{"type": "FeatureCollection", "features": [{"id": "10.1111/gcb.70130", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:19:26Z", "type": "Journal Article", "created": "2025-03-18", "title": "What Are the Limits to the Growth of Boreal Fires?", "description": "ABSTRACT

Boreal forest regions, including East Siberia, have experienced elevated fire activity in recent years, leading to record\uffe2\uff80\uff90breaking greenhouse gas emissions and severe air pollution. However, our understanding of the factors that eventually halt fire spread and thus limit fire growth remains incomplete, hindering our ability to model their dynamics and predict their impacts. We investigated the locations and timing of 2.2 million fire stops\uffe2\uff80\uff94defined as 300\uffe2\uff80\uff89m unburned pixels along fire perimeters\uffe2\uff80\uff94across the vast East Siberian taiga. Fire stops were retrieved from remote sensing data covering over 27,000 individual fires that collectively burned 80 Mha between 2012 and 2022. Several geospatial datasets, including hourly fire weather data and landscape variables, were used to identify the factors contributing to individual fire stops. Our analysis attributed 87% of all fire stops to a statistically significant (p\uffe2\uff80\uff89<\uffe2\uff80\uff890.01) change in one or more of these drivers, with fire\uffe2\uff80\uff90weather drivers limiting fire growth over time and landscape drivers constraining it across space. We found clear regional and temporal variations in the importance of these drivers. For instance, landscape drivers\uffe2\uff80\uff94such as less flammable land cover and the presence of roads\uffe2\uff80\uff94were key constraints on fire growth in southeastern Siberia, where the landscape is more populated and fragmented. In contrast, fire weather was the primary constraint on fire growth in the remote northern taiga. Additionally, in central Yakutia, a major fire hotspot in recent years, fuel limitations from previous fires increasingly restricted fire spread. The methodology we present is adaptable to other biomes and can be applied globally, providing a framework for future attribution studies on global fire growth limitations. In northeast Siberia, we found that with increasing droughts and heatwaves, remote northern fires could potentially grow even larger in the future, with major implications for the global carbon cycle and climate. The outstanding tropical land climate characteristic over the past decades is rapid warming, with no significant large-scale precipitation trends. This warming is expected to continue but the effects on tropical vegetation are unknown. El Ni\uffc3\uffb1o-related heat peaks may provide a test bed for a future hotter world. Here we analyse tropical land carbon cycle responses to the 2015/16 El Ni\uffc3\uffb1o heat and drought anomalies using an atmospheric transport inversion. Based on the global atmospheric CO 2 and fossil fuel emission records, we find no obvious signs of anomalously large carbon release compared with earlier El Ni\uffc3\uffb1o events, suggesting resilience of tropical vegetation. We find roughly equal net carbon release anomalies from Amazonia and tropical Africa, approximately 0.5 PgC each, and smaller carbon release anomalies from tropical East Asia and southern Africa. Atmospheric CO anomalies reveal substantial fire carbon release from tropical East Asia peaking in October 2015 while fires contribute only a minor amount to the Amazonian carbon flux anomaly. Anomalously large Amazonian carbon flux release is consistent with downregulation of primary productivity during peak negative near-surface water anomaly (October 2015 to March 2016) as diagnosed by solar-induced fluorescence. Finally, we find an unexpected anomalous positive flux to the atmosphere from tropical Africa early in 2016, coincident with substantial CO release.

This article is part of a discussion meeting issue \uffe2\uff80\uff98The impact of the 2015/2016 El Ni\uffc3\uffb1o on the terrestrial tropical carbon cycle: patterns, mechanisms and implications\uffe2\uff80\uff99.

", "keywords": ["Life Sciences & Biomedicine - Other Topics", "FLUX", "0301 basic medicine", "Hot Temperature", "550", "551", "global warming", "01 natural sciences", "Carbon Cycle", "Greenhouse Gases", "03 medical and health sciences", "[SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/Climatology", "CHEMICAL-TRANSPORT MODEL", "carbon cycle", "INVERSION", "Biology", "TEMPERATURE", "11 Medical and Health Sciences", "0105 earth and related environmental sciences", "tropical forests", "El Nino-Southern Oscillation", "Evolutionary Biology", "Tropical Climate", "Science & Technology", "Atmosphere", "PHOTOSYNTHESIS", "EQUATORIAL PACIFIC", "Articles", "06 Biological Sciences", "15. Life on land", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Droughts", "[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology", "13. Climate action", "PRECIPITATION", "Remote Sensing Technology", "INDUCED CHLOROPHYLL FLUORESCENCE", "CO2", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "SENSITIVITY", "environment", "Life Sciences & Biomedicine", "fire"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/135234/8/Tropical%20land%20carbon%20cycle%20responses%20to%202015/16%20El%20Ni%C3%B1o%20as%20recorded%20by%20atmospheric%20greenhouse%20gas%20and%20remote%20sensing%20data.pdf"}, {"href": "https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2017.0302"}, {"href": "https://doi.org/10.1098/rstb.2017.0302"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Philosophical%20Transactions%20of%20the%20Royal%20Society%20B%3A%20Biological%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1098/rstb.2017.0302", "name": "item", "description": "10.1098/rstb.2017.0302", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1098/rstb.2017.0302"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-08T00:00:00Z"}}, {"id": "10.1098/rstb.2017.0408", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:19:07Z", "type": "Journal Article", "created": "2018-10-08", "title": "Widespread reduction in sun-induced fluorescence from the Amazon during the 2015/2016 El Ni\u00f1o", "description": "

The tropical carbon balance dominates year-to-year variations in the CO 2 exchange with the atmosphere through photosynthesis, respiration and fires. Because of its high correlation with gross primary productivity (GPP), observations of sun-induced fluorescence (SIF) are of great interest. We developed a new remotely sensed SIF product with improved signal-to-noise in the tropics, and use it here to quantify the impact of the 2015/2016 El Ni\uffc3\uffb1o\uffc2\uffa0Amazon drought. We find that SIF was strongly suppressed over areas with anomalously high temperatures and decreased levels of water in the soil. SIF went below its climatological range starting from the end of the 2015 dry season (October) and returned to normal levels by February 2016 when atmospheric conditions returned to normal, but well before the end of anomalously low precipitation that persisted through June 2016. Impacts were not uniform across the Amazon basin, with the eastern part experiencing much larger (10\uffe2\uff80\uff9315%) SIF reductions than the western part of the basin (2\uffe2\uff80\uff935%). We estimate the integrated loss of GPP relative to eight previous years to be 0.34\uffe2\uff80\uff930.48 PgC in the three-month period October\uffe2\uff80\uff93November\uffe2\uff80\uff93December 2015.

This article is part of a discussion meeting issue \uffe2\uff80\uff98The impact of the 2015/2016 El Ni\uffc3\uffb1o on the terrestrial tropical carbon cycle: patterns, mechanisms and implications\uffe2\uff80\uff99.

", "keywords": ["0301 basic medicine", "FLUXES", "El Ni\u00f1o-Southern Oscillation", "Amazon rainforest", "sun-induced fluorescence", "El Ni\u00f1o Southern Oscillation", "drought response", "Forests", "SOUTHERN-OSCILLATION", "01 natural sciences", "Fluorescence", "Trees", "SCIAMACHY", "03 medical and health sciences", "GOME-2", "ATMOSPHERIC CARBON-DIOXIDE", "SATELLITE", "0105 earth and related environmental sciences", "El Nino-Southern Oscillation", "Amazone rainforest", "Articles", "15. Life on land", "tropical terrestrial carbon cycle", "gross primary production", "TERRESTRIAL CHLOROPHYLL FLUORESCENCE", "SIMULATIONS", "6. Clean water", "Droughts", "CLIMATE", "13. Climate action", "BALANCE", "Remote Sensing Technology", "Sunlight", "Brazil"]}, "links": [{"href": "https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2017.0408"}, {"href": "https://doi.org/10.1098/rstb.2017.0408"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Philosophical%20Transactions%20of%20the%20Royal%20Society%20B%3A%20Biological%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1098/rstb.2017.0408", "name": "item", "description": "10.1098/rstb.2017.0408", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1098/rstb.2017.0408"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-08T00:00:00Z"}}, {"id": "10.3390/s22020645", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:22:01Z", "type": "Journal Article", "created": "2022-01-17", "title": "Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing", "description": "

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

", "keywords": ["Agriculture and Food Sciences", "2. Zero hunger", "Spatial Analysis", "precision agriculture", "ACCURACY", "Chemical technology", "management zone delineation", "TP1-1185", "04 agricultural and veterinary sciences", "15. Life on land", "Article", "VARIABILITY", "Soil", "YIELD", "FUSION", "feature selection", "ATTRIBUTES", "clustering; feature selection; management zone delineation; precision agriculture", "Remote Sensing Technology", "Cluster Analysis", "0401 agriculture", " forestry", " and fisheries", "FIELD", "SOIL-PHOSPHORUS", "Algorithms", "clustering"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://doi.org/10.3390/s22020645"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s22020645", "name": "item", "description": "10.3390/s22020645", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s22020645"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-14T00:00:00Z"}}, {"id": "10.1371/journal.pone.0315399", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:20:19Z", "type": "Journal Article", "created": "2025-04-02", "title": "High resolution descriptors for UAV mapping in biodiversity conservation \u2013 A case study of sandy steppe habitat renewal", "description": "

Due to the large-scale disappearance of grasslands there is an urgent need for revitalization. It calls for consistent and accessible monitoring and mapping plans, and an integrated management approach. However, revitalization efforts often focus solely on the vegetation component, and skip the link to other animal species that perform vital functions as ecosystem engineers and umbrella species. In this study, we combine an in-situ standard phytocoenological survey with an UAV-based technology in the effort to improve the monitoring and mapping of the sandy steppe habitat of the European ground squirrel (Spermophilus citellus; EGS), undergoing revitalization in the northern Serbia. It is a model organism of an animal species that enables identifying habitat quality and quantity indicators to understand the broader implications of the ecosystem revitalization efforts on the wildlife populations. The proposed approach tested whether the commercially available RGB sensor and a relatively high flight height of the UAV have discriminative capacity to aid site managers by mapping identified steppe development stages (specific plant assemblages, reflecting different habitat types). Thus, a novel set of high-resolution image descriptors that are capable of discriminating plant mixtures corresponding to Fallow land, Forest steppe and shrubs, Young steppe I and II, was proposed. Despite high resolution imaging, the method solves a challenging problem of UAV vegetation mapping in the case of limited spectral and spatial information in the image (by using only RGB camera and multitemporal approach). Although the lack of visual information that would allow identification of individual plant parts and shapes prevented the use of usual object-based image analysis, proposed pixel-based descriptors and feature selection were able to provide the extent of the targeted areas and their compositional carriers. Presented holistic approach enables implementation of effective management strategies that support the entire ecological community.

This study presents a robust framework for spatially explicit monitoring of soil properties and Above Ground Biomass (AGB) estimation in Mediterranean agroforestry and cropland systems by integrating remote sensing (RS) and artificial intelligence (AI). These variables are critical for assimilation into process-based models for Soil Organic Carbon (SOC) dynamics monitoring within a Monitoring, Reporting, and Verification (MRV) system. The framework was developed as part of the MRV4SOC project in Spain, aimed at designing a comprehensive, robust, and cost-effective Tier-3 approach. The primary goal is to produce high-quality geospatial layers of topsoil properties and AGB estima tion, which serve as key inputs for SOC dynamics modeling.The methodology was tested at two long-term demonstration sites in Spain: Quercus ilex Dehesas in Extremadura (SW Spain) and rainfed cereal crops at La Canaleja experimental farm in central Spain. These agroecosystems provide diverse testing grounds for scalable and transferable SOC assessment methodologies within an MRV framework. The approach integrates multi-temporal remote sensing data (2018&#8211;2022) from Sentinel-2 and Landsat satellites with machine learning models to predict essential soil properties (SOC, Sand, Silt, Clay, pH, and Total N) and AGB. Ground truth data for AGB estimation were sourced from the Spanish National Forest Inventory (SNFI), while soil property predictions utilized the LUCAS 2018 topsoil libraries due to limited site-specific datasets for model training. A bare soil reflectance composite (2018&#8211;2022) derived from Sentinel-2 bands (B02&#8211;B12) at 20-meter resolution was employed for geospatial soil property mapping.Given the limited availability of ground truth data, simpler models like Quantile Regression Forests (QRF) and XGBoost were selected. QRF achieved better accuracy for soil texture properties, with R&#178; = 0.62 for clay and outperforming XGBoost for SOC (R&#178; = 0.63) and pH (R&#178; = 0.76) in the agroforestry site. However, XGBoost performed better for SOC (R&#178; = 0.54) and total nitrogen in croplands, as well as for sand, silt, clay, and total nitrogen in the agroforestry site (R&#178; = 0.61 for clay). For AGB estimation in the Dehesas area, a machine learning approach was implemented using SNFI data and remote sensing-derived transformation features. A gradient boosting algorithm (LightGBM) resulted in an R&#178; value of 0.8. In La Canaleja, a bare soil reflectance composite was similarly employed for soil property mapping. Further analysis will be carried out to develop a bottom-up approach for monitoring SOC using these products and process-based modelsUncertainty analysis using Prediction Interval Ratio (PIR) assessment was conducted separately for landscape (L) and sub-landscape (SL) levels. While most properties showed medium to low uncertainty, sand and silt exhibited higher variability in croplands, and SOC displayed the highest uncertainty in the agroforestry site across L and SL levels.This methodology contributes significantly to improving MRV systems by delivering high-quality geospatial layers for SOC dynamics monitoring in complex environments. Increasing ground truth data availability is essential for enhancing model accuracy and minimizing prediction uncertainties further.

", "keywords": ["Cropland management", "Artificial Intelligence", "Remote Sensing Technology", "Agroforestry"]}, "links": [{"href": "https://doi.org/10.5194/egusphere-egu25-13513"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu25-13513", "name": "item", "description": "10.5194/egusphere-egu25-13513", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu25-13513"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}, {"id": "11586/391721", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:26:15Z", "type": "Journal Article", "created": "2021-12-23", "title": "Use of remote sensing to evaluate the effects of environmental factors on soil salinity in a semi-arid area", "description": "The global water crisis, driven by water scarcity and water quality deterioration, is expected to continue and intensify in dry and overpopulated areas, and will play a critical role in meeting future agricultural demands. Sustainability of agriculture irrigated with low quality water will require a comprehensive approach to soil, water, and crop management consisting of site- and situation-specific preventive measures and management strategies. Other problem related with water quality deterioration is soil salinization. Around 1Bha globally are salinized and soil salinization may be accelerating for several reasons including the changing climate. The consequences of climate change on soil salinization need to be monitored and mapped and, in this sense, remote sensing has been successfully applied to soil salinity monitoring. Although many issues remain to be resolved, some as important as the imbalance between ground-based measurements and satellite data. The main objective of this paper was to determine the influence of environmental factors on salinity from natural causes, and its effect on irrigated agriculture with degraded water. The study was developed on Campo de Cartagena, an intensive water-efficient irrigated area which main fruit tree is citrus (30%), a sensible crop to salinity. Nine representative citrus farms were selected, soil samples were analysed and different remote sensing indices and sets of environmental data were applied. Despite the heterogeneity between variables found by the descriptive analysis of the data, the relationship between farms, soil salinity and environmental data showed that applied salinity spectral indices were valid to detect soil salinity in citrus trees. Also, a set of environmental characterization provided useful information to determine the variables that most influence primary salinity in crops. Although the data extracted from spatial analysis indicated that to apply soil salinity predictive models, other variables related to agricultural management practices must be incorporated.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Agricultural", "Salinity", "550", "Degraded water", "Secondary soil salinization", "Crops", "Agriculture", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "6. Clean water", "12. Responsible consumption", "Soil", "13. Climate action", "Remote Sensing Technology", "11. Sustainability", "Irrigated agriculture", "0401 agriculture", " forestry", " and fisheries", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/11586/391721"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11586/391721", "name": "item", "description": "11586/391721", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11586/391721"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "1854/LU-8746428", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:26:24Z", "type": "Journal Article", "created": "2022-01-16", "title": "Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing", "description": "

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

", "keywords": ["Agriculture and Food Sciences", "2. Zero hunger", "Spatial Analysis", "precision agriculture", "ACCURACY", "Chemical technology", "management zone delineation", "TP1-1185", "04 agricultural and veterinary sciences", "15. Life on land", "Article", "VARIABILITY", "Soil", "YIELD", "FUSION", "feature selection", "ATTRIBUTES", "clustering; feature selection; management zone delineation; precision agriculture", "Remote Sensing Technology", "Cluster Analysis", "0401 agriculture", " forestry", " and fisheries", "FIELD", "SOIL-PHOSPHORUS", "Algorithms", "clustering"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://doi.org/1854/LU-8746428"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8746428", "name": "item", "description": "1854/LU-8746428", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8746428"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-14T00:00:00Z"}}, {"id": "PMC11906168", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:29:48Z", "type": "Journal Article", "created": "2025-04-02", "title": "High resolution descriptors for UAV mapping in biodiversity conservation \u2013 A case study of sandy steppe habitat renewal", "description": "

Due to the large-scale disappearance of grasslands there is an urgent need for revitalization. It calls for consistent and accessible monitoring and mapping plans, and an integrated management approach. However, revitalization efforts often focus solely on the vegetation component, and skip the link to other animal species that perform vital functions as ecosystem engineers and umbrella species. In this study, we combine an in-situ standard phytocoenological survey with an UAV-based technology in the effort to improve the monitoring and mapping of the sandy steppe habitat of the European ground squirrel (Spermophilus citellus; EGS), undergoing revitalization in the northern Serbia. It is a model organism of an animal species that enables identifying habitat quality and quantity indicators to understand the broader implications of the ecosystem revitalization efforts on the wildlife populations. The proposed approach tested whether the commercially available RGB sensor and a relatively high flight height of the UAV have discriminative capacity to aid site managers by mapping identified steppe development stages (specific plant assemblages, reflecting different habitat types). Thus, a novel set of high-resolution image descriptors that are capable of discriminating plant mixtures corresponding to Fallow land, Forest steppe and shrubs, Young steppe I and II, was proposed. Despite high resolution imaging, the method solves a challenging problem of UAV vegetation mapping in the case of limited spectral and spatial information in the image (by using only RGB camera and multitemporal approach). Although the lack of visual information that would allow identification of individual plant parts and shapes prevented the use of usual object-based image analysis, proposed pixel-based descriptors and feature selection were able to provide the extent of the targeted areas and their compositional carriers. Presented holistic approach enables implementation of effective management strategies that support the entire ecological community.