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International audience; Agricultural drought is a complex phenomenon with numerous consequences and negative implications for agriculture and food systems. The Sahel is frequently affected by severe droughts, leading to significant losses in agricultural yields. Consequently, assessing vulnerability to agricultural drought is essential for strengthening early warning systems. The aim of this study is to develop a new multivariate agricultural drought vulnerability index (MADVI) that combines static and dynamic factors extracted from satellite data. First, pixel temporal regression from 1981 to 2021 was applied to climatic and biophysical covariates to determine the gradients of trend magnitudes. Second, principal component analysis was applied to groups of factors that indicate the same type of vulnerability to configure the basic equation of vulnerability to agricultural drought. Then, random forest (RF), K-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) were used to predict drought vulnerability classes using the 28 factors as inputs and 708 pts of randomly distributed class labels. The results showed statistical agreement between the predicted MADVI spatial variability and the reference model (R=0.86 for RF) and its statistical relationships with the vulnerability subcomponents, with an R=0.73 with exposure to climate risk, R=0.64 with the socioeconomic sensitivity index, R=0.6 with the biophysical sensitivity index and a relatively weak correlation (R=0.21) with the physiographic sensitivity index. The overall vulnerability situation in the watershed is 21.8% extreme, 10% very high, 16.8% high, 27.7% moderate, 22.2% low and 1.5% relatively low considering the cartographic results of the predicted vulnerability classes with SVM having the best performance (accuracy=0.96, Kappa=0.95). The study is the first approach that uses the gradients of magnitudes of satellite covariate anomaly trends in multivariate modelling of vulnerability to agricultural drought. It can be easily scaled up across the Sahel region to improve early warning measures related to the impacts of agricultural drought.

International audience; Land cover (LC) classification remains a challenging task due to the diversity of terrain and topography, limited prior knowledge, and complex data sets. These variations can lead to differences in illumination and shading, which can affect the appearance of objects in the image. The local binary pattern (LBP) model is an effective technique for capturing local texture information in an image, which can help to overcome the effects of topography diversity by analysing patterns in different image regions, even if the illumination or shading conditions are different. However, LBP alone is inadequate for characterizing high-resolution remotely sensed images with complex semantic content as it only utilizes sign information in the local region. In this paper, a new texture characterization descriptor, known as completed homogeneous LBP (CHLBP), is proposed as an improved version of homogeneous LBP (HLBP) for LC classification of remotely sensed images. The CHLBP method mainly involves the following steps: first, sign and magnitude information from the HLBP descriptor is extracted, providing an effective alternative to the HLBP complementary contrast measure. Second, for each sign and magnitude function, a new splitting factor δ is used to obtain a depth relationship between the centre and its neighbouring pixels and to enhance noise robustness. Finally, the centre pixels representing the image grey level are also considered to contain discriminative information. The performance of our descriptor is evaluated using four challenging texture databases: Outex (TC10, TC12), Geofen Image Dataset (GID), and large-scale aerial (AID). Extensive experiments were performed using four classifiers: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Random Forest (RF), and Multi-Layer Perceptron (MLP), demonstrating the effectiveness and robustness of our descriptor against noise and free noise conditions in public remote sensing datasets.

In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars. info:eu-repo/semantics/publishedVersion

Wheat and barley are crucial food resources for the global population, making their growth and monitoring essential to enhance food security worldwide. Effective observation of these crops is necessary to address production issues and mitigate the impacts of weather changes. Advancements in remote sensing technology have significantly improved the observation and estimation processes. In this study, various spectral vegetation indices were utilized, along with canopy biophysical properties (such as LAI) and biochemical properties (like chlorophyll). These properties were derived from satellite data, specifically Landsat 8 and Sentinel-2, using tools like Google Earth Engine (GEE) and the R Program. Samples of wheat and barley were collected before reaching their optimal harvest stage, and a correlation was established between the vegetation indices (e.g. NDVI, NDWI, EVI, SAVI, CMFI, SR, RVI, GRVI, and NDRI) and actual production data. Yield prediction algorithms were employed, and the results were used to generate prediction yield maps. The findings revealed a strong relationship between the vegetation indices derived from Sentinel-2 and Landsat images and the actual grain yield, with an R2 of 0.77 and 0.71, respectively. Additionally, the study demonstrated that the most robust relationship was observed between the LAI data obtained from Sentinel-2 and cereal yield data, achieving an R2 of 0.68. Among the indices derived from Landsat images, NDWI exhibited the highest correlation with an R2 of 0.59. The root mean square error (RMSE) was found to be the lowest for Sentinel-2 (0.57) and Landsat 8 (1.54). Furthermore, the study indicated that the least significant relationship for grain yield prediction was observed between the NDRI index for Sentinel-2 (R2 0.1) and the SAVI index for Landsat images (R2 0.47).

Microbial biofilms have received great attention in the last few decades from both aquatic ecologists and biogeomorphologists. Most recently, this has focused on mapping biofilms to understand their spatial distributions and ecosystem services. Such studies often involve the use of satellite imagery, which typically provides large temporal and spatial scales and wide-range spectral information. Although satellites have the advantage of multi- and hyper-spectral sensors, images often have low spatial resolution that limits their use in river studies, where both rivers are narrower and stream processes occur at resolutions smaller than the footprint of satellite sensors. Spatial resolution is sensor quality dependent but also controlled by sensor elevation above the ground. Hence, high resolutions can be achieved either by using a very expensive sensor or by decreasing the distance between the target area and the sensor itself. To date, sensor technology has advanced to a point where multi- or even hyper-spectral cameras can be easily carried out by an Uncrewed Aerial Vehicle (UAV) at unprecedented spatial resolutions. Where such sensors have high spectral resolution, they are often prohibitively expensive, especially as their use in extreme environments such as glacial forefields risks UAV damage. In this paper, we test the performance of visible band ratios in mapping biofilms in an Alpine glacier forefield characterized by a well-developed and heterogeneous stream ecosystem but using a low-cost UAV. The paper shows that low-cost and consumer grade UAVs can be easily deployed in such extreme environments, delivering both quality RGB images for photogrammetric (SfM-MVS) processing and sufficient spectral information for benthic biofilm mapping at high temporal and spatial resolution. RGB cameras are an alternative to expensive multi- or hyper-spectral cameras.Phototrophic biofilms can be detected and mapped through visible band ratios.High-temporal and high-resolution imagery can be collected by consumer-grade UAVs.Biofilm presence is restricted to stable and water-fed terraces in summer. RGB cameras are an alternative to expensive multi- or hyper-spectral cameras. Phototrophic biofilms can be detected and mapped through visible band ratios. High-temporal and high-resolution imagery can be collected by consumer-grade UAVs. Biofilm presence is restricted to stable and water-fed terraces in summer.

This article describes algorithms to extract tree crowns using two-dimensional (2D) and three-dimensional (3D) segmentation. As a first step, a 2D-search detected the tallest trees but was unable to detect trees located below other trees. However, a 3D-search for local maxima of model fits could be used in a second step to detect trees also in lower canopy layers. We compared tree detection results from ALS carried out at 1450 m above ground level (high altitude) and tree detection results from ALS carried out at 150 m above ground level (low altitude). For validation, we used manual measurements of trees in ten large field plots, each with an 80 m diameter, in a hemiboreal forest in Sweden (lat. 58 degrees 28' N, long. 13 degrees 38' E). In order to measure the effect of using algorithms with different computational costs, we validated the tree detection from the 2D segmentation step and compared the results with the 2D segmentation followed by 3D segmentation of the ALS point cloud. When applying 2D segmentation only, the algorithm detected 87% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 91% using low-altitude ALS data. However, when applying 3D segmentation as well, the algorithm detected 92% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 99% using low-altitude ALS data. For all combinations of algorithms and data resolutions, undetected trees accounted for, on average, 0-5% of the total stem volume in the field plots. The 3D tree crown segmentation, which was using crown density models, made it possible to detect a large percentage of trees in multi-layered forests, compared with using only a 2D segmentation method.

Quick and efficient classification of images is important in many Remote Sensing Image (RSI) understanding tasks. The enormous availability of the RSI makes the classification a challenging task, w...

Using the cross-validation approach, strategies for estimating biophysical parameters are still pre-operational with synthetic aperture radar (SAR) data. In this regard, the Joint Experiment for Cr...