Remote Sensing Applications-Society and Environment最新文献

{"title":"A metaheuristic design for hyperparameter tuning of XGBoost: Towards shaping an impeccable strategy for predictive modeling of mineral prospectivity","authors":"Saeid Esmaeiloghli ,&nbsp;Mahyar Yousefi ,&nbsp;Emmanuel John M. Carranza","doi":"10.1016/j.rsase.2026.101980","DOIUrl":"10.1016/j.rsase.2026.101980","url":null,"abstract":"<div><div>Over the past two decades, machine learning (ML) has been an avant-garde technology for data-driven mineral prospectivity mapping (MPM) due to its competence in modeling non-linear relationships embedded in multi-source geoscience datasets. XGBoost is a flagship algorithm in boosting-based ML that has gained popularity in MPM for its proficiency in speeding up the training process, enhancing prediction accuracy, reducing the risk of overfitting, and improving generalizability. However, achieving a well-trained XGBoost model usually entails careful tuning of multiple hyperparameters, i.e., <em>n_estimators</em>, <em>max_depth</em>, <em>min_child_weight</em>, <em>gamma</em>, <em>subsample</em>, <em>colsample_bytree</em>, and <em>learning_rate</em>. Motivated by this challenge, we conceptualized a computational framework in this paper that employs particle swarm optimization (PSO) to discover optimal XGBoost-related hyperparameters that yield MPM predictions with the highest accuracy. The PSO algorithm was engineered using a training dataset and a five-fold cross-validation strategy to find a hyperparameter setting that is globally optimal for achieving an XGBoost model with a robust performance. The PSO‒XGBoost model was coded and implemented by scripting over functions developed within the R programming language. The potential application of the proposed hybrid model was demonstrated through a real-case experiment for predictive modeling of porphyry-type copper prospectivity in the Baft-Sarduiyeh district, southern Iran. A comparative analysis between the PSO‒XGBoost model and manually tuned XGBoost scenarios revealed the superiority of the former by delivering higher values for confusion matrix-derived evaluation metrics and creating a higher-performance curve during receiver operating characteristic analysis. The findings suggest that tuning XGBoost with PSO-optimized hyperparameters can significantly improve predictive power and promote interpretability for more accurate modeling of mineral prospectivity.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101980"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147613480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationships between spectral and biological diversity depend on season and habitat type","authors":"Soroor Rahmanian ,&nbsp;Nico Eisenhauer ,&nbsp;Antonia Ludwig ,&nbsp;Yuanyuan Huang ,&nbsp;Hannes Feilhauer","doi":"10.1016/j.rsase.2026.102039","DOIUrl":"10.1016/j.rsase.2026.102039","url":null,"abstract":"<div><div>Remote sensing is increasingly used to monitor biodiversity, with spectral diversity—pixel-to-pixel variation in spectral reflectance—serving as a key proxy for taxonomic and functional diversity. However, seasonal dynamics and ecological drivers underlying spectral–biological diversity relationships remain less understood. This study examines seasonal patterns across three temperate open habitats in Germany—a nutrient-poor grassland, wet heathland, and floodplain meadow. We monitored 130 1 m² plots over three seasons, measuring taxonomic diversity (Shannon, Simpson, inverse Simpson, Pielou's evenness, and species richness), functional diversity (functional dispersion, richness, evenness, divergence, Rao's Q), and four spectral diversity indices (average angle dissimilarity, coefficient of variation of whole spectra and optical traits, RaoQ) across narrow 166 wavelength regions, along with vegetation parameters. Data were collected on six to seven dates during the growing season using a field spectrometer to capture seasonal and trait variation. We employed linear mixed-effects and structural equation models to evaluate how spectral diversity reflects biodiversity over time and across habitats. Results suggested that these relationships vary across habitats and seasons. Vegetation structure — especially non-photosynthetic vegetation (NPV; senescent/litter) and canopy height — are linked to spectral and biological diversity through context-dependent pathways that vary across habitats and seasons. NPV was positively associated with spectral diversity in the grassland and floodplain, whereas canopy height showed temporally variable effects, enhancing functional diversity mid-season but exhibiting weaker or negative relationships in the heathland. These findings highlight that links between spectral and biological diversity are context dependent and vary with vegetation structure and season.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102039"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A weighted ensemble DL framework for granular RPV inventory and energy yield analysis in Pakistan","authors":"Muhammad Kamran Lodhi ,&nbsp;Yumin Tan ,&nbsp;Yang Li ,&nbsp;Faisal Mumtaz ,&nbsp;Shahid Naeem","doi":"10.1016/j.rsase.2026.102032","DOIUrl":"10.1016/j.rsase.2026.102032","url":null,"abstract":"<div><div>The global imperative to triple renewable energy by 2030 underscores the vital role of Rooftop Photovoltaics (RPV). Despite high solar irradiation, Pakistan's RPV potential remains underexplored due to the poor generalizability of conventional Deep Learning (DL) models across complex urban architectures. This study addresses this limitation by proposing a weighted ensemble DL framework for large-scale RPV delineation in Islamabad, Lahore, and Karachi. Multiple DL models were trained on region-specific, high-resolution Google Satellite imagery (0.3 m spatial resolution), with final predictions synthesized through a performance-based weighted majority voting scheme. For solar resource assessment, a 10 m Digital Surface Model (DSM) was generated from Sentinel-1 data to account for urban morphology. The ensemble model demonstrated superior robustness, achieving an F1-score of 0.92, 0.96 accuracy, and a Matthews Correlation Coefficient of 0.89. Applying this framework, we identified 21,586 installations in Islamabad, 65,304 in Lahore, and 35,710 in Karachi, representing the most granular assessment to date. Analysis of annual electricity yield and carbon mitigation revealed that Karachi leads with 602 GWh (0.37 Mt CO₂ reduction), followed by Lahore at 373 GWh (0.23 Mt CO₂), and Islamabad at 141 GWh (0.09 Mt CO₂). Total annual generation from identified modules reached 1117.94 GWh, offsetting 0.693 Mt CO₂. Ground validation in Lahore confirmed high model reliability (R² = 0.98, MAPE = 6.02%). This research provides a scalable, data-driven methodology for RPV mapping in diverse developing regions, offering essential intelligence for policymakers to accelerate Pakistan's transition to a sustainable energy future.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102032"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond trial-and-error: Geometrically balanced, multi-objective optimization for segmenting remote sensing data over complex environments","authors":"Mohammad Javad Soltani ,&nbsp;Hooman Latifi ,&nbsp;Hamed Naghavi","doi":"10.1016/j.rsase.2026.102050","DOIUrl":"10.1016/j.rsase.2026.102050","url":null,"abstract":"<div><div>Conventional segmentation of remote sensing data mostly relies on trial-and-error, producing inconsistent over-/under-segmentations (OS/US) without thorough evaluation of geometric and thematic metrics. This challenge is particularly critical for the quality assessment of medium-resolution satellite image segmentation, and especially in complex and heterogenous environments like wetlands. Unsupervised methods often yield lower accuracy at high computational cost, while optimizing segmentation via single-objective techniques exacerbates OS/US trade-offs. Here, we propose a supervised multi-objective framework with hybrid labeling technique, leveraging Grey Wolf Optimization (GWO) to automate geometrically balanced segmentation by introducing an OS metric. The clustering-based segmentation was implemented on Sentinel-2 imagery. Following feature selection for dimensionality reduction, the selected features were optimized using the GWO with a novel Over-Segmentation Factor (OSF), Intersection over Union (IoU), weighted IoU, and a staged F1-score (F1-seg) as objectives. A hybrid labeling approach was developed to account for wetland seasonality dynamics. The inclusive evaluation framework integrated 1) thematic accuracy assessment, 2) Area- and Location-Based (AB/LB) geometric analysis, and 3) validating optimization trend. We found that labeled optimization achieved minimal OSF (0.088) and optimal F1-seg (0.442), while unlabeled data improved performance in both IoU (0.504) and weighted IoU (0.710). This indicates nearly equivalent performance (Labeled: F1 = 0.627, OSF = 0.175; Unlabeled: F1 = 0.625, OSF = 0.138) that can be achieved without labeling. The single-objective optimization based on Modified Euclidean Distance 2 (MED2) comparatively achieved the lowest LB error, performing almost three times better than alternative methods. Crucially, OSF strongly correlated with segment counts (R² = 0.99, p &lt; 0.000001, and multi-objective optimization successfully balanced OS/US, outperforming single-objective methods. This framework advances segmentation precision for medium-resolution spatiotemporal analysis, pending cross-site validation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102050"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147957108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Satellite-based soil texture mapping using GLOBAL–LOCAL spectral libraries for flood hazard assessment in arid regions","authors":"Abdel Rahman S. Alsaleh ,&nbsp;Mariam Alcibahy ,&nbsp;Delal E. Al Momani ,&nbsp;Hamed Al Hashemi ,&nbsp;Ali A. Al Hammadi ,&nbsp;Khaled AlAwadi ,&nbsp;Lakmal Seneviratne ,&nbsp;Maryam R. Al Shehhi","doi":"10.1016/j.rsase.2026.101997","DOIUrl":"10.1016/j.rsase.2026.101997","url":null,"abstract":"<div><div>Flooding in arid and semi-arid regions is a growing concern due to rapid urban expansion and more frequent extreme rainfall events, yet accurate flood hazard mapping remains difficult because of scarce in situ data and limited high-resolution soil information. To the best of our knowledge, this study represents one of the first demonstrations of satellite-based soil texture mapping via GLOBAL–LOCAL soil spectral library fusion for operational flood hazard assessment in an arid country. The fused model showed strong performance for sand (R² = 0.72, RMSE = 8%) and moderate performance for silt (R² = 0.44, RMSE = 2.8%) and clay (R² = 0.55, RMSE = 5.4%), while the Sentinel-2 land-cover classification used to mask non-soil surfaces achieved 93.75% overall accuracy (κ = 0.90). Combining predicted USDA soil texture classes with elevation, slope, hydrological, and land-cover data yielded a structural flood hazard map that highlights areas of higher susceptibility in low-elevation coastal zones and urban areas underlain by finer-textured soils, rather than predicting event-specific flood extents or water depths. Validation against Landsat 9 observations from a single major flood event in April 2024 showed strong spatial correspondence between high and very-high hazard classes and observed inundation in coastal cities and along major stream networks. By demonstrating the feasibility of transferring laboratory-trained soil spectral models to satellite sensors and embedding them in flood hazard mapping, this study establishes a scalable and transferable methodology applicable to arid and semi-arid regions worldwide where soil data scarcity limits hazard assessment. The resulting flood hazard and road vulnerability maps translate the scientific modelling framework into decision-ready products that support urban zoning regulations, drainage and transport infrastructure design and upgrading, and emergency response planning in rapidly developing arid and semi-arid regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101997"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147657706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing spatial scale effects in multi-sensor fire fuel mapping in the heterogeneous landscapes of Tasmania","authors":"Sophia Hoyer ,&nbsp;Anke Fluhrer ,&nbsp;Florian Hellwig ,&nbsp;Steve Harwin ,&nbsp;Jukka Matthias Krisp ,&nbsp;Thomas Jagdhuber","doi":"10.1016/j.rsase.2026.101996","DOIUrl":"10.1016/j.rsase.2026.101996","url":null,"abstract":"<div><div>Effective fire-risk management in Tasmania requires vegetation maps that capture both broad fuel patterns and small, highly flammable gorse (<em>Ulex europaeus</em>) infestations. Yet gorse often occurs in fragmented patches that disappear in coarser land-cover products, raising uncertainty about how much fuel information is lost when regional maps are produced at moderate or coarse resolution. To clarify these scale effects we compare Object-Based Image Analysis-Random Forest fuel mapping at 0.5, 3 and 10<!--> <!-->m in a heterogeneous Tasmanian agricultural landscape using fused optical, LiDAR and SAR features. Beyond accuracy at each scale, we quantify how classes merge, disappear, or persist between resolutions using transfer matrices and analyse how large a gorse patch must be to remain detectable at coarser scales. F1 scores are consistently high across scales (76%–99%), yet class-level behaviour differs substantially. The 3<!--> <!-->m model achieves the highest gorse classification performance while maintaining geometric coherence of these shrub patches. When transferred from 0.5<!--> <!-->m, 76% of fine-scale gorse area remains represented at 3<!--> <!-->m, compared to only 36.8% at 10<!--> <!-->m. Detection probability at 3<!--> <!-->m increases monotonically with patch size, whereas at 10<!--> <!-->m even large patches (10,000–30,000<!--> <!-->m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) are detected in only 60% of the cases. These results demonstrate that high within-scale accuracy does not guarantee cross-scale persistence of fine-grained fuels. 3<!--> <!-->m resolution provides optimal scale–patch alignment for regional fuel-zone delineation in Tasmania, whereas sub-metre imagery is required for explicit identification of individual gorse infestations. Overall, the results confirm that spatial aggregation disproportionately affects narrow and fragmented vegetation types. Resolution choice is therefore not merely a technical setting, but a decisive factor in whether hazardous fine fuels remain visible in regional fuel assessments.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101996"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147657708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the ICEYE constellation for observing short-term surface displacements induced by volcanic activity","authors":"MinJeong Jo ,&nbsp;Stacey A. Huang ,&nbsp;Jeanne M. Sauber","doi":"10.1016/j.rsase.2026.102003","DOIUrl":"10.1016/j.rsase.2026.102003","url":null,"abstract":"<div><div>ICEYE operates the world’s largest SAR constellation, offering global imaging services with more than 60 X-band SAR satellites launched since its first mission in 2018. The continuous expansion of the ICEYE fleet enables short-repeat observations of the Earth, which is crucial for responding to natural disasters such as floods, earthquakes, and volcanic activities. While ICEYE data have demonstrated capabilities in amplitude-based analysis for Earth science applications, the potential for InSAR analysis as part of the international SAR constellation has only begun to be explored. In particular, global monitoring and rapid response capabilities for short-term volcanic activity benefits immensely from a dense, multitemporal satellite network. Here, we quantitatively assessed the InSAR capability and quality of ICEYE data for observing volcanic activity over two locations: Kı̄lauea volcano (Hawai’i, US) and Reykjanes Peninsula (Iceland). We examined important InSAR quality metrics, namely decorrelation characteristics and accuracy of deformation measurement relative to GPS/GNSS, and then analyzed the feasibility of measuring three-dimensional (3D) displacements. We found that ICEYE data quality was more variable and had higher error relative to GPS/GNSS than other X-band satellites we studied, in particular COSMO-SkyMed (CSK), TerraSAR-X (TSX), and TanDEM-X (TDX). Furthermore, as with many current NewSpace providers, data tasking can be challenging. Still, we were able to demonstrate initial 2D deformation retrieval, which can be expanded to 3D retrieval if multiple geometries can be leveraged. These capabilities can be combined with ICEYE’s very short temporal baselines to enable unprecedented rapid-repeat observations of volcanic activity. Despite the reduced InSAR performance of the ICEYE constellation relative to larger InSAR-capable satellites, the fleet contributes unique capabilities to the global SAR constellation for capturing deformation patterns associated with rapidly evolving volcanic events.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102003"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147657709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced ResNet for lake wetland components classification based on Sentinel-2 composites: A case study of Taihu Lake, eastern China","authors":"Yuxuan Hu,&nbsp;Jingyi Wang,&nbsp;Yatian Xu,&nbsp;Rui Qian,&nbsp;Mingshi Li","doi":"10.1016/j.rsase.2026.102012","DOIUrl":"10.1016/j.rsase.2026.102012","url":null,"abstract":"<div><div>Lake wetland ecosystems perform critical ecological functions such as water purification, biodiversity maintenance, and climate regulation, making accurate and fine lake wetland components classification essential for ecological health assessment and productivity accounting. Although deep convolutional neural networks (CNNs) have demonstrated strong potential in image recognition, their application to fine-grained classification of lake wetlands remains limited due to the complex spectral characteristics created by water, land and vegetation interactions. This study developed an enhanced Multi-level Dual-Attention(MLDA)-ResNet50 deep learning model using multi-temporal Sentinel-2 data to achieve integrated classification of wetland components in Taihu Lake. A median compositing strategy based on phenological windows was implemented to address severe cloud contamination. Sample scarcity issue at local scales was resolved through upsampling, enabling native-resolution pixel-level classification via probability-based sliding window accumulation. Key improvements on the architectural framework of CNN included: embedding CBAM modules within residual blocks to enhance the discriminative power for key feature extraction, Reducing spatial attention kernel to prevent edge distortion in upsampled data., and proposing a multi-level dual-attention feature fusion (MLDA-FF) mechanism to integrate shallow texture features with deep semantic features. Experimental results showed an overall accuracy at 95.6% in the classifications, representing a 4.8% improvement over the baseline ResNet50, with substantially escalated performance in spectrally similar land cover mixtures and small-scale feature areas. This research validates the applicability of CNNs for lake wetland components classification in Taihu Lake, and offers new methodological insights for future studies through its improved classification framework and data preprocessing strategy.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102012"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147657813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Aedes aegypti breeding site detection: A deep learning approach to tire identification in unmanned aerial vehicle imagery from urban Indonesia","authors":"Mohammad Mehedy Hassan ,&nbsp;Andrew J. Chamberlin ,&nbsp;Morgan S. Tarpenning ,&nbsp;Whitney C. Weber ,&nbsp;Kavita Dave Coombe ,&nbsp;Giulio A. De Leo ,&nbsp;Muhammad Junaid ,&nbsp;Andang Suryana Soma ,&nbsp;Ansariadi ,&nbsp;Joelle I. Rosser","doi":"10.1016/j.rsase.2026.102011","DOIUrl":"10.1016/j.rsase.2026.102011","url":null,"abstract":"<div><div>Discarded tires remain a significant public health problem in tropical urban environments, serving as high-risk breeding sites for <em>Aedes aegypti,</em> the mosquitoes which transmit dengue, chikungunya, and Zika viruses. Identifying these habitats using ground-based surveillance methods can be resource-intensive and often misses fenced-off areas and tires on roofs. This study presents a novel approach using unmanned aerial vehicle (UAV) imagery combined with deep learning to automatically detect discarded tires across the Tallo sub-district of Makassar, Indonesia, an area with historically high dengue incidence. We trained and compared U-Net++ and DeepLabV3++ convolutional neural network architectures for tire segmentation. We evaluated the models’ ability to identify the locations, shapes, and spatial distribution of discarded tires in the study area. Both models demonstrated high prediction accuracy, with F1 scores of 0.87 and 0.82, respectively, with modestly superior performance by the U-Net++. The strong detection performance was largely attributable to the distinctive circular morphology and spectral characteristics of tires in aerial imagery, though this same characteristic occasionally led to false positives when encountering tire-mimicking objects such as outdoor air conditioning condenser units, water tank lids, and similar circular items. Identifying, locating, and eliminating potential larval breeding sites is a primary strategy for the control of <em>Aedes aegypti</em> -transmitted viruses. Accurate, rapid detection of high-risk <em>Aedes aegypti</em> breeding sites from UAV imaging is a promising new tool to improve vector control.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102011"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147657815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial impact evaluation of an agricultural intensification program","authors":"Fiona Kastel,&nbsp;Sanchi Lokhande,&nbsp;Devika Lakhote,&nbsp;Anca Dumitrescu,&nbsp;Douglas Glandon","doi":"10.1016/j.rsase.2026.102014","DOIUrl":"10.1016/j.rsase.2026.102014","url":null,"abstract":"<div><div>Climate change is exacerbating food insecurity worldwide, particularly in drought-prone regions, such as the Sahel region of West Africa. To improve food security and resilience, the government of Niger, with funding from the West African Development Bank, implemented a multi-faceted agricultural production intensification program. Evaluating the effectiveness of such programs over time is challenging in remote and conflict-affected regions. We use remote sensing data with a novel econometric design to measure the impact of constructing surface irrigation infrastructure and land rehabilitation projects within this program on agricultural production and water retention, using vegetation indices (Normalized Difference Vegetation Index and Soil Adjusted Vegetation Index) and soil moisture bands, respectively. We employ 500 m² pixel-level remote sensing data from Landsat-7, TerraClimate, and CHIRPS. We measure the direct impact of irrigation and land rehabilitation on those who benefited and the overall potential impact of providing surface irrigation on all intended recipients, whether or not they used it, to assess agricultural production outcomes. Results show that there are significant increases in vegetation and soil moisture due to the surface irrigation, particularly in the dry season. We also find heterogeneous impacts by project region, with a larger increase in vegetation and soil moisture in the Ibohamane village than the Dougeruoua village. At the same time, the effect of the combined land rehabilitation interventions on agricultural production and soil moisture are mixed, and ultimately, negligible. We demonstrate the unique value that using a synthetic difference-in-difference design alongside open-source remotely sensed data can provide to measure the long-term impact of agricultural interventions, particularly irrigation projects, which tend to fail over time in certain contexts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102014"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}