Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes

Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Classification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image

GLCM FEATURES FOR LEARNING FLOODED VEGETATION FROM SENTINEL-1 AND SENTINEL-2 DATA

Efforts on flood mapping from active and passive satellite Earth Observation sensors increased in the last decade especially due to the availability of free datasets from European Space Agency’s Sentinel-1 and Sentinel-2 platforms. Regular data acquisition scheme also allows observing areas prone to natural hazards with a small temporal interval (within a week). Thus, before and after datasets are often available for detecting surface changes caused by flooding. This study investigates the contribution of textural variables to the predictive performance of a data-driven machine learning algorithm for detecting the effects of a flooding caused by Sardoba Dam break in Uzbekistan. In addition to the spectral channels of Sentinel-2 and polarization bands of Sentinel-1, two spectral indices (normalized difference vegetation index and modified normalized difference water index), and textural features of gray-level co-occurrence matrix (GLCM) were used with the Random Forest. Due to high dimensionality of input variables, principal component (PC) analysis was applied to the GLCM features and only the most significant PCs were used for modeling. The feature stacks used for learning were derived from both pre- and post-event Sentinel-1 and Sentinel-2 images. The models were validated through model test measures and external reference data obtained from PlanetScope imagery. The results show that the GLCM features improve the classification of flooded areas (from 82% to 93%) and flooded vegetation (from 17% to 78%) expressed in user’s accuracy. As an outcome of the study, the use of textural features is recommended for accurate mapping of flooded areas and flooded vegetation

Integração de imagens Orbitais Ópticas e SAR Com Processamento em Nuvem no Mapeamento da Cobertura da Terra no Cerrado

The land cover mapping is of great relevance for the environmental monitoring and land management. Time series from the synthetic aperture radar (SAR) of Sentinel-1 (S-1) and the MSI/Sentinel-2 (S-2) optical sensor provide promising conditions for the land cover mapping due to their spectral, spatial and temporal resolutions. Here, we explored the hypothesis that the combination of S-1 and S-2 time series allows higher accuracy in the land cover mapping in Cerrado biome. The images were classified using the Random Forest algorithm in the Google Earth Engine cloud processing platform. The classifications obtained using only the S-2 data (kappa = 89.99) showed higher accuracy than those with the S-1 data (kappa = 75.78). The classification efficiency increased by combining the S-1 and S-2 data (kappa = 93.07). The results found here suggest that the shortwave infrared band, the VH polarization from SAR data, and the Cellulose absorption index (CAI) and Hall Cover index were the most significant variables in the land cover mapping of the Cerrado biome.La cartografía de la cubierta del suelo es de suma importancia para la vigilancia del medio ambiente y la gestión del territorio. Las series temporales del radar de apertura sintética (SAR) de Sentinel-1 (S-1) y del sensor óptico MSI/Sentinel-2 (S-2) ofrecen condiciones favorables para la cartografía de la cubierta terrestre debido a sus resoluciones espectral, espacial y temporal. Este trabajo parte de la hipótesis de que la combinación entre las series temporales de S-1 y S-2 permite una mayor precisión en la cartografía de la cobertura del suelo en el bioma del Cerrado. Las imágenes se clasificaron mediante el algoritmo Random Forest en la plataforma de procesamiento en la nube Google Earth Engine. Las clasificaciones obtenidas sólo con los datos S-2 (kappa = 89,99) fueron mejores que las obtenidas con los datos S-1 (kappa = 75,78). La eficacia de la clasificación aumentó al combinar los datos de las misiones S-1 y S-2 (kappa= 93,07). Los resultados obtenidos en este trabajo sugieren que la banda infrarroja de onda corta, la polarización VH de los datos del SAR y el índice Cellulose absorption index (CAI) y Hall Cover fueron las variables más importantes en la cartografía de la cubierta terrestre del bioma del Cerrado.O mapeamento da cobertura da terra é de suma importância para o monitoramento ambiental e gestão territorial. Séries temporais de radar de abertura sintética (SAR) do Sentinel-1 (S-1) e o sensor óptico MSI/Sentinel-2 (S-2) fornecem condições favoráveis para o mapeamento da cobertura da terra devido suas resoluções espectrais, espaciais e temporais. Este trabalho parte do pressuposto que a combinação entre as séries temporais do S-1 e S-2 permite maior exatidão no mapeamento da cobertura da terra no bioma Cerrado. As imagens foram classificadas utilizando o algoritmo Random Forest na plataforma de processamento em nuvem Google Earth Engine. As classificações obtidas apenas com os dados S-2 (kappa = 89,99) foram melhores do que as obtidas com os dados S-1 (kappa = 75,78). A eficiência da classificação aumentou ao combinar os dados de ambas as missões S-1 e S-2 (kappa= 93,07). Os resultados obtidos neste trabalho sugerem que a banda do infravermelho de ondas curtas, a polarização VH dos dados SAR e os índices cellulose absorption index (CAI) e o Hall Cover foram as variáveis mais importantes no mapeamento da cobertura da terra do bioma Cerrado

Multitemporal optical and radar metrics for wetland mapping at national level in Albania

Wetlands are highly dynamic, with many natural and anthropogenic drivers causing seasonal, periodic or permanent changes in their structure and composition. Thus, it is necessary to use time series of images for accurate classifications and monitoring. We used all available Sentinel-1 and Sentinel-2 images to produce a national wetlands map for Albania. We derived different indices and temporal metrics and investigated their impacts and synergies in terms of mapping accuracy. Best results were achieved when combining Sentinel-1 with Sentinel-2 and its derived indices. We reduced systematic errors and increased the thematic resolution using morphometric characteristics and knowledge-based rules, achieving an overall accuracy of 82%. Results were also validated against field inventories. This methodology can be reproducible to other countries and can be made operational for an integrated planning that considers the food, water, and energy nexus

Forest Species Mapping using Sentinel 2A images for the Central Alentejo Region (Portugal)

In past years, studies about Land Use and Land Cover (LULC) have been approached extensively in remote sensing for providing information on the environmental and global changes in the landscape. In the forest species mapping, one of the major challenges when using Sentinel-2 (S2A) multispectral data is to delineate and discriminate areas of heterogeneous forest components with spectral similarity at the canopy level. In this context, the main objective of this study was to evaluate the S2A data performance for LULC mapping, using a Random Forest classifier (RF). A set of 26 independent variables derived from the 2019 summer period S2A data, with a spatial resolution of 10 m, was used. A total of eight object-based LULC classes were created, four forest classes (Quercus suber, Quercus rotundifólia, Eucalyptus sp, and Pinus pinea) and four other uses. For this propose supervised classification method was applied using the RF classifier. The cartography accuracy assessment was performed using the statistics confusion matrix and Kappa coefficient (k). This study showed that the RF classifier achieved high overall accuracy (92%) and Kappa (91%) for the four forest classes defined using S2A data

Mapeamento das áreas inundáveis do Médio São Francisco utilizando técnicas de processamento digital de imagens de sensoriamento remoto e modelo HAND

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2019.As áreas inundáveis desempenham funções ecológicas primordiais para a manutenção do equilíbrio ecológico dos ecossistemas aquáticos e terrestres. Ademais as áreas inundáveis são fundamentais para o sustento de diversas atividades humanas. No entanto, essas áreas vêm sofrendo diversos distúrbios decorrentes das ações antrópicas. O presente trabalho visou realizar a análise da dinâmica fluvial em um trecho do rio São Francisco, localizado entre os municípios de Barra, Pilão Arcado e Xique-Xique, Bahia. Dessa forma, foi calculada a Linha Média das Enchentes Ordinárias (LMEO) e aplicadas técnicas de processamento digital nas imagens Landsat-8/OLI-TIRS e Sentinel-1 (SAR). Os índices espectrais MNDWI, NDWI e AWEI foram aplicados em duas imagens Landsat-8, uma representando a cota do rio próxima à LMEO e a outra um período de seca. A detecção dos alvos de água nas imagens foi feita a partir da técnica de threshould. O índice MNDWI demonstrou maior valor de acurácia, com índice kappa superior a 0,9. Também foi realizada uma análise multitemporal da dinâmica fluvial entre os anos de 2005 e 2019, empregando imagens Landsat 5 e Landsat 8. Em seguida, foram obtidas duas imagens Sentinel-1 representando a cota máxima e mínima do rio, entre os anos 2016 e 2017. Aplicou-se a técnica de threshould para a classificação da água nas imagens. O maior valor de acurácia demonstrado pelo índice kappa nas imagens Sentinel-1 foi 0,47. Além disso, foi gerado o modelo digital HAND da região e delimitada os terrenos marginais, a fim de realizar o levantamento das áreas inundáveis. Por último, foram realizadas simulações de cotas do rio no modelo HAND, as quais demonstraram valor de acurácia superior a 96,67%.Wetlands play a key role in ecological balance process of aquatic and terrestrial ecosystems. In addition, wetlands are crucial because support various human activities. However, anthropogenic actions have impacted these areas. The objective of the present study was to map the wetlands in a section of São Francisco River, using radar (SAR) and optical image processing. The study area is located between the counties of Barra, Pilão Arcado and Xique-Xique, Bahia. Were employed Sentinel-1, Landsat-8/OLI and Landsat-5/TM images. A HAND model was also generated from DEM to map the wetlands. Data from the São Francisco historical series were used to calculate the Limit from Ordinary Flood (LFOF). MNDWI, NDWI and AWEI were applied on two Landsat-8 images, one image representing the flood, with river level like LFOF, and the other image representing the driest period. This process was taken to determine which index demonstrated the best result for water detection. We used threshold technique to water extraction. The MNDWI showed the highest accuracy, Kappa index was greater than 0.9. A multitemporal analysis of river dynamics, between 2005 and 2019, was also performed, using Landsat images. Two Sentinel-1 images, representing the maximum and minimum level of the river, between 2016 and 2017, were obtained. Threshold technique was applied in Sentinel-1 images for open water extraction. The highest accuracy demonstrated by Kappa on Sentinel-1 images was 0.47. River simulations were performed in HAND model, which presented an accuracy higher than 96.67%

Distribución de humedales en la República Argentina

Los humedales, que representan aproximadamente el 7% de la superficie de la tierra (Ramsar, 2018) se encuentran entre los ecosistemas más valiosos no sólo en términos socioeconómico-productivos, sino también ambientales, dada su importancia en la provisión de servicios ecosistémicos y biodiversidad. La extensión de humedales naturales viene disminuyendo en todo el mundo y cada vez con mayor celeridad. El crecimiento poblacional previsto para las próximas décadas generará un incremento en la demanda de servicios ecosistémicos provenientes de esto socio-agroecosistemas, con lo cual el maximizar oportunidades productivas y al mismo tiempo minimizar potenciales impactos ambientales y sociales negativos, constituye el gran desafío para los tomadores de decisión. Esto requiere del análisis, aplicación y evaluación de herramientas y tecnologías que contribuyan a la gestión sostenible de los humedales. En los últimos años, el incremento de flujos de datos satelitales de libre acceso, el surgimiento de la computación en la nube y el creciente uso de algoritmos de aprendizaje automatizado han facilitado la integración y procesamiento de grandes volúmenes de datos permitiendo acortar tiempos y mejorar productos cartográficos. En Argentina son escasos los trabajos de abordaje a nivel nacional en relación a la ubicación de los humedales en el territorio nacional. El objetivo de nuestro trabajo fue, por un lado, generar un mapa actualizado de la probabilidad de ocurrencia y distribución de humedales en todo el país a partir de un análisis multitemporal de 20 años de imágenes satelitales, así como desarrollar un marco metodológico que permita establecer dicha ocurrencia en todo el territorio nacional, a través de una plataforma de fácil acceso y de fuente abierta. Para esto, se procesó 26.000 sitios de entrenamiento, 54720 imágenes satelitales de los sensores Landsat 5 y 8 y el Modelo Digital de Elevaciones de Argentina (MDE_Ar), a partir de los cuales se derivaron 43 variables predictoras, se aplicó el algoritmo Random Forest (RF) dentro del Google Earth Engine (GEE). El mapa resultante presenta una muy buena precisión en términos de análisis de campo, visual y estadístico, teniendo en cuenta la gran superficie del país (2,78 millones km2). La exactitud global de la determinación fue del 89%, mientras que la exactitud del productor y usuario (especificidad y precisión, respectivamente) para la clase humedal fue, en ambos casos, del 94%. Basándonos en el mapa final de determinación, estimamos que el 9,5% (265.200 km2) de Argentina está cubierto por humedales.Fil: Navarro, Marí­a Fabiana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Navarro, Carlos S. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista. ArgentinaFil: Barrios, Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes, ArgentinaFil: Dieta, Victorio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Delta del Paraná. Agencia De Extensión Rural Delta Frontal; ArgentinaFil: Garcia Martinez, Guillermo Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Esquel; ArgentinaFil: Iturralde, Rosario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Olavarría; Argentina.Fil: Iturralde Ortegui, María del Rosario. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Olavarría; Argentina.Fil: Kurtz, Ditmar Bernardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; Argentina.Fil: Michard, Nicole Jacqueline. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Paredes, Paula Natalia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Saucedo, Griselda Isabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Alday, Silvina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.Fil: Cianfagna, Francisco. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas, Departamento de Hidrología; ArgentinaFil: Curcio, Matías Hernán. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agroforestal Esquel; ArgentinaFil: Enriquez, Andrea Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área Recursos Naturales. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Lopez, Astor Emilio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Sáenz Peña; ArgentinaFil: Miranda, Federico Waldemar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria El Colorado. Agencia de Extensión Rural Formosa; ArgentinaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Umaña, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Laboratorio de Teledetección; ArgentinaFil: Vidal, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Reconquista; Argentina.Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Gavier Pizarro, Gregorio Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; ArgentinaFil: Calamari, Noelia Cecilia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentin

Satellite Earth observation to support sustainable rural development

Traditional survey and census data are not sufficient for measuring poverty and progress towards achieving the Sustainable Development Goals (SDGs). Satellite Earth Observation (EO) is a novel data source that has considerable potential to augment data for sustainable rural development. To realise the full potential of EO data as a proxy for socioeconomic conditions, end-users – both expert and non-expert – must be able to make the right decisions about what data to use and how to use it. In this review, we present an outline of what needs to be done to operationalise, and increase confidence in, EO data for sustainable rural development and monitoring the socioeconomic targets of the SDGs. We find that most approaches developed so far operate at a single spatial scale, for a single point in time, and proxy only one socioeconomic metric. Moreover, research has been geographically focused across three main regions: West Africa, East Africa, and the Indian Subcontinent, which underscores a need to conduct research into the utility of EO for monitoring poverty across more regions, to identify transferable EO proxies and methods. A variety of data from different EO platforms have been integrated into such analyses, with Landsat and MODIS datasets proving to be the most utilised to-date. Meanwhile, there is an apparent underutilisation of fusion capabilities with disparate datasets, in terms of (i) other EO datasets such as RADAR data, and (ii) non-traditional datasets such as geospatial population layers. We identify five key areas requiring further development to encourage operational uptake of EO for proxying socioeconomic conditions and conclude by linking these with the technical and implementational challenges identified across the review to make explicit recommendations. This review contributes towards developing transparent data systems to assemble the high-quality data required to monitor socioeconomic conditions across rural spaces at fine temporal and spatial scales

Seasonal monitoring of biochemical variables in natural rangelands using Sentinel-1 and Sentinel-2 data

Rangelands are natural ecosystems that serve as essential sources of forage for domesticated livestock and wildlife. Therefore, accurately mapping nutrient levels in rangelands is crucial for sustainable development and effective management of grazing animals. Remote sensing tools offer a reliable means to explore nutrient concentrations across large spatial areas. This study aimed to estimate and map seasonal foliar concentrations of nitrogen (N), phosphorus, and neutral detergent fibre (NDF) in mesic tropical rangelands of Limpopo using Sentinel-1, Sentinel-2, and the integration of S1 and S2 data. Fieldwork was conducted to collect samples for seasonal foliar nutrients (N, P, and NDF) during early-summer (November-January 2020), winter (July-August 2021), and late-summer (February-March 2022). Various conventional and red-edge-based vegetation indices were computed. The results demonstrate that integration data from S1 and S2 can effectively estimate and predict foliar concentrations of N, P, and NDF in mesic rangelands throughout the seasons, achieving R2 values of 0.76, 0.78, and 0.71, with corresponding RMSE values of 0.13, 0.04, and 2.52. Notably, red-edge variables emerged as the most significant parameters for predicting seasonal N, P, and NDF concentrations. Additionally, factors such as season and slope significantly influenced the distribution and occurrence of these foliage nutrients, with higher foliage production observed during late-summer and on steeper slopes. The study concludes that the integration of S1 and S2 data can effectively monitor the seasonal dynamics of biochemical parameters. This finding holds significant implications for policymakers and rangeland users, offering a comprehensive understanding of the intricate variations within rangeland ecosystems. Further research could expand on these findings by applying the knowledge to various datasets, exploring different rangelands, and examining additional ecological factors such as slope altitude to detect foliar fibre biochemicals. Finally, the applications of this research extend beyond individual properties, providing practical tools for sustainable rangeland management and informed decision-making in resource utilization and conservation.http://www.tandfonline.com/loi/tres20hj2024Geography, Geoinformatics and MeteorologyPlant Production and Soil ScienceSDG-15:Life on lan