remotesensing-logo

Journal Browser

Journal Browser

Remote Sensing and Proximal Sensing in Support of Agricultural Cultivation and Crop Risk Management

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 84241

Special Issue Editors

Institute of Sugar Beet Research (IfZ), Holtenser Landstrasse 77, 37079 Göttingen, Germany
Interests: plant diseases and protection; phenotyping; precision agriculture; optical sensors; biotic and abiotic plant stress; machine learning; robotics; digital technologies

Special Issue Information

Dear Colleagues,

Modern management of agricultural resources is a complex challenge needing the association of technical advances in remote and proximal sensing, information sciences, geographic positioning systems and robotics.

Remote and proximal sensing are the two most common techniques concerning the acquisition of information about an object or any phenomenon without making any physical contact with the object. Remote sensing is widely tied to the use of satellite, airborne or UAV platforms using multi- or hyperspectral imagery. In terms of proximal sensing, the sensor setup is in close distance to the object and the platforms range from handheld, over stationary installations to robotics and tractor-based sensors. The types of sensors range from simple RGB or grey-level-imaging over multispectral to hyperspectral high resoluted imaging or IR-thermography.

Numerous applications use these techniques, including photography, surveying, geology, forestry. Especially the field of agriculture and plant monitoring has found significant uses. Modern applications of remote and proximal sensing are based on the use of high resolution spectral signatures of leaves and soils, crop canopies for natural and cultivated species or for spectral features identification. The sensor and monitoring approach is always tied to an efficient data analysis approach, ranging from the identification of relevant wavelengths and features, calculation of spectral vegetation indices or advanced machine learning and data mining approaches. Associated with plant growth conditions (in the field, in laboratories or in greenhouses) and phenotyping techniques, remote and proximal sensing is able to provide information on nutrient deficiency, biotic stress such as pests and diseases as well as for abiotic stresses.

We invite thus papers on the following non-exhaustive list of topics around Support of Agricultural Cultivation and Crop Risk Management:

  • Assessment of crop damage and crop progress: remote and proximal sensing technology could be used to determine how much of a crop has been damaged and by which way (weather, animals …).
  • Identification of pests and disease infestation: Remote and proximal sensing technology also plays a significant role in the identification of pests and diseases and gives now big data on the pest control mechanisms to be used to prevent and/or recover diseases and to manage their propagation.
  • Water Management: Plant Water Status, Evapotranspiration and Crop development / Salinity Stress …
  • Nutrient Management: Efficient management of nutrients is one of the main challenges facing production agriculture. Here, remote and proximal sensing can provide field-scale diagnostic methods that will enable detection of nutrient deficiencies.

Fundamental research relating spectral properties of soils and crops to agronomic, biophysical parameters, genetic and phenotypic parameters has been done in a variety of programs opening the way for more confident research. The future brings tedious prospects for combining spatial, spectral and temporal information based on sensed multi- and hyperspectral imagery with the capabilities of management-oriented crop simulation models.

Dr. Frédéric Cointault
Dr. Anne-Katrin Mahlein
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 2943 KiB  
Article
Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios
by Marc Sancho-Adamson, Maria Isabel Trillas, Jordi Bort, Jose Armando Fernandez-Gallego and Joan Romanyà
Remote Sens. 2019, 11(6), 607; https://doi.org/10.3390/rs11060607 - 13 Mar 2019
Cited by 20 | Viewed by 3300
Abstract
Verticillium Wilt of Olive, a disease caused by the hemibiotrophic vascular fungus Verticillium dahliae Kleb. presents one of the most important constraints to olive production in the world, with an especially notable impact in Mediterranean agriculture. This study evaluates the use of RGB [...] Read more.
Verticillium Wilt of Olive, a disease caused by the hemibiotrophic vascular fungus Verticillium dahliae Kleb. presents one of the most important constraints to olive production in the world, with an especially notable impact in Mediterranean agriculture. This study evaluates the use of RGB vegetation indexes in assessing the effects of this disease during the biotrophic phase of host-pathogen interaction, in which symptoms of wilt are not yet evident. While no differences were detected by measuring stomatal conductance and chlorophyll fluorescence, results obtained from RGB indexes showed significant differences between control and inoculated plants for indexes Saturation, a*, b*, green Area (GA), normalized green-red difference index (NGRDI) and triangular greenness index (TGI), presenting a reduction in plant growth as well as in green and yellow color components as an effect of inoculation. These results were contrasted across two scenarios of mineral fertilization in soil and soil amended with two different olive mill waste composts, presenting a clear interaction between the host-pathogen relationship and plant nutrition and suggesting the effect of V. dahliae infection during the biotrophic phase was not related to plant water status. Full article
Show Figures

Graphical abstract

17 pages, 5701 KiB  
Article
Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment
by Aleem Khaliq, Lorenzo Comba, Alessandro Biglia, Davide Ricauda Aimonino, Marcello Chiaberge and Paolo Gay
Remote Sens. 2019, 11(4), 436; https://doi.org/10.3390/rs11040436 - 20 Feb 2019
Cited by 142 | Viewed by 11112
Abstract
In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed [...] Read more.
In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant. Full article
Show Figures

Graphical abstract

17 pages, 4324 KiB  
Article
Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States
by Xiaojie Li, Jianhua Ren, Kai Zhao and Zhengwei Liang
Remote Sens. 2019, 11(4), 388; https://doi.org/10.3390/rs11040388 - 14 Feb 2019
Cited by 8 | Viewed by 3041
Abstract
The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline [...] Read more.
The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na+, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2–11% decrease in the parameters mean absolute error (MAE). Full article
Show Figures

Graphical abstract

19 pages, 3382 KiB  
Article
Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method
by Linyi Liu, Yingying Dong, Wenjiang Huang, Xiaoping Du, Juhua Luo, Yue Shi and Huiqin Ma
Remote Sens. 2019, 11(3), 298; https://doi.org/10.3390/rs11030298 - 01 Feb 2019
Cited by 20 | Viewed by 3276
Abstract
In order to monitor the prevalence of wheat powdery mildew, current methods require sufficient sample data to obtain results with higher accuracy and stable validation. However, it is difficult to collect data on wheat powdery mildew in some regions, and this limitation in [...] Read more.
In order to monitor the prevalence of wheat powdery mildew, current methods require sufficient sample data to obtain results with higher accuracy and stable validation. However, it is difficult to collect data on wheat powdery mildew in some regions, and this limitation in sampling restricts the accuracy of monitoring regional prevalence of the disease. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region. By taking into account the representativeness of contributions of auxiliary samples to adjust the weight placed on auxiliary samples, an optimized TrAdaBoost algorithm, named OpTrAdaBoost, was generated to map regional wheat powdery mildew. The algorithm conducts this by: (1) producing uncertainty associated with each prediction based on the similarities, and calculating the representativeness contribution of all auxiliary samples by taking into account the overall uncertainty of the wheat powdery mildew map; (2) calculating the errors of the weak learners during the training process and using boosting to filter out the unreliable auxiliary samples by adjusting the weights of auxiliary samples; (3) combining all weak learners according to the weights of training instances to build a strong learner to classify disease severity. OpTrAdaBoost was tested using a dataset with 39 study area samples and 106 auxiliary samples. The overall monitoring accuracy was 82%, and the kappa coefficient was 0.72. Moreover, OpTrAdaBoost performed better than other algorithms that are commonly used to monitor wheat powdery mildew at the regional level. Experimental results demonstrated that OpTrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples. Full article
Show Figures

Graphical abstract

47 pages, 17072 KiB  
Article
Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato
by Marston Héracles Domingues Franceschini, Harm Bartholomeus, Dirk Frederik van Apeldoorn, Juha Suomalainen and Lammert Kooistra
Remote Sens. 2019, 11(3), 224; https://doi.org/10.3390/rs11030224 - 22 Jan 2019
Cited by 55 | Viewed by 11783
Abstract
Assessment of disease incidence and severity at farm scale or in agronomic trials is frequently performed based on visual crop inspection, which is a labor intensive task prone to errors associated with its subjectivity. Therefore, alternative methods to relate disease incidence and severity [...] Read more.
Assessment of disease incidence and severity at farm scale or in agronomic trials is frequently performed based on visual crop inspection, which is a labor intensive task prone to errors associated with its subjectivity. Therefore, alternative methods to relate disease incidence and severity with changes in crop traits are of great interest. Optical imagery in the visible and near-infrared (Vis-NIR) can potentially be used to detect changes in crop traits caused by pathogen development. Also, cameras on-board of Unmanned Aerial Vehicles (UAVs) have flexible data collection capabilities allowing adjustments considering the trade-off between data throughput and its resolution. However, studies focusing on the use of UAV imagery to describe changes in crop traits related to disease infection are still lacking. More specifically, evaluation of late blight (Phytophthora infestans) incidence in potato concerning early discrimination of different disease severity levels has not been extensively reported. In this article, the description of spectral changes related to the development of potato late blight under low disease severity levels is performed using sub-decimeter UAV optical imagery. The main objective was to evaluate the sensitivity of the data acquired regarding early changes in crop traits related to disease incidence. For that, UAV images were acquired on four dates during the growing season (from 37 to 78 days after planting), before and after late blight was detected in the field. The spectral variability observed in each date was summarized using Simplex Volume Maximization (SiVM), and its relationship with experimental treatments (different crop systems) and disease severity levels (evaluated by visual assessment) was determined based on pixel-wise log-likelihood ratio (LLR) calculation. Using this analytical framework it was possible to identify considerable spectral changes related to late blight incidence in different treatments and also to disease severity level as low as between 2.5 and 5.0% of affected leaf area. Comparison of disease incidence and spectral information acquired using UAV (with 4–5 cm of spatial resolution) and ground-based imagery (with 0.1–0.2 cm of spatial resolution) indicate that UAV data allowed identification of patterns comparable to those described by ground-based images, despite some differences concerning the distribution of affected areas detected within the sampling units and an attenuation in the signal measured. Finally, although aggregated information at sampling unit level provided discriminative potential for higher levels of disease development, focusing on spectral information related to disease occurrence increased the discriminative potential of the data acquired. Full article
Show Figures

Figure 1

26 pages, 13711 KiB  
Article
On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dorée and Grapevine Trunk Diseases
by Johanna Albetis, Anne Jacquin, Michel Goulard, Hervé Poilvé, Jacques Rousseau, Harold Clenet, Gerard Dedieu and Sylvie Duthoit
Remote Sens. 2019, 11(1), 23; https://doi.org/10.3390/rs11010023 - 23 Dec 2018
Cited by 69 | Viewed by 7346
Abstract
Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the [...] Read more.
Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge® sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines). Full article
Show Figures

Graphical abstract

26 pages, 12878 KiB  
Article
Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca Disease in Bordeaux Vineyards
by Florian Rançon, Lionel Bombrun, Barna Keresztes and Christian Germain
Remote Sens. 2019, 11(1), 1; https://doi.org/10.3390/rs11010001 - 20 Dec 2018
Cited by 38 | Viewed by 8273
Abstract
Grapevine wood fungal diseases such as esca are among the biggest threats in vineyards nowadays. The lack of very efficient preventive (best results using commercial products report 20% efficiency) and curative means induces huge economic losses. The study presented in this paper is [...] Read more.
Grapevine wood fungal diseases such as esca are among the biggest threats in vineyards nowadays. The lack of very efficient preventive (best results using commercial products report 20% efficiency) and curative means induces huge economic losses. The study presented in this paper is centered around the in-field detection of foliar esca symptoms during summer, exhibiting a typical “striped” pattern. Indeed, in-field disease detection has shown great potential for commercial applications and has been successfully used for other agricultural needs such as yield estimation. Differentiation with foliar symptoms caused by other diseases or abiotic stresses was also considered. Two vineyards from the Bordeaux region (France, Aquitaine) were chosen as the basis for the experiment. Pictures of diseased and healthy vine plants were acquired during summer 2017 and labeled at the leaf scale, resulting in a patch database of around 6000 images (224 × 224 pixels) divided into red cultivar and white cultivar samples. Then, we tackled the classification part of the problem comparing state-of-the-art SIFT encoding and pre-trained deep learning feature extractors for the classification of database patches. In the best case, 91% overall accuracy was obtained using deep features extracted from MobileNet network trained on ImageNet database, demonstrating the efficiency of simple transfer learning approaches without the need to design an ad-hoc specific feature extractor. The third part aimed at disease detection (using bounding boxes) within full plant images. For this purpose, we integrated the deep learning base network within a “one-step” detection network (RetinaNet), allowing us to perform detection queries in real time (approximately six frames per second on GPU). Recall/Precision (RP) and Average Precision (AP) metrics then allowed us to evaluate the performance of the network on a 91-image (plants) validation database. Overall, 90% precision for a 40% recall was obtained while best esca AP was about 70%. Good correlation between annotated and detected symptomatic surface per plant was also obtained, meaning slightly symptomatic plants can be efficiently separated from severely attacked plants. Full article
Show Figures

Graphical abstract

31 pages, 11616 KiB  
Article
Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa
by Lillian Kay Petersen
Remote Sens. 2018, 10(11), 1726; https://doi.org/10.3390/rs10111726 - 01 Nov 2018
Cited by 41 | Viewed by 10749
Abstract
Developing countries often have poor monitoring and reporting of weather and crop health, leading to slow responses to droughts and food shortages. Here, I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. This [...] Read more.
Developing countries often have poor monitoring and reporting of weather and crop health, leading to slow responses to droughts and food shortages. Here, I develop satellite analysis methods and software tools to predict crop yields two to four months before the harvest. This method measures relative vegetation health based on pixel-level monthly anomalies of NDVI, EVI and NDWI indices. Because no crop mask, tuning, or subnational ground truth data are required, this method can be applied to any location, crop, or climate, making it ideal for African countries with small fields and poor ground observations. Testing began in Illinois where there is reliable county-level crop data. Correlations were computed between corn, soybean, and sorghum yields and monthly vegetation health anomalies for every county and year. A multivariate regression using every index and month (up to 1600 values) produced a correlation of 0.86 with corn, 0.74 for soybeans, and 0.65 for sorghum, all with p-values less than 10 6 . The high correlations in Illinois show that this model has good forecasting skill for crop yields. Next, the method was applied to every country in Africa for each country’s main crops. Crop production was then predicted for the 2018 harvest and compared to actual production values. Twenty percent of the predictions had less than 2% error, and 40% had less than 5% error. This method is unique because of its simplicity and versatility: it shows that a single user on a laptop computer can produce reasonable real-time estimates of crop yields across an entire continent. Full article
Show Figures

Graphical abstract

22 pages, 23769 KiB  
Article
Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images
by M Dian Bah, Adel Hafiane and Raphael Canals
Remote Sens. 2018, 10(11), 1690; https://doi.org/10.3390/rs10111690 - 26 Oct 2018
Cited by 215 | Viewed by 19156
Abstract
In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One [...] Read more.
In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field. Full article
Show Figures

Figure 1

13 pages, 1653 KiB  
Article
Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System
by Shereen S. Xavier, Alisa W. Coffin, Dawn M. Olson and Jason M. Schmidt
Remote Sens. 2018, 10(9), 1485; https://doi.org/10.3390/rs10091485 - 18 Sep 2018
Cited by 8 | Viewed by 5260
Abstract
Studies show that agricultural land requires investment in the habitat management of non-cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing [...] Read more.
Studies show that agricultural land requires investment in the habitat management of non-cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing greater numbers of flowers supported significantly higher pollinator populations over that of spontaneous weed plots. Here, we examined the potential of deploying an inexpensive small unmanned aerial vehicle (UAV) as a tool to remotely estimate floral resources and corresponding pollinator populations. Data were collected from previously established native wildflower plots in 19 locations on the University of Georgia experimental farms in South Georgia, USA. A UAV equipped with a lightweight digital camera was deployed to capture images of the flowers during the months of June and September 2017. Supervised image classification using a geographic information system (GIS) was carried out on the acquired images, and classified images were used to evaluate the floral area. The floral area obtained from the images positively correlated with the floral counts gathered from the quadrat samples. Furthermore, the floral area derived from imagery significantly predicted pollinator populations, with a positive correlation indicating that plots with greater area of blooming flowers contained higher numbers of pollinators. Full article
Show Figures

Graphical abstract

Back to TopTop