A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis.

Conservation Status of Forest Types Vary Greatly in Myanmar - the Most Forested Country in the Indo-burma Biodiversity Hotspot

Diverse forests with distinct forest types, harbor exceptional biodiversity and provide many ecosystem goods and services, making some forest types more economically valuable and prone to exploitation than others. The high rates of deforestation in Southeast Asia endanger the existence of such vulnerable forest types. Myanmar, the region’s largest forest frontier provides a last opportunity to conserve these vulnerable forest types. However, the exact distribution and spatial extent of Myanmar’s forest types has not been well characterized. To address this research gap, we developed a national scale Forest Type map of Myanmar at 20m resolution, using moderate resolution, multi-sensor satellite images (Sentinel-1, Sentinel-2 and ALOS-PALSAR), extensive field data, and a machine learning model (RandomForest). We mapped nine major forest types and developed a Conservation Status Score to evaluate the conservation status of the mapped forest types. Swamp, Mangrove, Dry Deciduous, Lowland Evergreen and Thorn forests were ranked as the five least conserved forest types. We also identified the largest remaining patch for each of the five least conserved forest types and determined their protection status to inform future forest conservation policy. In most cases, these patches lay outside protected areas indicating areas that may be prioritized for future conservation.

Free Global DEMs and Flood Modelling—A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique)

Flood hazard and risk analysis in developing countries is a difficult task due to the absence or scarce availability of flow data and digital elevation models (DEMs) with the necessary quality. Up to eight DEMs (ALOS Palsar, Aster GDEM, Bare Earth DEM, SRTM DEM, Merit DEM, TanDEM-X DEM, NASA DEM, and Copernicus DEM) of different data acquisition, spatial resolution, and data processing were used to reconstruct the January 2015 flood event. The systematic flow rate record from the Mocuba city gauge station as well as international aid organisms and field data were used to define both the return period peak flows in years for different flood frequencies (Tyear) and the January 2015 flooding event peak flow. Both visual and statistical analysis of flow depth values at control point locations give us a measure of the different hydraulic modelling performance. The results related to the Copernicus DEM, both in visual and statistical approach, show a clear improvement over the results of the other free global DEMs. Under the assumption that Copernicus DEM provides the best results, a flood hazard analysis was carried out, its results being in agreement with previous data of the effects of the January 2015 flooding event in the Mocuba District. All these results highlight the step forward that Copernicus DEM represents for flood hazard analysis in developing countries, along with the use of so-called “citizen science” in the form of flooding evidence field data acquisition.

Retrieval of DTM under Complex Forest Stand Based on Spaceborne LiDAR Fusion Photon Correction

The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) is limited. Therefore, this paper proposes a photon correction data processing method based on ICESat-2 to improve the DTM inversion accuracy in complex terrain and high forest coverage areas. The correction value is first extracted based on the ALOS PALSAR DEM reference data to correct the cross-track photon data of ICESat-2. The slope filter threshold is then selected from the reference data, and the extracted possible ground photons are slope filtered to obtain accurate ground photons. Finally, the impacts of cross-track photon and slope filtering on fine ground extraction from the ICESat-2 data are discussed. The results show that the proposed photon correction and slope filtering algorithms help to improve the extraction accuracy of forest DTM in complex terrain areas. Compared with the forest DTM extracted without the photon correction and slope filtering methods, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are reduced by 51.90~57.82% and 49.37~53.55%, respectively. To the best of our knowledge, this is the first study demonstrating that photon correction can improve the terrain inversion ability of ICESat-2, while providing a novel method for ground extraction based on ICESat-2 data. It provides a theoretical basis for the accurate inversion of canopy parameters for ICESat-2.

ANÁLISE ESPACIAL DA VEGETAÇÃO NATIVA EM ÁREAS DE PRESERVAÇÃO PERMANENTE E DE RESERVA LEGAL E SUAS IMPLICAÇÕES LEGAIS, NA BACIA DO RIO BURANHÉM, NOS ESTADOS DE MINAS GERAIS E BAHIA

A legislação ambiental brasileira estabelece restrições de uso em determinadas áreas nos imóveis rurais, no intuito de proteger espaços relevantes para a conservação das espécies e dos recursos naturais. Neste aspecto, o geoprocessamento vem se tornando uma ferramenta essencial no monitoramento e fiscalização em áreas cuja vegetação nativa deve ser mantida. O objetivo deste estudo consistiu em identificar as Áreas de Preservação Permanente (APPs) e de Reserva Legal (RL) situadas na bacia do rio Buranhém, nos estados de Minas Gerais e Bahia, e verificar se estas áreas atendem ao previsto na legislação brasileira ou se há conflitos de uso da terra. Para o mapeamento da vegetação e das classes de APPs foram utilizadas imagens do satélite Sentinel-2, o modelo digital de elevação (MDE) do ALOS PALSAR, e o programa gratuito QGIS. Foi constatado que em 74,7% das APPs e 41,8% das áreas de Reserva Legal não possuem cobertura vegetal predominantemente nativa, o que caracteriza um conflito legal de uso dessas áreas. Portanto, para que as propriedades rurais se regularizem é necessário a recuperação destas áreas que não estão em conformidade com a legislação vigente.

Complémentarité des images optiques SENTINEL-2 avec les images radar SENTINEL-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique.

Dans cet article, nous évaluons les performances de classification de trois algorithmes non paramétriques (kNN, RF et SVM) en utilisant les données multi-temporelles de trois satellites (Sentinel-1, Alos-Palsar-2 et Sentinel-2) et de leurs combinaisons. La zone d'étude choisie se caractérise par un climat méditerranéen subhumide et une topographie très accidentée qui rend la classification d’occupation du sol particulièrement difficile. En outre, elle contient une aire protégée nommée Jbel Moussa et présente une diversité biologique exceptionnelle. Afin de suivre le couvert végétal de cette dernière, nous avons acquis et prétraités les images satellitaires optiques et radar pour la période du 1er janvier au 31 décembre 2017. Ensuite, nous avons combiné les trois satellites, soit douze scénarios produits. Des cartes de classifications illustrent notre approche. Un total de trente-six classifications a été obtenu, en se basant sur sept classes : eau, bâtiment et infrastructures, sol nu, végétation peu dense, prairies, forêt peu dense et forêt dense. Les résultats ont montré que pour tous les scénarios, la précision globale la plus élevée a été produite par RF (53,03%-93,06%), suivie de kNN (49,16%-89,63%), tandis que SVM (47,86%-86,08%) a produit la précision de classification la plus faible. L'étude a également montré une similitude entre les performances de la combinaison des trois satellites et celles de Sentinel-2 seul. Les estimations de la superficie pour les différentes classes vont de 0,85 km2 (0,11% de la zone d'étude) à 326,84 km2 (41,31% de la zone d'étude)

Sinergia de imágenes Sentinel 1 y Sentinel 2 A para la delimitación de humedales a partir de un enfoque ecohidrogeomórfico

En los últimos años se desarrolla un creciente interés en detectar, delimitar e inventariar humedales bajo un enfoque ecohidrogeomórfico para ampliar el conocimiento de estos ecosistemas y posibilitar la elaboración de lineamientos de gestión para su aprovechamiento. El objetivo de esta investigación es detectar y delimitar aquellos sectores que reflejan condiciones ecohidrogeomórficas favorables para definirlos como humedales en un sector de la cuenca Alsina ubicada en el sudoeste de la provincia de Buenos Aires, Argentina. Se aplicó una metodología complementaria entre imágenes ópticas y radar que consiste en la utilización de un modelo digital de elevación Alos Palsar e imágenes Sentinel 2 A y Sentinel 1. Se abordó un pulso de inundación en la laguna ocurrido en el año 2017. Los eventos húmedos reflejan el funcionamiento de la laguna y los sistemas de humedales. Se analizaron cambios biofísicos que ocurrieron en los sectores perilagunares en referencia al ingreso de lluvias. Los resultados verificaron la presencia de diferentes tipologías de humedales entre la laguna y su área perilagunar que durante una inundación funcionan como un ecosistema híbrido.

THE LATEST DTM USING INSAR FOR DYNAMICS DETECTION OF SEMANGKO FAULT-INDONESIA

The latest Digital Terrain Model (DTM) is seen as an upgradable DTM that is fitted to the latest combination of DTM master and its displacement. The latest DTM can be used to overcome the problem of static DTM weaknesses in displaying the latest topographic changes. DTM masters are obtained from InSAR and Digital Surface Model (DSM) ALOS PALSAR conversions. Meanwhile, the displacement is obtained from Sentinel-1 images, which can be updated every 6–12 days or at least every month. ALOS PALSAR data were the images acquired in 2008 and 2017, while Sentinel-1 data used were images acquired in 2018 and 2020. This study aims to reveal the importance of an upgradable DTM so called latest DTM which is combination of DTM master and its displacement in order to show the latest condition of study area. The case study is the dynamics analyze of the Semangko fault specifically in the Sianok and Sumani segments situated in Indonesia. The vertical accuracy assessment was done to evaluate the DSM to DTM conversion with a tolerance of 1.96σ. The result obtained is the latest DTM. It is derived from the integration of the DTM master with displacement. The latest DTM can be used to detect the dynamics of Semangko fault. The study area has vertical deformation at a value of –50 cm to 30 cm. The Semangko fault area is dominated by –25 to 5 cm deformation. In general, this region has decreased. The decline in this region ranges from 7.5 cm to 10 cm per year. The latest DTM vertical accuracy is 2.158 m (95% confidence level) with a scale of 1: 10,000 to 1: 20,000.

Análise de risco da barragem de minério de ferro na Serra Norte no Pará

A mineração é um dos setores responsáveis pelo uso do solo e ocupação do território brasileiro, tem sido essencial para o crescimento econômico do país. O principal crescimento da Região Norte, no estado do Pará, localiza -se na Serra dos Carajás, onde situa – se uma das maiores jazidas de minério de ferro puro do mundo. Por isso é importante a análise de um futuro impacto socioambiental em caso de rompimento da barragem que recebe o estéril da produção de ferro, por meio do fluxo dos rejeitos em um raio de 50 km da área afetada, utilizando como base às imagens do sensor ALOS PALSAR, com resolução de 12,5 metros. Ao analisar, verifica – se que em caso de rompimento a área afetada primeiramente será Parauapebas devido sua localização, logo após as cidades de Marabá e a região de Canaã, conforme os tipos de elevação demonstrados no mapa, ocasionando impactos ambientais, sociais e até mesmo econômicos, tais efeitos que podem ser irreversíveis e de difícil gestão. De tal modo, para mitigar e prevenir os danos, deve- se fortalecer a legislação através de melhores práticas de governança, programas de monitoramento estruturados, rigorosidade nas leis ambientais, fiscalizações de órgãos ambientais competentes as atividades de barragens e liberação de licenças ambientais.