Rapid oil-palm expansion in Southeast Asia demands accurate, up-to-date information on plantation extent and stand age, yet single-sensor approaches often misclassify mixed tropical vegetation and cannot reconstruct planting histories that underlie yield forecasts and sustainability metrics. We integrated the full 1987–2024 Landsat surface-reflectance archive (38 years) with 2024 Sentinel-2 imagery to build a cloud-native monitoring pipeline in Google Earth Engine (GEE). A Random-Forest (RF) model, trained on stratified field samples, delineated plantation extent from six Sentinel-2 bands, five vegetation/surface indices, three Sentinel-1-derived back-scatter layers, and two terrain variables. Age was retrieved inside the verified extent mask by locating the most recent year in which smoothed NDVI dropped below its 20th percentile while the Bare-Soil Index (BSI) exceeded its 80th percentile, thereby flagging the latest planting or replanting events in the Landsat record. Model skill was assessed with a 30 % hold-out set using overall accuracy, κ, precision, and recall; age estimates were validated against 234 ground plots with MAE and RMSE. The multi-sensor classifier reached an overall accuracy of 90.5 % and Kappa = 0.81, with balanced error rates (oil palm: 88.6 % precision, 92.7 % recall; non-oil palm: 92.5 % precision, 88.4 % recall). Mapped plantations covered 561 km², or 60.9 % of the study district. Age retrieval achieved an RMSE of 4.0 yr, revealing distinct replanting events that correspond to known market cycles and providing spatially explicit age strata relevant to yield and carbon-stock modelling. Combining the long Landsat record with high-resolution Sentinel data markedly outperforms single-sensor methods for both extent mapping and age estimation. The workflow relies solely on free, globally available imagery and scalable cloud computing, making it immediately transferable to government and NGO monitoring programmes. By delivering accurate maps of plantation area and stand age, the approach fills a critical information gap for sustainable palm-oil certification, carbon accounting, and land-use policy across the humid tropics.

Ili Lewotolo Volcano, a stratovolcano in East Nusa Tenggara, Indonesia, erupted on November 27, 2020, following eight years of dormancy, releasing significant ash and sulfur dioxide (SO₂) emissions. This study is the first to combine multi-temporal Interferometric Synthetic Aperture Radar (InSAR) and SO₂ imaging for monitoring volcanic activity, offering a comprehensive pre- and post-eruption analysis approach. The New Small Baseline Subset (NSBAS)-InSAR method, using Sentinel-1 SAR data (from November 2019 to November 2020), detected notable ground deformation, while the Tropospheric Monitoring Instrument (TROPOMI) sensor on Sentinel-5P (from January 2020 to December 2021) captured fluctuations in SO₂ emissions. InSAR time-series analysis revealed significant ground uplift, indicating a magma source around 2000 m depth, confirmed through parametric optimization using the Mogi model. Furthermore, SO₂ levels surged by 512 % during the eruption compared to pre- and post-eruption periods, demonstrating a clear correlation between ground deformation and gas emissions. This combined approach provides deeper insights into volcanic behaviour and underscores the value of combining remote sensing techniques to enhance hazard mitigation strategies, paving the way for an effective early warning system for monitoring volcanic activity. • First study combining NSBAS InSAR & Sentinel-5P for Ili Lewotolo volcano dynamics, showing potential for global application. • NSBAS InSAR shows a 35 mm/ year ground deformation produced by over-pressurized magma at around 2000 m depth. • SO2 emissions peaked at 1.4 DU post-eruption, declining below 0.4 DU by 2021's end.

Information on tree plantation structures, such as tree type, density, and tree height, is essential for developing smart agriculture and plantation management strategies to support production estimation and investment, including biomass for carbon sequestration estimation. In this study, multisource remote sensing data from radar (Sentinel-1 C band), optical (Pléiades), and drones (multispectral drone) were used to support effective and cost-efficient sustainable tree plantation management in Siak Regency, Riau Province, Indonesia. Tree plantation maps were created using the difference backscatter VH and VV from Sentinel-1. Tree counting was then performed using Pléiades red, green, and blue visible bands and multispectral drone bands using a maximum a posteriori pixel-based classifier integrated with a filter function and statistical estimation. The validation of the tree map using manual measurements yielded accuracies ranging from approximately 79% to 97%. Tree heights were calculated from the difference between the Digital Surface Model (DSM) derived from drone data and the Digital Terrain Model (DTM) obtained from DEM Nasional (DEMNAS) data. Further improvements in the current map accuracy can be achieved using a combination of remote sensing and field measurements of tree structure inventories. • Tree plantations map, tree structure and tree height inventory produced using integrated SAR, optical imaging and UAV • Tree counting developed using The Maximum A Posteriori (MAP) classifier of high-resolution satellite image and drone • Tree height developed using drone DSM and DTM data

This article introduces an improved method for estimating Soil Organic Carbon (SOC) using Sentinel 2 images, with a specific emphasis on the Dakshina Kannada area in India. By examining 364 soil samples, SOC estimation models were constructed using Random forests (RF) and Partial Least Squares Regression (PLSR), focusing on the impact of nearby vegetation pixels. The approach consisted of classifying soil samples by the presence of plant pixels at distances of 0, 10, and 20 m, and evaluating the influence of dry vegetation by the use of the Normalised Burn Ratio 2 (NBR2). The findings demonstrated a significant improvement in the precision of the model (by up to 20 %) when vegetation pixels within a 20-meter radius of the sample locations were omitted. The research also included a temporal analysis utilizing Sentinel-2 images from April 2017 to May 2023. This analysis showed strong relationships between the amount of exposed soil and the accuracy of predicting soil organic carbon (SOC) levels. These results emphasize the need to take into account both the spatial dynamics of vegetation and the temporal variations in bare soil covering to get an accurate estimate of soil organic carbon (SOC). This study improves the accuracy and dependability of SOC evaluations by including geographical and temporal aspects, providing useful insights for agricultural and ecological applications.

Dans le cadre d’une meilleure gestion de son territoire, le Gabon a mis en place un Programme d’Affectation des Terres (PNAT), qui vise à mieux gérer les différentes ressources que compte le pays. Mais cette gestion nécessite le règlement de plusieurs problèmes saillants. Ainsi, l’implication de plusieurs parties-prenantes dans ce secteur entraine une forte production des données géographiques hétérogènes qui se superposent les unes aux autres. Cette situation entraine des conflits de compétence entre administrations en matière d’affectation des terres. Face à cette problématique et aux limites des modèles de représentation des objets géographiques existants, nous avons jugé nécessaire de proposer une approche spécifique de modélisation qui réponde aux exigences de l’affectation des terres au Gabon. Par ailleurs, l’imbrication des zones destinées à des activités incompatibles conduit à des conflits fonciers difficiles à régler. En outre, la représentation incomplète des objets géographiques sur le terrain et une représentation spatiale ponctuelle des objets surfaciques de type communes et villages sont autant de difficultés à lever ; il en est de même de la menace sur les espèces végétales et animales protégées et la prise en compte dans la gestion, de l’évolution temporelle des activités des acteurs sur le territoire Gabonais. Afin de répondre à l’essentiel des problèmes ainsi listés, nous proposons un cadre théorique original à travers lequel nous avons premièrement étendu le modèle jaune d’œuf ( « egg–yolk » ) — imposé de fait par une décision gouvernementale — avec une fonction mathématique appelée fonction d’appartenance, permettant de déterminer le degré d’appartenance d’un objet quelconque A par rapport à un autre objet B. Cette fonction a pour intérêt, à la fois, de prendre en compte la variabilité du flou des objets géographiques manipulés dans le processus d’affectation des terres au Gabon, et d’étendre un objet géographique au-delà de ses limites connues en lui agrégeant des objets satellites. Deuxièmement, nous avons proposé la modélisation des relations topologiques binaires entre des régions spatiales aux limites fixes et celles aux limites floues sur la base du modèle à 9 intersections. Il a s’agit de croiser toutes les situations possibles en matière d’affectation des Terres au Gabon. A la suite de cela, nous avons abouti à un ensemble de relations admises que nous appelons pouvoir d’expression topologique. De ce pouvoir, nous avons déduit plusieurs définitions. Enfin, nous avons proposé un diagramme de classes qui cadre avec le processus d’affectation des terres au Gabon. L’objectif de ce travail est de proposer à terme, une solution qui permettra de régler les litiges qui pourraient en résulter.