{"type": "FeatureCollection", "features": [{"id": "10.1016/j.compag.2021.106421", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:15:47Z", "type": "Journal Article", "created": "2021-08-31", "title": "Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms", "description": "Rapid and accurate estimation of rice Nitrogen Nutrition Index (NNI) is beneficial for management of nitrogen application in rice production. Traditional estimation methods required manual actual measurement data in the field, which was time-consuming and cost-expensive, and RGB images from unmanned aerial vehicle (UAV) provided an alternative option for nitrogen nutrition index (NNI) monitoring. In this study, RGB images from unmanned aerial vehicle (UAV) were obtained from each growth period of rice, and six machine learning (ML) algorithms, i.e., adaptive boosting (AB), artificial neural network (ANN), K-nearest neighbor (KNN), partial least squares (PLSR), random forest (RF) and support vector machine (SVM), were used to extract target information for estimating NNI as well as vegetation index (VI). Results showed that most UAV VIs were significantly correlated with rice NNI at the key growing periods; the estimation results of rice NNI using six ML algorithms showed that the RF algorithms performed the best at each growth period with the determination coefficient (R<sup>2</sup> ) ranged from 0.88 to 0.96 and room mean square error (RMSE) ranged from 0.03 to 0.07, in which the estimation of NNI was the best in filling period and the early jointing stage. Rice NNI at the early jointing stage was significantly correlated with soil available nitrogen (AN) with the R<sup>2 </sup>of 0.84 in Pukou and 0.72 in Luhe, respectively, and rice NNI was significantly correlated with the yield with the R2 of more than 0.7 in Pukou at the whole period and more than 0.7 in Luhe from late jointing to maturity stage. Therefore, the combination of RGB images from UAV and ML algorithms was a scalable, simple and inexpensive method for rapid qualification of rice NNI, which effectively improved nitrogen use efficiency and provided guidance for precision fertilization in rice production.", "keywords": ["2. Zero hunger", "Machine learning", "0211 other engineering and technologies", "0401 agriculture", " forestry", " and fisheries", "Precision fertilization", "Rice", "Nitrogen nutrition index", "Unmanned aerial vehicle", "04 agricultural and veterinary sciences", "02 engineering and technology", "6. Clean water"], "contacts": [{"organization": "Zhengchao Qiu, Ma, Fei, Zhenwang Li, Xuebin Xu, Haixiao Ge, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.compag.2021.106421"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.compag.2021.106421", "name": "item", "description": "10.1016/j.compag.2021.106421", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.compag.2021.106421"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-01T00:00:00Z"}}, {"id": "10.1016/b978-0-444-64177-9.00006-0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:15:15Z", "type": "Report", "created": "2020-04-16", "title": "SfM photogrammetry for GeoArchaeology", "description": "Geoarchaeological studies have benefits from new technological developments in remote-sensing technologies that have become an integral and important part of the archeological researches. In particular, structure-from-motion (SfM) photogrammetry is one of the most successful emerging techniques in high-resolution topography (HRT) and provides exceptionally fast, low-cost, and easy three-dimensional (3D) survey for geoscience applications. In this chapter, we present an example of SfM application for geoarchaeology. The purpose is to realize HRT digital terrain models (DTMs) of an area of prehistoric agricultural terracing together with a geoarchaeological excavation trench in the Ingram Valley, Northumberland National Park, NE England. The study area is one of the six pilot case studies of TerrACE archeological research project (ERC-2017-ADG: 787790, 2018\u20132023; https://www.terrace.no/), a 5-year European Research Council grant funded by the European Union. An integrated approach utilizing ground-based and UAV (nadir and oblique) images was used to preserve fine-grained topographic detail and permit the accurate survey of highly vegetated areas and steep or subvertical surfaces (e.g., vertical walls of terraces), while also allowing for the capture of large spatial datasets. The SfM-DTM provided an accurate and high level of detail of the terrace landscape, the archeological features, and the soil and sediment stratigraphy along the excavation trench. An additional terrace was identified that had not been recognized before due to the HRT study bringing out a level of detail that had not been previously observable in this area. The SfM 3D outputs allowed the extraction of profiles, sections, scaled plans, and orthomosaics of the terrace complex and the excavation trench, simplifying and speeding the archeologist's field and laboratory work. SfM has shown it to be a rapid, cost effective, and highly accurate technique for surveying archeological sites at both a landscape and localized scale and adding new and more accurate information in nationally important landscapes and beyond.", "keywords": ["Archeological sites; Digital terrain models; Prehistoric agricultural terraces; Structure from motion; TerrACE project; Unmanned aerial vehicles", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.1016/b978-0-444-64177-9.00006-0"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/b978-0-444-64177-9.00006-0", "name": "item", "description": "10.1016/b978-0-444-64177-9.00006-0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/b978-0-444-64177-9.00006-0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.3390/rs13163272", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:49Z", "type": "Journal Article", "created": "2021-08-19", "title": "UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms\u2014support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)\u2014were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.</p></article>", "keywords": ["<i>Map of the Natural Vegetation of Europe</i>", "Orfeo ToolBox", "unmanned aerial vehicle; object-based image analysis; Orfeo ToolBox; QGIS; random forest; hoverfly; Map of the Natural Vegetation of Europe", "Science", "Q", "0211 other engineering and technologies", "Unmanned aerial vehicle", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Object-based image analysis", "Map of the Natural Vegetation of Europe", "13. Climate action", "unmanned aerial vehicle;\u00a0object-based image analysis;\u00a0Orfeo ToolBox;\u00a0QGIS;\u00a0random forest;\u00a0hoverfly;\u00a0Map of the Natural Vegetation of Europe", "unmanned aerial vehicle", "object-based image analysis", "Hoverfly", "QGIS", "random forest", "Random forest", "hoverfly", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3272/pdf"}, {"href": "https://doi.org/10.3390/rs13163272"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13163272", "name": "item", "description": "10.3390/rs13163272", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13163272"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-18T00:00:00Z"}}, {"id": "10.3390/rs12193228", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:49Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.</p></article>", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "estimation accuracy", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/10.3390/rs12193228"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs12193228", "name": "item", "description": "10.3390/rs12193228", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12193228"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "10.3390/rs16081324", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:50Z", "type": "Journal Article", "created": "2024-04-10", "title": "Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This systematic review explores the role of remote sensing technology in addressing the requirements of sustainable olive growing, set against the backdrop of growing global food demands and contemporary environmental constraints in agriculture. The critical analysis presented in this document assesses different remote sensing platforms (satellites, manned aircraft vehicles, unmanned aerial vehicles and terrestrial equipment) and sensors (RGB, multispectral, thermal, hyperspectral and LiDAR), emphasizing their strategic selection based on specific study aims and geographical scales. Focusing on olive growing, particularly prominent in the Mediterranean region, this article analyzes the diverse applications of remote sensing, including the management of inventory and irrigation; detection/monitoring of diseases and phenology; and estimation of crucial parameters regarding biophysical parameters, water stress indicators, crop evapotranspiration and yield. Through a global perspective and insights from studies conducted in diverse olive-growing regions, this review underscores the potential benefits of remote sensing in shaping and improving sustainable agricultural practices, mitigating environmental impacts and ensuring the economic viability of olive trees.</p></article>", "keywords": ["RGB", "2. Zero hunger", "multispectral", "Science", "Q", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "satellite imagery", "manned aircraft vehicles", "12. Responsible consumption", "hyperspectral", "0401 agriculture", " forestry", " and fisheries", "unmanned aerial vehicles"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/16/8/1324/pdf"}, {"href": "https://doi.org/10.3390/rs16081324"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs16081324", "name": "item", "description": "10.3390/rs16081324", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs16081324"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-09T00:00:00Z"}}, {"id": "10.5281/zenodo.5770286", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:06Z", "type": "Journal Article", "created": "2021-08-18", "title": "UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms\u2014support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)\u2014were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.</p></article>", "keywords": ["<i>Map of the Natural Vegetation of Europe</i>", "Orfeo ToolBox", "unmanned aerial vehicle; object-based image analysis; Orfeo ToolBox; QGIS; random forest; hoverfly; Map of the Natural Vegetation of Europe", "Science", "Q", "0211 other engineering and technologies", "Unmanned aerial vehicle", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Object-based image analysis", "Map of the Natural Vegetation of Europe", "13. Climate action", "unmanned aerial vehicle", "object-based image analysis", "Hoverfly", "QGIS", "random forest", "Random forest", "hoverfly", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3272/pdf"}, {"href": "https://doi.org/10.5281/zenodo.5770286"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5770286", "name": "item", "description": "10.5281/zenodo.5770286", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5770286"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-18T00:00:00Z"}}, {"id": "10.5281/zenodo.8085976", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:22Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.</p></article>", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8085976"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8085976", "name": "item", "description": "10.5281/zenodo.8085976", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8085976"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "10.5281/zenodo.8090784", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:23Z", "type": "Journal Article", "created": "2021-08-31", "title": "Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms", "description": "Rapid and accurate estimation of rice Nitrogen Nutrition Index (NNI) is beneficial for management of nitrogen application in rice production. Traditional estimation methods required manual actual measurement data in the field, which was time-consuming and cost-expensive, and RGB images from unmanned aerial vehicle (UAV) provided an alternative option for nitrogen nutrition index (NNI) monitoring. In this study, RGB images from unmanned aerial vehicle (UAV) were obtained from each growth period of rice, and six machine learning (ML) algorithms, i.e., adaptive boosting (AB), artificial neural network (ANN), K-nearest neighbor (KNN), partial least squares (PLSR), random forest (RF) and support vector machine (SVM), were used to extract target information for estimating NNI as well as vegetation index (VI). Results showed that most UAV VIs were significantly correlated with rice NNI at the key growing periods; the estimation results of rice NNI using six ML algorithms showed that the RF algorithms performed the best at each growth period with the determination coefficient (R<sup>2</sup> ) ranged from 0.88 to 0.96 and room mean square error (RMSE) ranged from 0.03 to 0.07, in which the estimation of NNI was the best in filling period and the early jointing stage. Rice NNI at the early jointing stage was significantly correlated with soil available nitrogen (AN) with the R<sup>2 </sup>of 0.84 in Pukou and 0.72 in Luhe, respectively, and rice NNI was significantly correlated with the yield with the R2 of more than 0.7 in Pukou at the whole period and more than 0.7 in Luhe from late jointing to maturity stage. Therefore, the combination of RGB images from UAV and ML algorithms was a scalable, simple and inexpensive method for rapid qualification of rice NNI, which effectively improved nitrogen use efficiency and provided guidance for precision fertilization in rice production.", "keywords": ["2. Zero hunger", "Machine learning", "0211 other engineering and technologies", "0401 agriculture", " forestry", " and fisheries", "Precision fertilization", "Rice", "Nitrogen nutrition index", "Unmanned aerial vehicle", "04 agricultural and veterinary sciences", "02 engineering and technology", "6. Clean water"], "contacts": [{"organization": "Zhengchao Qiu, Ma, Fei, Zhenwang Li, Xuebin Xu, Haixiao Ge, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8090784"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090784", "name": "item", "description": "10.5281/zenodo.8090784", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090784"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-01T00:00:00Z"}}, {"id": "21.15107/rcub_nardus_21364", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:38Z", "type": "Report", "title": "\u0423\u0442\u0438\u0446\u0430\u0458 \u043a\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0430 \u0441\u0442\u0430\u043d\u0438\u0448\u0442\u0430 \u0438 \u043f\u0440\u0435\u0434\u0435\u043b\u0430 \u043d\u0430 \u0434\u0438\u0432\u0435\u0440\u0437\u0438\u0442\u0435\u0442 \u043e\u0441\u043e\u043b\u0438\u043a\u0438\u0445 \u043c\u0443\u0432\u0430 (Diptera: Syrphidae) \u0443 \u0421\u0440\u0431\u0438\u0458\u0438", "description": "Open AccessLJudske aktivnosti menjaju karakteristike stani\u0161ta i predela koji predstavljaju preduslov za postojanje i odr\u017eavanje biolo\u0161kog diverziteta. Predmet istra\u017eivanja u okviru ove doktorske disertacije je uticaj karakteristika stani\u0161ta i predela na diverzitet osolikih muva na 27 istra\u017eivanih lokaliteta na teritoriji Srbije. Priloikom odre\u0111ivanja loklanih karakteristika stani\u0161ta podaci su dobijeni klasifikacijom ortomozaika pomo\u0107u bespilotne letelice kao i analizom uzoraka hemijskih i fizi\u010dkih svojstava zemlji\u0161ta na istra\u017eivanim lokalitetima. Definisani su, kvantifikovani i pore\u0111eni stepeni degradacije stani\u0161ta iz mapa dobijenih bespilotnom letelicom sa mapom potencijalne vegetacije i mapom zemlji\u0161nog pokriva\u010da. Pored toga, utvr\u0111en je gubitak vrsta na istra\u017eivanim lokalitetima odnosom recentnog i potencijalnog diverziteta osolikih muva u Srbiji. Uticaji definisanih karakteristika stani\u0161ta - lokalnih i predeonih analizirani su kroz \u010detiri modela regresije i klasifikacije: recentni diverzitet, gubitak recentnog diverziteta, prisustvo i gubitak za\u0161ti\u0107enih i strogo za\u0161ti\u0107enih vrsta. Lokaliteti na podru\u010dju Vojvodine i lokalitet Donje Vlase su najsiroma\u0161nija stani\u0161ta u pogledu raznovrsnosti faune osolikih muva zbog intenzivnih antropogenih aktivnosti kao \u0161to su poljoprivreda, intenzivna upotreba agrohemikalija i se\u010da \u0161uma, \u0161to dovodi do ireverzibilnih procesa daljeg gubitka vrsta. Nasuprot njima, lokaliteti Demizlok i Malinik sa najve\u0107im diverzitetom osolikih muva u Srbiji istovremeno se odlikuju najmanjim stepenom degradacije stani\u0161ta. Navedeni lokaliteti imaju potencijal da se priklju\u010de za\u0161ti\u0107enim podru\u010djima u cilju daljeg o\u010duvanja biodiverziteta. U tri od \u010detiri analizirana modela klasa \u0161uma, dobijena na razli\u010ditim skalama, je glavno obele\u017eje koje obja\u0161njava trend i rangiranost diverziteta osolikih muva na istra\u017eivanim lokalitetima. Potvr\u0111eno je da su izvorne bukove \u0161ume glavni centri raznovrsnosti vrsta koje zaslu\u017euju posebnu pa\u017enju u konzervacionom smislu. Rezultati ovog istra\u017eivanja ukazuju na va\u017enost klasifikacije lokalnih karakteristika stani\u0161ta pomo\u0107u bespilotne letelice i potvr\u0111uju uticaj na diverzitet osolikih muva i kao takve imaju potencijal da postanu alati za monitoring osolikih muva i drugih opra\u0161iva\u010da i model organizama na nacionalnom i evropskom nivou.", "keywords": ["human impact", "klasifikacija stani\u0161ta", "land use", "na\u010din kori\u0161\u0107enja zemlji\u0161ta", "syrphid flies", "\u043d\u0430\u0447\u0438\u043d \u043a\u043e\u0440\u0438\u0448\u045b\u0435\u045a\u0430 \u0437\u0435\u043c\u0459\u0438\u0448\u0442\u0430", "ortomozaik", "orthomosaic", "\u043a\u043b\u0430\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0458\u0430 \u0441\u0442\u0430\u043d\u0438\u0448\u0442\u0430", "\u0431\u0435\u0441\u043f\u0438\u043b\u043e\u0442\u043da \u043b\u0435\u0442\u0435\u043b\u0438\u0446a", "habitat classification", "bespilotna letelica", "sirfide", "\u0447\u043e\u0432\u0435\u043a\u043e\u0432 \u0443\u0442\u0438\u0446\u0430\u0458", "unmanned aerial vehicle", "\u010dovekov uticaj", "\u043e\u0440\u0442\u043e\u043c\u043e\u0437\u0430\u0438\u043a", "\u0441\u0438\u0440\u0444\u0438\u0434\u0435"]}, "links": [{"href": "https://doi.org/21.15107/rcub_nardus_21364"}, {"rel": "self", "type": "application/geo+json", "title": "21.15107/rcub_nardus_21364", "name": "item", "description": "21.15107/rcub_nardus_21364", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/21.15107/rcub_nardus_21364"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-10T00:00:00Z"}}, {"id": "3092600768", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:41Z", "type": "Journal Article", "created": "2020-10-05", "title": "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.</p></article>", "keywords": ["2. Zero hunger", "object-oriented segmentation method", "optimal index method", "rice", "Science", "Q", "rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "multi-stage vegetation index", "15. Life on land", "estimation accuracy", "growth indicators", "13. Climate action", "unmanned aerial vehicle", "0401 agriculture", " forestry", " and fisheries"], "contacts": [{"organization": "Zhengchao Qiu, Haitao Xiang, Fei Ma, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/19/3228/pdf"}, {"href": "https://doi.org/3092600768"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3092600768", "name": "item", "description": "3092600768", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3092600768"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-03T00:00:00Z"}}, {"id": "3195911513", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:49Z", "type": "Journal Article", "created": "2021-08-19", "title": "UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms\u2014support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)\u2014were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.</p></article>", "keywords": ["<i>Map of the Natural Vegetation of Europe</i>", "Orfeo ToolBox", "unmanned aerial vehicle; object-based image analysis; Orfeo ToolBox; QGIS; random forest; hoverfly; Map of the Natural Vegetation of Europe", "Science", "Q", "0211 other engineering and technologies", "Unmanned aerial vehicle", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Object-based image analysis", "Map of the Natural Vegetation of Europe", "13. Climate action", "unmanned aerial vehicle;\u00a0object-based image analysis;\u00a0Orfeo ToolBox;\u00a0QGIS;\u00a0random forest;\u00a0hoverfly;\u00a0Map of the Natural Vegetation of Europe", "unmanned aerial vehicle", "object-based image analysis", "Hoverfly", "QGIS", "random forest", "Random forest", "hoverfly", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3272/pdf"}, {"href": "https://doi.org/3195911513"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3195911513", "name": "item", "description": "3195911513", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3195911513"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-18T00:00:00Z"}}, {"id": "3198159648", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:49Z", "type": "Journal Article", "created": "2021-08-31", "title": "Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms", "description": "Rapid and accurate estimation of rice Nitrogen Nutrition Index (NNI) is beneficial for management of nitrogen application in rice production. Traditional estimation methods required manual actual measurement data in the field, which was time-consuming and cost-expensive, and RGB images from unmanned aerial vehicle (UAV) provided an alternative option for nitrogen nutrition index (NNI) monitoring. In this study, RGB images from unmanned aerial vehicle (UAV) were obtained from each growth period of rice, and six machine learning (ML) algorithms, i.e., adaptive boosting (AB), artificial neural network (ANN), K-nearest neighbor (KNN), partial least squares (PLSR), random forest (RF) and support vector machine (SVM), were used to extract target information for estimating NNI as well as vegetation index (VI). Results showed that most UAV VIs were significantly correlated with rice NNI at the key growing periods; the estimation results of rice NNI using six ML algorithms showed that the RF algorithms performed the best at each growth period with the determination coefficient (R<sup>2</sup> ) ranged from 0.88 to 0.96 and room mean square error (RMSE) ranged from 0.03 to 0.07, in which the estimation of NNI was the best in filling period and the early jointing stage. Rice NNI at the early jointing stage was significantly correlated with soil available nitrogen (AN) with the R<sup>2 </sup>of 0.84 in Pukou and 0.72 in Luhe, respectively, and rice NNI was significantly correlated with the yield with the R2 of more than 0.7 in Pukou at the whole period and more than 0.7 in Luhe from late jointing to maturity stage. Therefore, the combination of RGB images from UAV and ML algorithms was a scalable, simple and inexpensive method for rapid qualification of rice NNI, which effectively improved nitrogen use efficiency and provided guidance for precision fertilization in rice production.", "keywords": ["2. Zero hunger", "Machine learning", "0211 other engineering and technologies", "0401 agriculture", " forestry", " and fisheries", "Precision fertilization", "Rice", "Nitrogen nutrition index", "Unmanned aerial vehicle", "04 agricultural and veterinary sciences", "02 engineering and technology", "6. Clean water"], "contacts": [{"organization": "Zhengchao Qiu, Ma, Fei, Zhenwang Li, Xuebin Xu, Haixiao Ge, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/3198159648"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20and%20Electronics%20in%20Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3198159648", "name": "item", "description": "3198159648", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3198159648"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-01T00:00:00Z"}}, {"id": "50|nardusnacion::2f3238f2694f6732c72d8fee3ab334d2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:26:17Z", "type": "Report", "title": "\u0423\u0442\u0438\u0446\u0430\u0458 \u043a\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0430 \u0441\u0442\u0430\u043d\u0438\u0448\u0442\u0430 \u0438 \u043f\u0440\u0435\u0434\u0435\u043b\u0430 \u043d\u0430 \u0434\u0438\u0432\u0435\u0440\u0437\u0438\u0442\u0435\u0442 \u043e\u0441\u043e\u043b\u0438\u043a\u0438\u0445 \u043c\u0443\u0432\u0430 (Diptera: Syrphidae) \u0443 \u0421\u0440\u0431\u0438\u0458\u0438", "description": "Open AccessLJudske aktivnosti menjaju karakteristike stani\u0161ta i predela koji predstavljaju preduslov za postojanje i odr\u017eavanje biolo\u0161kog diverziteta. Predmet istra\u017eivanja u okviru ove doktorske disertacije je uticaj karakteristika stani\u0161ta i predela na diverzitet osolikih muva na 27 istra\u017eivanih lokaliteta na teritoriji Srbije. Priloikom odre\u0111ivanja loklanih karakteristika stani\u0161ta podaci su dobijeni klasifikacijom ortomozaika pomo\u0107u bespilotne letelice kao i analizom uzoraka hemijskih i fizi\u010dkih svojstava zemlji\u0161ta na istra\u017eivanim lokalitetima. Definisani su, kvantifikovani i pore\u0111eni stepeni degradacije stani\u0161ta iz mapa dobijenih bespilotnom letelicom sa mapom potencijalne vegetacije i mapom zemlji\u0161nog pokriva\u010da. Pored toga, utvr\u0111en je gubitak vrsta na istra\u017eivanim lokalitetima odnosom recentnog i potencijalnog diverziteta osolikih muva u Srbiji. Uticaji definisanih karakteristika stani\u0161ta - lokalnih i predeonih analizirani su kroz \u010detiri modela regresije i klasifikacije: recentni diverzitet, gubitak recentnog diverziteta, prisustvo i gubitak za\u0161ti\u0107enih i strogo za\u0161ti\u0107enih vrsta. Lokaliteti na podru\u010dju Vojvodine i lokalitet Donje Vlase su najsiroma\u0161nija stani\u0161ta u pogledu raznovrsnosti faune osolikih muva zbog intenzivnih antropogenih aktivnosti kao \u0161to su poljoprivreda, intenzivna upotreba agrohemikalija i se\u010da \u0161uma, \u0161to dovodi do ireverzibilnih procesa daljeg gubitka vrsta. Nasuprot njima, lokaliteti Demizlok i Malinik sa najve\u0107im diverzitetom osolikih muva u Srbiji istovremeno se odlikuju najmanjim stepenom degradacije stani\u0161ta. Navedeni lokaliteti imaju potencijal da se priklju\u010de za\u0161ti\u0107enim podru\u010djima u cilju daljeg o\u010duvanja biodiverziteta. U tri od \u010detiri analizirana modela klasa \u0161uma, dobijena na razli\u010ditim skalama, je glavno obele\u017eje koje obja\u0161njava trend i rangiranost diverziteta osolikih muva na istra\u017eivanim lokalitetima. Potvr\u0111eno je da su izvorne bukove \u0161ume glavni centri raznovrsnosti vrsta koje zaslu\u017euju posebnu pa\u017enju u konzervacionom smislu. Rezultati ovog istra\u017eivanja ukazuju na va\u017enost klasifikacije lokalnih karakteristika stani\u0161ta pomo\u0107u bespilotne letelice i potvr\u0111uju uticaj na diverzitet osolikih muva i kao takve imaju potencijal da postanu alati za monitoring osolikih muva i drugih opra\u0161iva\u010da i model organizama na nacionalnom i evropskom nivou.", "keywords": ["human impact", "klasifikacija stani\u0161ta", "land use", "na\u010din kori\u0161\u0107enja zemlji\u0161ta", "syrphid flies", "\u043d\u0430\u0447\u0438\u043d \u043a\u043e\u0440\u0438\u0448\u045b\u0435\u045a\u0430 \u0437\u0435\u043c\u0459\u0438\u0448\u0442\u0430", "ortomozaik", "orthomosaic", "\u043a\u043b\u0430\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0458\u0430 \u0441\u0442\u0430\u043d\u0438\u0448\u0442\u0430", "\u0431\u0435\u0441\u043f\u0438\u043b\u043e\u0442\u043da \u043b\u0435\u0442\u0435\u043b\u0438\u0446a", "habitat classification", "bespilotna letelica", "sirfide", "\u0447\u043e\u0432\u0435\u043a\u043e\u0432 \u0443\u0442\u0438\u0446\u0430\u0458", "unmanned aerial vehicle", "\u010dovekov uticaj", "\u043e\u0440\u0442\u043e\u043c\u043e\u0437\u0430\u0438\u043a", "\u0441\u0438\u0440\u0444\u0438\u0434\u0435"]}, "links": [{"href": "https://doi.org/50|nardusnacion::2f3238f2694f6732c72d8fee3ab334d2"}, {"rel": "self", "type": "application/geo+json", "title": "50|nardusnacion::2f3238f2694f6732c72d8fee3ab334d2", "name": "item", "description": "50|nardusnacion::2f3238f2694f6732c72d8fee3ab334d2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|nardusnacion::2f3238f2694f6732c72d8fee3ab334d2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-10T00:00:00Z"}}, {"id": "bddcad29-92ec-40a9-9b90-5deb3ffe7b2d", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[13.47, 53.29], [13.47, 53.43], [13.86, 53.43], [13.86, 53.29], [13.47, 53.29]]]}, "properties": {"themes": [{"concepts": [{"id": "farming"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "kettle holes"}, {"id": "plants"}, {"id": "water levels"}, {"id": "vegetation"}, {"id": "dynamics"}, {"id": "climate change"}, {"id": "plant succession"}, {"id": "unmanned aerial vehicles"}, {"id": "unmanned aerial vehicles"}, {"id": "water levels"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "Lebensr\u00e4ume und Biotope"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "opendata"}, {"id": "macrophytes"}], "scheme": "individual"}, {"concepts": [{"id": "Germany"}, {"id": "Brandenburg"}, {"id": "Uckermark"}, {"id": "Quillow"}], "scheme": "Individual"}], "rights": "Reports, articles, papers, scientific and non-scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data re-used from the BonaRes Data Centre www.bonares.de. This data were created as part of ZALF research activities\". Although every care has been taken in preparing and testing the data, ZALF and BonaRes Data Centre cannot guarantee that the data are correct; neither does ZALF and BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The ZALF and Data Centre will not be responsible for any direct or indirect use which might be made of the data. If access to actual data is requested, please contact the data owner/author because these underlay an embargo.", "updated": "2023-08-16", "type": "Dataset", "created": "2021-03-05", "language": "eng", "title": "Coverage of dominant plant taxa, water surface area and hydrogeomorphological information for kettle holes surveyed in 2016, 2018 and 2020.", "description": "The published Excel tables contain coverage data from dominant plant communities (response variables) as well as from environmental variables (explanatory variables) collected at kettle holes.\nThe dominant plant communities and their coverage were determined on the basis of UAS images in summer 2016, 2018 and 2020. The delimitation of homogeneous vegetation from the surrounding vegetation areas, the water or the soil was done manually using the color, texture and/or shape in the UAS images. In the end there were 14 dominant plant communities. \nThe environmental variables include the kettle hole and the water surface area, which were determined every year based on the same UAS images as dominant plant communities. In addition, we publish the hydrogeomorphological types of the examined kettle holes and the class of the shore slope, both variables  included as dummy variables.", "formats": [{"name": "CSV"}], "keywords": ["kettle holes", "plants", "water levels", "vegetation", "dynamics", "climate change", "plant succession", "unmanned aerial vehicles", "unmanned aerial vehicles", "water levels", "Lebensr\u00e4ume und Biotope", "opendata", "macrophytes", "Germany", "Brandenburg", "Uckermark", "Quillow"], "contacts": [{"name": "Paetzig, Marlene", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["author"], "phones": [{"value": "+49 33432 82-470"}], "emails": [{"value": "marlene.paetzig@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Str.84"], "city": "M\u00fcncheberg", "administrativeArea": "15374", "postalCode": "15374", "country": "Germany"}], "links": [{"href": {"url": "https://orcid.org/", "protocol": null, "protocol_url": "", "name": "orcid:0000-0003-1687-6982", "name_url": "", "description": "orcid", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Henning, Dorith", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["dataCollector"], "phones": [{"value": "+49 33432 82 332"}], "emails": [{"value": "henning@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Str.84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandenburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": null}]}, {"name": "D\u00fcker, Eveline", "organization": "unknown", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "unknown"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Kalettka, Thomas", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["dataCollector"], "phones": [{"value": "+49 (0)33432 82-361"}], "emails": [{"value": "tkalettka@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Str. 84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandenburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": null}]}, {"name": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": "Research Platform 'Data Analysis & Simulation' - WG Research Data Management", "roles": ["publisher"], "phones": [{"value": "+49 33432 82 300"}], "emails": [{"value": "dataservice@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Strasse 84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandenburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": null}]}, {"name": "P\u00e4tzig, Marlene", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["pointOfContact"], "phones": [{"value": "+49 33432 82-470"}], "emails": [{"value": "marlene.paetzig@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Str. 84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandeburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": {"url": "https://orcid.org/", "protocol": null, "protocol_url": "", "name": "orcid:0000-0003-1687-6982", "name_url": "", "description": "Orcid", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "P\u00e4tzig, Marlene", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["projectLeader"], "phones": [{"value": "+49 33432 82-470"}], "emails": [{"value": "marlene.paetzig@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Str. 84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandeburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": {"url": "https://orcid.org/", "protocol": null, "protocol_url": "", "name": "orcid:0000-0003-1687-6982", "name_url": "", "description": "Orcid", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "roles": ["contributor"]}]}, "links": [{"href": "https://maps.bonares.de/mapapps/resources/apps/bonares/index.html?lang=en&mid=bddcad29-92ec-40a9-9b90-5deb3ffe7b2d", "rel": "information"}, {"href": "https://metadata.bonares.de:443/smartEditor/preview/P\u00e4tzing_M..png", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "bddcad29-92ec-40a9-9b90-5deb3ffe7b2d", "name": "item", "description": "bddcad29-92ec-40a9-9b90-5deb3ffe7b2d", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/bddcad29-92ec-40a9-9b90-5deb3ffe7b2d"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["2016-07-15T00:00:00Z", "2021-08-01T00:00:00Z"]}}, {"id": "74c81a1b-546f-4ff6-8889-244397f0ce26", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[13.12, 52.93], [13.12, 52.94], [13.13, 52.94], [13.13, 52.93], [13.12, 52.93]]]}, "properties": {"themes": [{"concepts": [{"id": "farming"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "opendata"}, {"id": "unmanned aerial vehicles"}, {"id": "imagery"}, {"id": "remote sensing"}, {"id": "UAV"}, {"id": "RGB"}, {"id": "DAKIS"}], "scheme": "Individual"}, {"concepts": [{"id": "Boden"}], "scheme": "GEMET - 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Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the ZALF Datenerfassung's research activities.\" Although every care has been taken in preparing and testing the data, the ZALF Datenerfassung and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the ZALF Datenerfassung and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The ZALF Datenerfassung and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data.", "updated": "2025-07-24", "type": "Dataset", "created": "2025-07-15", "language": "eng", "title": "Remote sensing  RGB imagery Grossmutz (Loewenberger Land)", "description": "Georeferenced RGB orthophoto mosaic of a drone survey of the agroforestry project area (Bloch et al. 2024) from 21.08.2019.\nBloch, R.;, M.; Donat, T.; Cremer, und S.; Bellingrath-Kimura. \u201eProject Area - DAKIS\u201c. 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