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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 15683

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Special Issue Editors

Dr. Hooman Latifi

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Guest Editor

1. Department of Remote Sensing, University of Würzburg, Würzburg, Germany
2. Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
Interests: ecosystem monitoring; vegetation health; time series remote sensing; LiDAR
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Nikos Koutsias

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Guest Editor

Department of Sustainable Agriculture, University of Patras, 2 Seferi, Agrinio, GR-30100, Greece
Interests: remote sensing; GIS; spatial analysis; wildland fires; natural disasters; landscape ecology; phenology
Special Issues, Collections and Topics in MDPI journals

Dr. Hamed Naghavi

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Guest Editor

Department of Forestry, Lorestan University, P.O. Box, Khorramabad 68151-44316, Iran
Interests: vegetation mapping using satellite imagery; monitoring semi-arid woody vegetation; forest change modelling; estimating the attributes of trees using 3D data

Special Issue Information

Dear Colleagues,

This is the 2nd volume of the Special Issue “Remote Sensing of Arid/Semiarid Lands”, it was a great success.

Recent publication trends reveal the continuous progress in the application of remote sensing and Earth observation approaches for semi-arid vegetation monitoring. Many previous surveys were conducted based on the fundamental assumption that drought and other extreme climate events affect the ecological environment for vegetation in semi-arid zones, yet with a response lag of vegetation to drought that differs from ecosystem to ecosystem. Phenomena like changing patterns in soil use, natural disasters, climatic change and wildfires alter the spatial dynamics of arid and semi-arid vegetation.

Amongst the remote sensing methods and despite their inherent intuition, vegetation index-based approaches are still advantageous given their simple calculation and intuitive interpretability. In particular, permanent sample plots established within semi-arid regions could offer excellent locations for long-term and mostly remote-sensing-based spectral trajectory analysis of

Vegetation. Furthermore, approaches based on vegetation phenology could also provide invaluable insights for the trajectory analysis of semi-arid vegetation, which have so far been largely understudied, and remote-sensing-assisted phenological investigations seem to be lacking for semi-arid regions.

In addition, spatiotemporal image-fusion approaches have experienced rapid progress for various remote-sensing applications in recent decades as a constantly growing field of

research, which calls for their augmented applications across arid and semi-arid vegetation zones. Finally, approaches based on new technologies like UAVs, terrestrial and mobile laser scanners (for small-scale quantification and monitoring) as well as large-scale applications via new satellite data series (in active and passive domains) are required to be calibrated for arid and semi-arid vegetation, in particular across remote and mountainous areas.

In this Special Issue, we aim to cover those and other relevant topics and sub-topics by welcoming reviews, case studies and communications from all over the world. In particular, we welcome submissions with the following thematic emphases:

Remote sensing applications for soil erosion monitoring across arid and semi-arid areas;
Applications for spatial extrapolations and upscaling from plant to plant groups and landscapes;
Approaches for pest, disease and decline monitoring across arid and semi-arid natural vegetation;
Applications based on terrestrial platforms (TLS, iPhone, GeoSLAM etc.) for small-scale vegetation monitoring across arid and semi-arid regions;
Applications for estimating growing stock, woody biomass and bioenergy across arid and semi-arid trees;
Species distribution mapping and monitoring;
Approaches to overcome challenges faced when processing remote sensing data sources like soil background reflectance, atmospheric effects, shadows and grouped tree crowns in coppice structures;
Spatial and temporal forests change modelling across arid and semi-arid areas;
Defining and modelling ecosystem services of arid and semi-arid woodlands;
Assessing the impact of climate change on arid and semi-arid vegetation.

Dr. Hooman Latifi
Dr. Nikos Koutsias
Dr. Hamed Naghavi
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

arid woody vegetation
semi-arid woody vegetation
remote sensing
UAV photogrammetry
coppice structure
burned area mapping
tree detection and delineation
spatial ecology

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Published Papers (8 papers)

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Research

21 pages, 11573 KiB

Open AccessArticle

Analyzing Spatiotemporal Variations and Driving Factors of Grassland in the Arid Region of Northwest China Surrounding the Tianshan Mountains

by Yutong Fang, Xiang Zhao, Naijing Liu, Wenjie Zhang and Wenxi Shi

Remote Sens. 2024, 16(11), 1952; https://doi.org/10.3390/rs16111952 - 29 May 2024

Viewed by 1029

Abstract

The Tianshan Mountains, the largest arid mountain range in Central Asia, feature diverse terrains and significant landscape heterogeneity. The grasslands within the Xinjiang Tianshan region are particularly sensitive to climate change and human activities. However, until recently, the patterns and mechanisms underlying grassland changes in this region have been unclear. In this study, we analyzed spatial and temporal changes in grassland fractional vegetation cover (FVC) from 2001 to 2020, analyzed spatial and temporal changes in grassland, and predicted future trends using Global Land Surface Satellite (GLASS) FVC data, trend analysis, and the Hurst index method. We also explored the driving mechanisms behind these changes through the structural equation model (SEM). The results showed that from 2001 to 2020, the grassland FVC in the Tianshan region of Xinjiang was higher in the central and western regions and lower in the northern and southern regions, showing an overall fluctuating growth trend, with a change in the growth rate of 0. 0017/a (p < 0.05), and that this change was spatially heterogeneous, with the sum of significant improvement (20.6%) and slight improvement (29.9%) being much larger than the sum of significant degradation (0.6%) and slight degradation (9.5%). However, the Hurst index (H = 0.47) suggests that this trend may not continue, and there is a risk of degradation. Our study uncovers the complex interactions between the Tianshan barrier effect and grassland ecosystems, highlighting regional differences in driving mechanisms. Although the impacts of climatic conditions in grasslands vary over time in different regions, the topography and its resulting hydrothermal conditions are still dominant, and the extent of the impact is susceptible to fluctuations of varying degrees due to extreme climatic events. Additionally, the number of livestock changes significantly affects the grasslands on the southern slopes of the Tianshan Mountains, while the effects of nighttime light are minimal. By focusing on the topographical barrier effect, this study enhances our understanding of grassland vegetation dynamics in the Tianshan Mountains of Xinjiang, contributing to improved ecosystem management strategies under climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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24 pages, 11051 KiB

Open AccessArticle

Spatio-Temporal Transferability of Drone-Based Models to Predict Forage Supply in Drier Rangelands

by Vistorina Amputu, Florian Männer, Katja Tielbörger and Nichola Knox

Remote Sens. 2024, 16(11), 1842; https://doi.org/10.3390/rs16111842 - 22 May 2024

Viewed by 1138

Abstract

Unmanned aerial systems offer a cost-effective and reproducible method for monitoring natural resources in expansive areas. But the transferability of developed models, which are often based on single snapshots, is rarely tested. This is particularly relevant in rangelands where forage resources are inherently patchy in space and time, which may limit model transfer. Here, we investigated the accuracy of drone-based models in estimating key proxies of forage provision across two land tenure systems and between two periods of the growing season in semi-arid rangelands. We tested case-specific models and a landscape model, with the expectation that the landscape model performs better than the case-specific models as it captures the highest variability expected in the rangeland system. The landscape model did achieve the lowest error when predicting herbaceous biomass and predicted land cover with better or similar accuracy to the case-specific models. This reinforces the importance of incorporating the widest variation of conditions in predictive models. This study contributes to understanding model transferability in drier rangeland systems characterized by spatial and temporal heterogeneity. By advancing the integration of drone technology for accurate monitoring of such dynamic ecosystems, this research contributes to sustainable rangeland management practices. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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20 pages, 15304 KiB

Open AccessArticle

Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

by Haiying Yu, Qianhua Yang, Shouzheng Jiang, Bao Zhan and Cun Zhan

Remote Sens. 2024, 16(7), 1280; https://doi.org/10.3390/rs16071280 - 5 Apr 2024

Cited by 4 | Viewed by 1937

Abstract

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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25 pages, 18811 KiB

Open AccessArticle

A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China

by Jingbo Li, Chunxiang Cao, Min Xu, Xinwei Yang, Xiaotong Gao, Kaimin Wang, Heyi Guo and Yujie Yang

Remote Sens. 2023, 15(24), 5716; https://doi.org/10.3390/rs15245716 - 13 Dec 2023

Cited by 1 | Viewed by 1561

Abstract

Grassland desertification stands as an ecological concern globally. It is crucial for desertification prevention and control to comprehend the variation in area and severity of desertified grassland (DGL), clarify the intensities of conversion among DGLs of different desertification levels, and explore the spatial and temporal driving factors of desertification. In this study, a Desertification Difference Index (DDI) model was constructed based on albedo-EVI to extract desertification information. Subsequently, intensity analysis, the Geo-detector model, and correlation analysis were applied to analyze the dynamics and driving factors of desertification. The results showed the following: (1) Spatially, the DGL in Xilingol exhibited a zonal distribution. Temporally, the degree of DGL decreased, with the proportion of severely and moderately desertified areas decreasing from 51.77% in 2000 to 37.23% in 2020, while the proportion of nondesertified and healthy areas increased from 17.85% in 2000 to 37.40% in 2020; (2) Transition intensities among different desertification levels were more intense during 2000–2012, stabilizing during 2012–2020; (3) Meteorological factors and soil conditions primarily drive the spatial distribution of DDI, with evapotranspiration exhibiting the most significant influence (q-value of 0.83), while human activities dominate interannual DDI variations. This study provides insights into the conversion patterns among different desertification levels and the divergent driving forces shaping desertification in both spatial and temporal dimensions in Xilingol. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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21 pages, 2180 KiB

Open AccessArticle

Combination of UAV Photogrammetry and Field Inventories Enables Description of Height–Diameter Relationship within Semi-Arid Silvopastoral Systems

by Arvin Fakhri, Hooman Latifi, Kyumars Mohammadi Samani, Zahed Shakeri, Hamed Naghavi and Fabian Ewald Fassnacht

Remote Sens. 2023, 15(21), 5261; https://doi.org/10.3390/rs15215261 - 6 Nov 2023

Cited by 2 | Viewed by 1510

Abstract

Pollarding oak trees is a traditional silvopastoral technique practiced across wide areas of the northern Zagros mountains, a unique and vast semi-arid forest area with a strong cultural and ecological significance. So far, the effects of pollarding on tree structure in terms of DBH (diameter at breast height)~H (height) relationships within the typical pollarding cycle, which often lasts 4 years, has not been scientifically described. Here, we combine field inventories of DBH with H obtained from photogrammetric UAV flights for the first time to assess DBH~H relationships within this system. We conducted the research at six pollarded forest sites throughout the Northern Zagros. The sampling encompassed all three main species of coppice oak trees. In the case of multi-stem trees, we used the maximum DBH of each tree that formed a unique crown. A linear relationship between UAV and extracted H and the maximum DBH of pollarded trees explained a notable part of the variation in maximum DBH (R² = 0.56), and more complex and well-known nonlinear allometries were also evaluated, for which the accuracies were in the same range as the linear model. This relationship proved to be stable across oak species, and the pollarding stage had a notable effect on the DBH~H relationship. This finding is relevant for future attempts to inventory biomass using remote sensing approaches across larger areas in northern Zagros, as well as for general DBH estimations within stands dominated by pollarded, multi-stem coppice structures. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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23 pages, 15171 KiB

Open AccessArticle

Spatial and Temporal Variation in Water Use Efficiency and Ecosystem Photosynthetic Efficiency in Central Asia

by Heran Yahefujiang, Jie Zou, Jianli Ding, Wensong Zou, Wulala Tangjialeke and Miao Yang

Remote Sens. 2023, 15(21), 5240; https://doi.org/10.3390/rs15215240 - 4 Nov 2023

Viewed by 1587

Abstract

Ecosystem water use efficiency (WUE) and ecosystem photosynthetic efficiency (EPE) are key indicators in studies of the carbon–water cycle in terrestrial ecosystems. Analyses of WUE and EPE can enhance our understanding of the relationship between ecosystem light use efficiency and WUE. Although several studies of individual indexes (i.e., either WUE or EPE) have been conducted, analyses of variation in both WUE and EPE, as well as their relationships, have rarely been conducted. Here, we analyzed spatial and temporal variation in WUE and EPE in Central Asia. Specifically, time trend analysis was conducted to characterize temporal and spatial changes in WUE and EPE in Central Asia from 2001 to 2020 at different altitudes and latitudes. Pearson correlation analysis was used to characterize the effects of precipitation and temperature on WUE and EPE. WUE decreased and EPE increased in Central Asia over the 20-year study period; this might have been due to interannual variations in precipitation and temperature. WUE was highest in August, and EPE was highest in June and July. Substantial spatial heterogeneity in WUE and EPE was observed; WUE was highly variable in Central Asia as well as in western and southern Central Asia. Major changes in EPE were observed in northern, eastern, and southern Central Asia. We also found that both WUE and EPE decreased with the increase in altitude. WUE was positively correlated with temperature and negatively correlated with precipitation, whereas EPE was positively correlated with both temperature and precipitation. The increase in photosynthetic efficiency might be one of the main factors contributing to increases in ecosystem productivity in arid environments. The temporal and spatial variation in WUE and EPE observed in our study will aid ecosystem research, providing a reliable theoretical basis for ecosystem research in areas with scarce large-scale data, integrated water resources management, and ecosystem restoration efforts. Our findings also enhance our understanding of the terrestrial carbon–water cycle and have implications for predicting ecosystem responses to climate change. The results of this study provide insights that will aid studies of the terrestrial carbon–water cycle under the background of climate change. It is of great significance to further study the carbon water cycle in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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20 pages, 6828 KiB

Open AccessArticle

Analysis of Spatial and Temporal Variability of Ecosystem Service Values and Their Spatial Correlation in Xinjiang, China

by Shuai Zhang, Yang Wang, Wenzhe Xu, Ziyi Sheng, Zhen Zhu and Yifeng Hou

Remote Sens. 2023, 15(19), 4861; https://doi.org/10.3390/rs15194861 - 7 Oct 2023

Cited by 6 | Viewed by 1421

Abstract

Xinjiang is located in arid northwest China, which is a key area for promoting the high-quality development of the regional ecological environment. In recent years, against a background of increasing human activities and rapid natural changes, Xinjiang has faced enormous ecological challenges. This paper utilizes land-use data from 2000 to 2020 to verify the region’s current state of the ecosystem. Additionally, it uses the value equivalent factor per unit area, ecosystem service value (ESV) loss and gain matrix, and double-factor spatial autocorrelation analysis to study the spatial and temporal variabilities of ESV in Xinjiang and its attribution to spatial correlation. The results show that (1) the ESV in Xinjiang exhibits an overall increasing trend during 2000–2020, with a total increase of about CNY 18.202 billion. Regulation-service ESV takes the main position in the single-service function, accounting for about 67.18% of the total ESV. In northern Xinjiang, the ESV demonstrates a decreasing trend, dropping by about CNY 16.885 billion, while in southern Xinjiang, the ESV shows an increasing trend, rising by CNY 35.086 billion. (2) For the study period, the main loss of ESV in Xinjiang is the conversion of ecological land with a high ESV into cropland or barren land with a low ESV. The conversion of bare land to grassland led to the largest increase in ESV (about CNY 209.308 billion), whereas the conversion of grassland to barren land led to the largest loss (about CNY 183.046 billion). (3) There are positive correlations among ESV, net primary productivity (NPP), and human activity intensity (HAI). However, all of the relationships weaken year by year. The spatial agglomeration of ESV ∩ NPP is significantly greater than that of ESV ∩ HAI, so NPP is the dominant factor in the spatial correlation of ESV in Xinjiang. The findings of this study provide a scientific basis for promoting high-quality regional ecological development in China’s arid northwest. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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20 pages, 4694 KiB

Open AccessEditor’s ChoiceArticle

Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China

by Yuanyuan Luo, Wei Liang, Jianwu Yan, Weibin Zhang, Fen Gou, Chengxi Wang and Xiaoru Liang

Remote Sens. 2023, 15(10), 2593; https://doi.org/10.3390/rs15102593 - 16 May 2023

Cited by 5 | Viewed by 4084

Abstract

Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of indicators. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Solar-Induced Chlorophyll Fluorescence (SIF) were utilized as vegetation growth indicators, while the standardized precipitation evapotranspiration index (SPEI) and soil moisture indicators served as water use indices. To investigate the vegetation response to water deficit in the Loess Plateau during the growing season from 2000 to 2020, Spearman’s rank correlation coefficients were calculated using a 5-year sliding window approach. The spatial and temporal heterogeneity of vegetation response to water deficit during the growing seasons were also explored. The results showed that: (1) with the improvement of moisture conditions, vegetation growth recovered significantly, and there was no expansion trend for vegetation water deficit. (2) The most sensitive timescale of vegetation to water deficit was 6–8 months; the response degree and sensitivity of vegetation to water surplus and deficit were the highest from June to August; and broadleaved forest was the vegetation type most sensitive to water deficit in the early growing season, while grass was the vegetation type most sensitive to water deficit during the mid and late growing seasons. (3) Soil moisture emerged as the dominant factor influencing vegetation growth in the Loess Plateau, followed by precipitation, albeit to a lesser extent. These findings contribute to understanding the mechanism and characteristics of the response of vegetation to climate fluctuations induced by global climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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