their flight stability is not strong enough in areas with large terrain fluctuations and the lack of flight altitude limits the image size. In terms of sensors, hyperspectral or LiDAR sensors are still relatively expensive, which limits the expansion of applications to a certain extent. In terms of data integration, UAVs are often equipped with a single sensor, while multisensor integration is beneficial to improve monitoring accuracy and efficiency. In terms of data processing, the technology of mass data processing needs to be improved due to the rich structure and variety of data obtained.
(2)
The application scenarios of UAV remote sensing in grassland ecosystem monitoring need to be expanded and deepened. At present, the application of UAV remote sensing in grassland ecosystem monitoring is mainly vegetation monitoring, but its application in animal investigation and soil physical and chemical monitoring is still limited. Moreover, the application scenarios need to be deepened. For example, at present, in the monitoring of grassland soil physical and chemical properties using UAV remote sensing, most studies have focused on the estimation of physical and chemical properties of surface soil, which needs further research. It is also conducive to promoting the technological progress of remote sensing science.
(3)
The combination of UAV remote sensing with ground data and satellite data needs to be strengthened. At present, most studies at home and abroad have focused on the integrated modelling of UAV remote sensing data and measured ground data, while there have been few studies on the fusion of UAV remote sensing data and satellite remote sensing data. In fact, UAV remote sensing is more suitable for monitoring small- and medium-sized regions. Only when it is combined with satellite remote sensing data can regional and even global grassland ecosystems be monitored, and a three-dimensional grassland ecological environmental monitoring perception system can be formed.
(4)
The correlation between the scientific research of UAV remote sensing monitoring and practical decision making of grassland management is insufficient. At present, the work of scholars at home and abroad have mainly focused on the research of UAV remote sensing monitoring technology, and the research supporting grassland management decisions needs to be strengthened. For example, on the basis of grassland vegetation monitoring and animal surveys, it is necessary to explore suitable regional sustainable grassland management schemes from the perspective of livestock balance. Moreover, it is necessary to deepen the application research of UAV remote sensing real-time monitoring advantages in grassland management.

4.2. Future Directions

Based on the research progress at home and abroad, we analyzed the future directions of UAV remote sensing in grassland ecosystem monitoring from four aspects, as shown in Figure 3. In the future, with the improvement of technology and the expansion of spatial scale, the application scenarios of UAV remote sensing in grassland ecosystem monitoring will be further deepened, which can provide stronger decision-making support for grassland management. A detailed description is as follows.
(1)
UAV remote sensing technology will be further improved and developed in the direction of precision and intelligence. The endurance, stability, flight height and other performance parameters of UAV platforms will be significantly improved, and the development of integrated modes such as self-networking can expand the effective monitoring range of UAV remote sensing. The cost of sensors can be reduced, and sensors can develop towards the integration of "radar point cloud" + "hyperspectral" multisensors. Machine learning gradually will become an important technical means to provide a technical basis for automatic processing and analysis of massive monitoring data [101].
(2)
UAV remote sensing can provide important technical support for the study of the combined relationship of "structure–process (function)–service–human well-being" in grassland ecosystems. The monitoring ability of UAV remote sensing will be significantly improved, which means it can meet the needs of fine monitoring of grassland structural properties, including the following: vegetation types, height and species richness; help to monitoring grassland process (function), such as nutrient change and forage production; provide sufficient data for accurate assessment of ecosystem services, such as the net primary production and soil conservation; and ultimately improve human well-being through scientific management of grasslands [102,103,104].
(3)
A space–sky–terrestrial integrated monitoring network of “satellite remote sensing–low-altitude remote sensing–ground monitoring” will form and promote the continuous expansion of the space monitoring scale. UAV remote sensing will be more closely integrated with satellite remote sensing and traditional ground monitoring. Thus, the space–sky–terrestrial integrated monitoring network will form to meet the application requirements of more scenes and develop towards multiscale, multilevel, precision and regional monitoring, opening up new development space for the precision and information management of grassland [105,106,107].
(4)
The decision-making support of UAV remote sensing for grassland management will be enhanced. Massive monitoring data of grassland ecosystems can be obtained by UAV remote sensing, and a decision model can be established based on the monitoring database to further guide grassland management [108]. With the development of UAV remote sensing technology, real-time monitoring systems of pastures based on UAVs can realize analysis and decision making through video streams and guide the scientific management of pastures [80].

5. Conclusions

In this study, the application of UAV remote sensing in grassland ecosystem monitoring was systematically summarized. We studied and analyzed the development trend of UAV remote sensing applications in grassland ecosystem monitoring, introduced the commonly used UAV platforms and sensors, systematically reviewed several application scenarios of UAV remote sensing in grassland ecosystem monitoring and summarized the current application limitations and future development directions. The application cases of UAV remote sensing in grassland ecosystem monitoring have increased rapidly in recent years, but the application scenarios are mainly grassland vegetation monitoring, and the application scenarios in animal surveys, soil physical and chemical monitoring and other aspects are still limited. UAV remote sensing still has limitations in performance, price, technology and application scenarios, and its relevance to realistic grassland management decision making is still insufficient. It is believed that with the continuous development of UAV remote sensing technology in the future, the application scenarios of UAV remote sensing in grassland ecosystem monitoring will continue to expand and deepen, forming a space–sky–terrestrial integrated monitoring network of "satellite remote sensing–low-altitude remote sensing–ground monitoring", and further strengthen the ability to guide the sustainable management of grasslands. This study can provide more systematic and comprehensive reference information for ecological remote sensing research and can promote the application of UAV remote sensing in grassland ecosystem monitoring. In future studies, we will strengthen the research in the field of UAVs to provide new technical knowledge for relevant research.

Author Contributions

Conceptualization, X.L. (Xin Lyu), X.L. (Xiaobing Li) and D.D.; Data curation, X.L. (Xin Lyu); Funding acquisition, X.L. (Xiaobing Li); Methodology, X.L. (Xin Lyu), D.D., H.D. and K.W.; Project administration, X.L. (Xiaobing Li); Software, X.L. (Xin Lyu); Supervision, X.L. (Xiaobing Li) and A.L.; Formal analysis, X.L. (Xin Lyu); Writing (original draft preparation), X.L., X.L. (Xiaobing Li), D.D. and H.D.; Writing (reviewing and editing), X.L. (Xin Lyu), X.L. (Xiaobing Li), D.D. and H.D.; and Visualization, X.L. (Xin Lyu). All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Key Science and Technology Special Program of Inner Mongolia Autonomous Region (grant no. 2021ZD0015 and 2021ZD0011).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lyu, X.; Li, X.; Dang, D.; Dou, H.; Xuan, X.; Liu, S.; Li, M.; Gong, J. A new method for grassland degradation monitoring by vegetation species composition using hyperspectral remote sensing. Ecol. Indic. 2020, 114, 106310. [Google Scholar] [CrossRef]
  2. Balasubramanian, D.; Zhou, W.; Ji, H.; Grace, J.; Bai, X.; Song, Q.; Liu, Y.; Sha, L.; Fei, X.; Zhang, X.; et al. Environmental and management controls of soil carbon storage in grasslands of southwestern China. J. Environ. Manag. 2020, 254, 109810. [Google Scholar] [CrossRef]
  3. Villoslada Peciña, M.; Ward, R.D.; Bunce, R.G.H.; Sepp, K.; Kuusemets, V.; Luuk, O. Country-scale mapping of ecosystem services provided by semi-natural grasslands. Sci. Total Environ. 2019, 661, 212–225. [Google Scholar] [CrossRef] [PubMed]
  4. Al-Yaari, A.; Wigneron, J.P.; Dorigo, W.; Colliander, A.; Pellarin, T.; Hahn, S.; Mialon, A.; Richaume, P.; Fernandez-Moran, R.; Fan, L.; et al. Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements. Remote Sens. Environ. 2019, 224, 289–303. [Google Scholar] [CrossRef]
  5. Joiner, J.; Guanter, L.; Lindstrot, R.; Voigt, M.; Vasilkov, A.P.; Middleton, E.M.; Huemmrich, K.F.; Yoshida, Y.; Frankenberg, C. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: Methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 2013, 6, 2803–2823. [Google Scholar] [CrossRef] [Green Version]
  6. Li, C.; Han, W.; Peng, M. Improving the spatial and temporal estimating of daytime variation in maize net primary production using unmanned aerial vehicle-based remote sensing. Int. J. Appl. Earth Obs. Geoinf. 2021, 103, 102467. [Google Scholar] [CrossRef]
  7. Gao, F.; Anderson, M.C.; Zhang, X.Y.; Yang, Z.W.; Alfieri, J.G.; Kustas, W.P.; Mueller, R.; Johnson, D.M.; Prueger, J.H. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sens. Environ. 2017, 188, 9–25. [Google Scholar] [CrossRef] [Green Version]
  8. Feng, L.; Chen, S.; Zhang, C.; Zhang, Y.; He, Y. A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping. Comput. Electron. Agric. 2021, 182, 106033. [Google Scholar] [CrossRef]
  9. Isokangas, E.; Davids, C.; Kujala, K.; Rauhala, A.; Ronkanen, A.-K.; Rossi, P.M. Combining unmanned aerial vehicle-based remote sensing and stable water isotope analysis to monitor treatment peatlands of mining areas. Ecol. Eng. 2019, 133, 137–147. [Google Scholar] [CrossRef]
  10. Zheng, H.; Zhou, X.; He, J.; Yao, X.; Cheng, T.; Zhu, Y.; Cao, W.; Tian, Y. Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV). Comput. Electron. Agric. 2020, 169, 105223. [Google Scholar] [CrossRef]
  11. Qin, R.J. An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images. Remote Sens. 2014, 6, 7911–7932. [Google Scholar] [CrossRef] [Green Version]
  12. Honkavaara, E.; Saari, H.; Kaivosoja, J.; Polonen, I.; Hakala, T.; Litkey, P.; Makynen, J.; Pesonen, L. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sens. 2013, 5, 5006–5039. [Google Scholar] [CrossRef] [Green Version]
  13. Adao, T.; Hruska, J.; Padua, L.; Bessa, J.; Peres, E.; Morais, R.; Sousa, J.J. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens. 2017, 9, 1110. [Google Scholar] [CrossRef] [Green Version]
  14. Reis, B.P.; Martins, S.V.; Fernandes Filho, E.I.; Sarcinelli, T.S.; Gleriani, J.M.; Leite, H.G.; Halassy, M. Forest restoration monitoring through digital processing of high resolution images. Ecol. Eng. 2019, 127, 178–186. [Google Scholar] [CrossRef] [Green Version]
  15. Libran-Embid, F.; Klaus, F.; Tscharntke, T.; Grass, I. Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes-A systematic review. Sci. Total Environ. 2020, 732, 139204. [Google Scholar] [CrossRef]
  16. Pajares, G. Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogramm. Eng. Remote Sens. 2015, 81, 281–329. [Google Scholar] [CrossRef] [Green Version]
  17. Gao, J.T.; Sun, F.D.; Huo, F.; Zhang, L.B.; Zhou, S.; Yang, T.Y.; Dabian, Z.X. Application and evaluation of unmanned aerial vehicle remote sensing in grassland animal and plant monitoring. Acta Agrestia Sin. 2021, 29, 1–9. [Google Scholar]
  18. Senf, C.; Seidl, R.; Hostert, P. Remote sensing of forest insect disturbances: Current state and future directions. Int. J. Appl. Earth Obs. Geoinf. 2017, 60, 49–60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Zheng, Z.J.; Du, S.S.; Taubenböck, H.; Zhang, X.Y. Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances. Remote Sens. Environ. 2022, 271, 112913. [Google Scholar] [CrossRef]
  20. Modak, N.M.; Sinha, S.; Raj, A.; Panda, S.; Merigó, J.M.; Lopes de Sousa Jabbour, A.B. Corporate social responsibility and supply chain management: Framing and pushing forward the debate. J. Clean. Prod. 2020, 273, 122981. [Google Scholar] [CrossRef]
  21. Laengle, S.; Merigo, J.M.; Modak, N.M.; Yang, J.B. Bibliometrics in operations research and management science: A university analysis. Ann. Oper. Res. 2020, 294, 769–813. [Google Scholar] [CrossRef]
  22. Radoglou-Grammatikis, P.; Sarigiannidis, P.; Lagkas, T.; Moscholios, I. A compilation of UAV applications for precision agriculture. Comput. Netw. 2020, 172, 107148. [Google Scholar] [CrossRef]
  23. Lu, B.; He, Y.H. Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS J. Photogramm. 2017, 128, 73–85. [Google Scholar] [CrossRef]
  24. Awais, M.; Li, W.; Cheema, M.J.M.; Hussain, S.; AlGarni, T.S.; Liu, C.C.; Ali, A. Remotely sensed identification of canopy characteristics using UAV-based imagery under unstable environmental conditions. Environ. Technol. Innov. 2021, 22, 101465. [Google Scholar] [CrossRef]
  25. Wang, F.M.; Yi, Q.X.; Hu, J.H.; Xie, L.L.; Yao, X.P.; Xu, T.Y.; Zheng, J.Y. Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield. Int. J. Appl. Earth Obs. Geoinf. 2021, 102, 102397. [Google Scholar] [CrossRef]
  26. Bolourian, N.; Hammad, A. LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection. Automat. Constr. 2020, 117, 103250. [Google Scholar] [CrossRef]
  27. Meng, B.P.; Yang, Z.G.; Yu, H.Y.; Qin, Y.; Sun, Y.; Zhang, J.G.; Chen, J.J.; Wang, Z.W.; Zhang, W.; Li, M.; et al. Mapping of Kobresia pygmaea Community Based on Umanned Aerial Vehicle Technology and Gaofen Remote Sensing Data in Alpine Meadow Grassland: A Case Study in Eastern of Qinghai-Tibetan Plateau. Remote Sens. 2021, 13, 2483. [Google Scholar] [CrossRef]
  28. Yang, H.; Du, J. Classification of desert steppe species based on unmanned aerial vehicle hyperspectral remote sensing and continuum removal vegetation indices. Optik 2021, 247, 167877. [Google Scholar] [CrossRef]
  29. Sun, S.Z.; Wang, C.J.; Yin, X.J.; Wang, W.Q.; Liu, W.; Zhang, Y.; Zhao, Q.Z. Estimating aboveground biomass of natural grassland based on multispectral images of Unmanned Aerial Vehicles. J. Remote Sens. 2018, 22, 848–856. [Google Scholar]
  30. Pi, W.; Du, J.; Bi, Y.; Gao, X.; Zhu, X. 3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research. Ecol. Inform. 2021, 62, 101278. [Google Scholar] [CrossRef]
  31. Wijesingha, J.; Astor, T.; Schulze-Bruninghoff, D.; Wengert, M.; Wachendorf, M. Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy. Remote Sens. 2020, 12, 126. [Google Scholar] [CrossRef] [Green Version]
  32. Zhang, X.; Bao, Y.H.; Wang, D.L.; Xin, X.P.; Ding, L.; Xu, D.W.; Hou, L.L.; Shen, J. Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland. Remote Sens. 2021, 13, 656. [Google Scholar] [CrossRef]
  33. Wang, D.; Xin, X.; Shao, Q.; Brolly, M.; Zhu, Z.; Chen, J. Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar. Sensors 2017, 17, 180. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Guo, Q.H.; Wu, F.F.; Hu, T.Y.; Chen, L.H.; Liu, J.; Zhao, X.Q.; Gao, S.; Pang, S.X. Perspectives and prospects of unmanned aerial vehicle in remote sensing monitoring of biodiversity. Biodivers. Sci. 2016, 24, 1267–1278. [Google Scholar] [CrossRef]
  35. Schmidt, J.; Fassnacht, F.E.; Neff, C.; Lausch, A.; Kleinschmit, B.; Förster, M.; Schmidtlein, S. Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status. Int. J. Appl. Earth Obs. Geoinf. 2017, 60, 61–71. [Google Scholar] [CrossRef]
  36. Yuan, G.; Wang, Y.J.; Zhao, F.; Wang, T.; Zhang, L.X.; Hao, M.; Yan, S.Y.; Dang, L.B.; Peng, B. Accuracy assessment and scale effect investigation of UAV thermography for underground coal fire surface temperature monitoring. Int. J. Appl. Earth Obs. Geoinf. 2021, 102, 102426. [Google Scholar] [CrossRef]
  37. Tang, L.N.; Shao, G.F. Drone remote sensing for forestry research and practices. J. For. Res. 2015, 26, 791–797. [Google Scholar] [CrossRef]
  38. Jin, X.L.; Liu, S.Y.; Baret, F.; Hemerle, M.; Comar, A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens. Environ. 2017, 198, 105–114. [Google Scholar] [CrossRef] [Green Version]
  39. Rey, N.; Volpi, M.; Joost, S.; Tuia, D. Detecting animals in African Savanna with UAVs and the crowds. Remote Sens. Environ. 2017, 200, 341–351. [Google Scholar] [CrossRef] [Green Version]
  40. Webster, C.; Westoby, M.; Rutter, N.; Jonas, T. Three-dimensional thermal characterization of forest canopies using UAV photogrammetry. Remote Sens. Environ. 2018, 209, 835–847. [Google Scholar] [CrossRef] [Green Version]
  41. Huang, C.; Geiger, E.L.; Van Leeuwen, W.J.D.; Marsh, S.E. Discrimination of invaded and native species sites in a semi-desert grassland using MODIS multi-temporal data. Int. J. Remote Sens. 2009, 30, 897–917. [Google Scholar] [CrossRef] [Green Version]
  42. Hall, K.; Johansson, L.J.; Sykes, M.T.; Reitalu, T.; Larsson, K.; Prentice, H.C. Inventorying management status and plant species richness in semi-natural grasslands using high spatial resolution imagery. Appl. Veg. Sci. 2010, 13, 221–233. [Google Scholar] [CrossRef]
  43. Sun, Y.; Yi, S.H.; Hou, F.J. Unmanned aerial vehicle methods makes species composition monitoring easier in grasslands. Ecol. Indic. 2018, 95, 825–830. [Google Scholar] [CrossRef]
  44. Lopatin, J.; Fassnacht, F.E.; Kattenborn, T.; Schmidtlein, S. Mapping plant species in mixed grassland communities using close range imaging spectroscopy. Remote Sens. Environ. 2017, 201, 12–23. [Google Scholar] [CrossRef]
  45. Meng, B.P.; Jinlong, G.L.; Liang, T.G.; Cui, X.; Ge, J.; Yin, J.P.; Feng, Q.S.; Xie, H.J. Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China. Remote Sens. 2018, 10, 320. [Google Scholar] [CrossRef] [Green Version]
  46. Pullanagari, R.R.; Yule, I.J.; Tuohy, M.P.; Hedley, M.J.; Dynes, R.A.; King, W.M. In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture. Precis. Agric. 2012, 13, 351–369. [Google Scholar] [CrossRef]
  47. Safari, H.; Fricke, T.; Wachendorf, M. Determination of fibre and protein content in heterogeneous pastures using field spectroscopy and ultrasonic sward height measurements. Comput. Electron. Agric. 2016, 123, 256–263. [Google Scholar] [CrossRef]
  48. Hardin, P.J.; Jackson, M.W. An unmanned aerial vehicle for rangeland photography. Rangel. Ecol. Manag. 2005, 58, 439–442. [Google Scholar] [CrossRef]
  49. Capolupo, A.; Kooistra, L.; Berendonk, C.; Boccia, L.; Suomalainen, J. Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches. ISPRS Int. J. Geo-Inf. 2015, 4, 2792–2820. [Google Scholar] [CrossRef]
  50. Nasi, R.; Viljanen, N.; Kaivosoja, J.; Alhonoja, K.; Hakala, T.; Markelin, L.; Honkavaara, E. Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features. Remote Sens. 2018, 10, 1082. [Google Scholar] [CrossRef] [Green Version]
  51. Oliveira, R.A.; Näsi, R.; Niemeläinen, O.; Nyholm, L.; Alhonoja, K.; Kaivosoja, J.; Jauhiainen, L.; Viljanen, N.; Nezami, S.; Markelin, L.; et al. Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry. Remote Sens. Environ. 2020, 246, 111830. [Google Scholar] [CrossRef]
  52. Castro, P.A.; Garbulsky, M.F. Spectral normalized indices related with forage quality in temperate grasses: Scaling up from leaves to canopies. Int. J. Remote Sens. 2018, 39, 3138–3163. [Google Scholar] [CrossRef]
  53. Pullanagari, R.R.; Kereszturi, G.; Yule, I. Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression. Remote Sens. 2018, 10, 1117. [Google Scholar] [CrossRef] [Green Version]
  54. Gholizadeh, H.; Gamon, J.A.; Townsend, P.A.; Zygielbaum, A.I.; Helzer, C.J.; Hmimina, G.Y.; Yu, R.; Moore, R.M.; Schweiger, A.K.; Cavender-Bares, J. Detecting prairie biodiversity with airborne remote sensing. Remote Sens. Environ. 2019, 221, 38–49. [Google Scholar] [CrossRef]
  55. De Bello, F.; Leps, J.; Sebastia, M.T. Variations in species and functional plant diversity along climatic and grazing gradients. Ecography 2006, 29, 801–810. [Google Scholar] [CrossRef]
  56. Spellerberg, I.F.; Fedor, P.J. A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon-Wiener’ Index. Glob. Ecol. Biogeogr. 2003, 12, 177–179. [Google Scholar] [CrossRef] [Green Version]
  57. Hickman, K.R.; Hartnett, D.C.; Cochran, R.C.; Owensby, C.E. Grazing management effects on plant species diversity in tallgrass prairie. J. Range Manag. 2004, 57, 58–65. [Google Scholar] [CrossRef]
  58. Mulero-Pazmany, M.; Barasona, J.A.; Acevedo, P.; Vicente, J.; Negro, J.J. Unmanned Aircraft Systems complement biologging in spatial ecology studies. Ecol. Evol. 2015, 5, 4808–4818. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Linchant, J.; Lisein, J.; Semeki, J.; Lejeune, P.; Vermeulen, C. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Rev. 2015, 45, 239–252. [Google Scholar] [CrossRef]
  60. Jones, G.P.; Pearlstine, L.G.; Percival, H.F. An assessment of small unmanned aerial vehicles for wildlife research. Wildl. Soc. Bull. 2006, 34, 750–758. [Google Scholar] [CrossRef]
  61. Shao, Q.Q.; Guo, X.J.; Li, Y.Z.; Wang, Y.C.; Wang, D.L.; Liu, J.Y.; Fan, J.W.; Yang, F. Using UAV remote sensing to analyze the population and distribution of large wild herbivores. J. Remote Sens. 2018, 22, 497–507. [Google Scholar]
  62. Gonzalez, L.F.; Montes, G.A.; Puig, E.; Johnson, S.; Mengersen, K.; Gaston, K.J. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors 2016, 16, 97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Norouzzadeh, M.S.; Nguyen, A.; Kosmala, M.; Swanson, A.; Palmer, M.S.; Packer, C.; Clune, J. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. USA 2018, 115, E5716–E5725. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Sun, Y.; Yi, S.H.; Hou, F.J.; Luo, D.W.; Hu, J.Q.; Zhou, Z.Y. Quantifying the Dynamics of Livestock Distribution by Unmanned Aerial Vehicles (UAVs): A Case Study of Yak Grazing at the Household Scale. Rangel. Ecol. Manag. 2020, 73, 642–648. [Google Scholar] [CrossRef]
  65. Wang, D.L.; Liao, X.H.; Zhang, Y.J.; Cong, N.; Ye, H.P.; Shao, Q.Q.; Xin, X.P. Grassland livestock real-time detection and weight estimation based on unmanned aircraft system video streams. Chin. J. Ecol. 2021, 40, 4099–4108. [Google Scholar]
  66. Rango, A.; Laliberte, A.; Herrick, J.E.; Winters, C.; Havstad, K.; Steele, C.; Browning, D. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. J. Appl. Remote Sens. 2009, 3, 033542. [Google Scholar]
  67. Chi, D.; Wang, H.; Li, X.; Liu, H.; Li, X. Assessing the effects of grazing on variations of vegetation NPP in the Xilingol Grassland, China, using a grazing pressure index. Ecol. Indic. 2018, 88, 372–383. [Google Scholar] [CrossRef]
  68. Li, X.; Lyu, X.; Dou, H.; Dang, D.; Li, S.; Li, X.; Li, M.; Xuan, X. Strengthening grazing pressure management to improve grassland ecosystem services. Glob. Ecol. Conserv. 2021, 31, e01782. [Google Scholar] [CrossRef]
  69. Morrison, M.L. Bird Populations as Indicators of Environmental Change. In Current Ornithology; Johnston, R.F., Ed.; Springer: Boston, MA, USA, 1986; pp. 429–451. [Google Scholar]
  70. Dion, N.; Hobson, K.A.; Lariviere, S. Interactive effects of vegetation and predators on the success of natural and simulated nests of grassland songbirds. Condor 2000, 102, 629–634. [Google Scholar] [CrossRef]
  71. McClelland, G.T.W.; Bond, A.L.; Sardana, A.; Glass, T. Rapid Population Estimate of a Surface-Nesting Seabird on a Remote Island Using a Low-Cost Unmanned Aerial Vehicle. Mar. Ornithol. 2016, 44, 215–220. [Google Scholar]
  72. Wilson, A.M.; Barr, J.; Zagorski, M. The feasibility of counting songbirds using unmanned aerial vehicles. Auk 2017, 134, 350–362. [Google Scholar] [CrossRef]
  73. Galligan, E.W.; Bakken, G.S.; Lima, S.L. Using a thermographic imager to find nests of grassland birds. Wildl. Soc. Bull. 2003, 31, 865–869. [Google Scholar]
  74. Scholten, C.N.; Kamphuis, A.J.; Vredevoogd, K.J.; Lee-Strydhorst, K.G.; Atma, J.L.; Shea, C.B.; Lamberg, O.N.; Proppe, D.S. Real-time thermal imagery from an unmanned aerial vehicle can locate ground nests of a grassland songbird at rates similar to traditional methods. Biol. Conserv. 2019, 233, 241–246. [Google Scholar] [CrossRef]
  75. Aide, T.M.; Corrada-Bravo, C.; Campos-Cerqueira, M.; Milan, C.; Vega, G.; Alvarez, R. Real-time bioacoustics monitoring and automated species identification. PeerJ 2013, 1, e103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Pieri, D.; Abrams, M. ASTER observations of thermal anomalies preceding the April 2003 eruption of Chikurachki volcano, Kurile Islands, Russia. Remote Sens. Environ. 2005, 99, 84–94. [Google Scholar] [CrossRef]
  77. Ge, X.; Wang, J.; Ding, J.; Cao, X.; Zhang, Z.; Jie, L.; Li, X. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ 2019, 7, e6926. [Google Scholar] [CrossRef]
  78. Zhang, W.; Yi, S.H.; Qin, Y.; Sun, Y.; Shangguan, D.H.; Meng, B.P.; Li, M.; Zhang, J.G. Effects of Patchiness on Surface Soil Moisture of Alpine Meadow on the Northeastern Qinghai-Tibetan Plateau: Implications for Grassland Restoration. Remote Sens. 2020, 12, 4121. [Google Scholar] [CrossRef]
  79. Lu, F.S.; Sun, Y.; Hou, F.J. Using UAV Visible Images to Estimate the Soil Moisture of Steppe. Water 2020, 12, 2334. [Google Scholar] [CrossRef]
  80. Sankey, T.T.; Leonard, J.M.; Moore, M.M. Unmanned Aerial Vehicle−Based Rangeland Monitoring: Examining a Century of Vegetation Changes. Rangel. Ecol. Manag. 2019, 72, 858–863. [Google Scholar] [CrossRef]
  81. Pluer, E.G.M.; Robinson, D.T.; Meinen, B.U.; Macrae, M.L. Pairing soil sampling with very-high resolution UAV imagery: An examination of drivers of soil and nutrient movement and agricultural productivity in southern Ontario. Geoderma 2020, 379, 114630. [Google Scholar] [CrossRef]
  82. Li, Z.F.; Li, X.B.; Chen, L.H.; Li, R.H.; Deng, F.; Zhang, M.; Wen, L.Q. Carbon flux and soil organic carbon content and density of different community types in a typical steppe ecoregion of Xilin Gol in inner Mongolia, China. J. Arid Environ. 2020, 178, 104155. [Google Scholar] [CrossRef]
  83. Lyu, X.; Li, X.; Gong, J.; Wang, H.; Dang, D.; Dou, H.; Li, S.; Liu, S. Comprehensive grassland degradation monitoring by remote sensing in Xilinhot, Inner Mongolia, China. Sustainability 2020, 12, 3682. [Google Scholar] [CrossRef]
  84. Li, H.; Shen, H.H.; Chen, L.Y.; Liu, T.Y.; Hu, H.F.; Zhao, X.; Zhou, L.H.; Zhang, P.J.; Fang, J.Y. Effects of shrub encroachment on soil organic carbon in global grasslands. Sci. Rep. 2016, 6, 28974. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Caracciolo, D.; Istanbulluoglu, E.; Noto, L.V.; Collins, S.L. Mechanisms of shrub encroachment into Northern Chihuahuan Desert grasslands and impacts of climate change investigated using a cellular automata model. Adv. Water Resour. 2016, 91, 46–62. [Google Scholar] [CrossRef] [Green Version]
  86. Li, W.; Buitenwerf, R.; Munk, M.; Amoke, I.; Bocher, P.K.; Svenning, J.C. Accelerating savanna degradation threatens the Maasai Mara socio-ecological system. Glob. Environ. Chang. 2020, 60, 102030. [Google Scholar] [CrossRef]
  87. Zhao, Y.J.; Liu, X.L.; Wang, Y.; Zheng, Z.Y.; Zheng, S.X.; Zhao, D.; Bai, Y.F. UAV-based individual shrub aboveground biomass estimation calibrated against terrestrial LiDAR in a shrub-encroached grassland. Int. J. Appl. Earth Obs. Geoinf. 2021, 101, 102358. [Google Scholar] [CrossRef]
  88. Prošek, J.; Šímová, P. UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy? Int. J. Appl. Earth Obs. Geoinf. 2019, 75, 151–162. [Google Scholar] [CrossRef]
  89. Pang, G.J.; Wang, T.; Liao, J.; Li, S. Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China. Soil Sci. Soc. Am. J. 2014, 78, 546. [Google Scholar] [CrossRef]
  90. Ivushkin, K.; Bartholomeus, H.; Bregt, A.K.; Pulatov, A.; Franceschini, M.H.D.; Kramer, H.; van Loo, E.N.; Jaramillo Roman, V.; Finkers, R. UAV based soil salinity assessment of cropland. Geoderma 2019, 338, 502–512. [Google Scholar] [CrossRef]
  91. Romero-Trigueros, C.; Nortes, P.A.; Alarcón, J.J.; Hunink, J.E.; Parra, M.; Contreras, S.; Droogers, P.; Nicolás, E. Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing. Agric. Water Manag. 2017, 183, 60–69. [Google Scholar] [CrossRef] [Green Version]
  92. Hu, J.; Peng, J.; Zhou, Y.; Xu, D.Y.; Zhao, R.Y.; Jiang, Q.S.; Fu, T.T.; Wang, F.; Shi, Z. Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images. Remote Sens. 2019, 11, 736. [Google Scholar] [CrossRef] [Green Version]
  93. Zhang, J.G.; Liu, D.W.; Meng, B.P.; Chen, J.J.; Wang, X.Y.; Jiang, H.; Yu, Y.; Yi, S.H. Using UAVs to assess the relationship between alpine meadow bare patches and disturbance by pikas in the source region of Yellow River on the Qinghai-Tibetan Plateau. Glob. Ecol. Conserv. 2021, 26, e01517. [Google Scholar] [CrossRef]
  94. Tang, Z.; Zhang, Y.J.; Cong, N.; Wimberly, M.; Wang, L.; Huang, K.; Li, J.X.; Zu, J.X.; Zhu, Y.X.; Chen, N. Spatial pattern of pika holes and their effects on vegetation coverage on the Tibetan Plateau: An analysis using unmanned aerial vehicle imagery. Ecol. Indic. 2019, 107, 105551. [Google Scholar] [CrossRef]
  95. Yi, S.H. FragMAP: A tool for long-term and cooperative monitoring and analysis of small-scale habitat fragmentation using an unmanned aerial vehicle. Int. J. Remote Sens. 2017, 38, 2686–2697. [Google Scholar] [CrossRef]
  96. Pérez-Cabello, F.; Montorio, R.; Alves, D.B. Remote sensing techniques to assess post-fire vegetation recovery. Curr. Opin. Environ. Sci. Health 2021, 21, 100251. [Google Scholar] [CrossRef]
  97. Li, Q.; Cui, J.; Jiang, W.; Jiao, Q.; Gong, L.; Zhang, J.; Shen, X. Monitoring of the Fire in Muli County on March 28, 2020, based on high temporal-spatial resolution remote sensing techniques. Nat. Hazards Res. 2021, 1, 20–31. [Google Scholar] [CrossRef]
  98. Cruz, H.; Eckert, M.; Meneses, J.; Martinez, J.F. Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs). Sensors 2016, 16, 893. [Google Scholar] [CrossRef] [Green Version]
  99. Liu, X.P.; Zhang, G.Q.; Lu, J.; Zhang, J.Q. Risk assessment using transfer learning for grassland fires. Agric. For. Meteorol. 2019, 269, 102–111. [Google Scholar] [CrossRef]
  100. Yuan, C.; Zhang, Y.M.; Liu, Z.X. A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. 2015, 45, 783–792. [Google Scholar] [CrossRef]
  101. Osco, L.P.; Marcato Junior, J.; Marques Ramos, A.P.; de Castro Jorge, L.A.; Fatholahi, S.N.; de Andrade Silva, J.; Matsubara, E.T.; Pistori, H.; Gonçalves, W.N.; Li, J. A review on deep learning in UAV remote sensing. Int. J. Appl. Earth Obs. Geoinf. 2021, 102, 102456. [Google Scholar] [CrossRef]
  102. Tilman, D. Causes, consequences and ethics of biodiversity. Nature 2000, 405, 208–211. [Google Scholar] [CrossRef]
  103. Chapin, F.S.; Zavaleta, E.S.; Eviner, V.T.; Naylor, R.L.; Vitousek, P.M.; Reynolds, H.L.; Hooper, D.U.; Lavorel, S.; Sala, O.E.; Hobbie, S.E.; et al. Consequences of changing biodiversity. Nature 2000, 405, 234–242. [Google Scholar] [CrossRef] [PubMed]
  104. Hou, L.; Xia, F.; Chen, Q.; Huang, J.; He, Y.; Rose, N.; Rozelle, S. Grassland ecological compensation policy in China improves grassland quality and increases herders’ income. Nat. Commun. 2021, 12, 4683. [Google Scholar] [CrossRef] [PubMed]
  105. Anderson, K.; Gaston, K.J. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 2013, 11, 138–146. [Google Scholar] [CrossRef] [Green Version]
  106. Obermeier, W.A.; Lehnert, L.W.; Pohl, M.J.; Makowski Gianonni, S.; Silva, B.; Seibert, R.; Laser, H.; Moser, G.; Müller, C.; Luterbacher, J.; et al. Grassland ecosystem services in a changing environment: The potential of hyperspectral monitoring. Remote Sens. Environ. 2019, 232, 111273. [Google Scholar] [CrossRef]
  107. Sankey, J.B.; Sankey, T.T.; Li, J.; Ravi, S.; Wang, G.; Caster, J.; Kasprak, A. Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland. Remote Sens. Environ. 2021, 253, 112223. [Google Scholar] [CrossRef]
  108. Yang, F.; Shao, Q.Q.; Jiang, Z.G. A Population Census of Large Herbivores Based on UAV and Its Effects on Grazing Pressure in the Yellow-River-Source National Park, China. Int. J. Environ. Res. Public Health. 2019, 16, 4402. [Google Scholar] [CrossRef] [Green Version]
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Figure 1. The flowchart of the paper structure.
Figure 1. The flowchart of the paper structure.
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Figure 2. Development trend of unmanned aerial vehicle (UAV) remote sensing and research hotspots in grassland ecosystems. (a) WOS philological analysis, (b) keyword cloud image and (c) keyword co-occurrence analysis. In (c), the same color indicates that keywords are closer and may appear together in several papers. The larger the node is, the more frequently the keyword appears.
Figure 2. Development trend of unmanned aerial vehicle (UAV) remote sensing and research hotspots in grassland ecosystems. (a) WOS philological analysis, (b) keyword cloud image and (c) keyword co-occurrence analysis. In (c), the same color indicates that keywords are closer and may appear together in several papers. The larger the node is, the more frequently the keyword appears.
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Figure 3. Future directions of unmanned aerial vehicle remote sensing in grassland ecosystem monitoring. HWB, human well-being.
Figure 3. Future directions of unmanned aerial vehicle remote sensing in grassland ecosystem monitoring. HWB, human well-being.
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Lyu, X.; Li, X.; Dang, D.; Dou, H.; Wang, K.; Lou, A. Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review. Remote Sens. 2022, 14, 1096. https://doi.org/10.3390/rs14051096

AMA Style

Lyu X, Li X, Dang D, Dou H, Wang K, Lou A. Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review. Remote Sensing. 2022; 14(5):1096. https://doi.org/10.3390/rs14051096

Chicago/Turabian Style

Lyu, Xin, Xiaobing Li, Dongliang Dang, Huashun Dou, Kai Wang, and Anru Lou. 2022. "Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review" Remote Sensing 14, no. 5: 1096. https://doi.org/10.3390/rs14051096

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