Remote Sensing Applications-Society and Environment最新文献

{"title":"Validating the IMERG remote sensing precipitation data for extremes analysis using the new hybrid depth duration frequency model","authors":"Kenneth Okechukwu Ekpetere","doi":"10.1016/j.rsase.2025.101547","DOIUrl":"10.1016/j.rsase.2025.101547","url":null,"abstract":"<div><div>This study introduces a novel hybrid model for estimating depth-duration-frequency (DDF) estimates by integrating four probability distribution functions (PDFs) – Gumbel, GEV, GPD, and EP–through a maximum likelihood-based weighting scheme. Addressing a critical gap in extreme precipitation analysis, where model selection often fails to capture the variability across diverse climate zones, this hybrid model dynamically allocates weights to each PDF component according to the prevailing climate conditions at each location, optimizing DDF estimates for both wet and dry climates. Using Integrated Multi-Satellite Retrievals for GPM (IMERG) data, DDF estimates were calculated across multiple durations and return periods and validated against NOAA Atlas 14 precipitation frequency estimates (PFE) from 2360 stations across the continental United States (CONUS). Results indicate high correspondence between IMERG-based DDF estimates and Atlas 14 PFE, with an average correlation coefficient of 0.71, an average relative bias of 45.6 %, and an NRMSE of 12.1 mm across return periods. The model demonstrated increased agreement over longer durations and in regions with higher rainfall, with correlation coefficients rising from 0.569 for 0.5-h durations to 0.768 for 72-h durations. Spatial analysis shows the hybrid model's robustness, particularly in capturing trends across both wet and dry regions, suggesting its utility for extreme rainfall estimation in ungaged and climatologically diverse areas. This hybrid approach provides a versatile and regionally adaptive tool for engineers, hydrologists, and policymakers, offering improved precision for flood risk management and climate resilience planning. The hybrid model ensures that the prevailing PDF based on regional susceptibility, gets the highest weight, thus dominating influence of the model. Future work aims to extend the Hybrid model application beyond CONUS, enabling broader global applications in diverse, data-scarce regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101547"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864097","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":"Modeling PM2.5 concentration in tehran using satellite-based Aerosol optical depth (AOD) and machine learning: Assessing input contributions and prediction accuracy","authors":"Zahra Amiri ,&nbsp;Maryam Zare Shahne","doi":"10.1016/j.rsase.2025.101549","DOIUrl":"10.1016/j.rsase.2025.101549","url":null,"abstract":"<div><div>The adverse effects of PM_2.5 on human health and the environment necessitate precise and continuous monitoring of this pollutant. Satellite remote sensing technology provides an effective and cost-efficient alternative to ground-based measurements. However, accurately estimating ground-based PM_2.5 concentrations using Aerosol Optical Depth (AOD) is challenging due to the influence of various parameters and atmospheric conditions on the AOD-PM_2.5 relationship. In this study, Aerosol Optical Depth (AOD) data were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at a spatial resolution of 1 km, covering a ten-year period from 2012 to 2023. The objective was to estimate PM₂.₅ concentrations for six ground-based monitoring stations in Tehran, Iran. These estimated concentrations were compared with daily measurements from the Tehran Air Quality Control Company air pollution monitoring stations for the same period. To determine the most significant conditioning factors in the modeling process and their impacts, the genetic algorithm optimization method and the Recursive Feature Elimination (RFE) technique were employed. The results indicated that, in addition to the AOD parameter, meteorological parameters such as wind speed, wind direction, temperature, precipitation, normalized difference vegetation index (NDVI), and visibility (VIS) could enhance model accuracy. Predictions were made using three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and <span>Gaussian</span> Process Regression (GPR). The findings revealed that the RF method was the most accurate, achieving accuracy in the range of 94–98 % for predicting PM_2.5 concentrations for all the studied stations. This study's results can significantly aid policymakers and researchers in utilizing satellite data for air pollution monitoring and management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101549"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868625","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":"Walrus Optimization Algorithm for panchromatic and multispectral image fusion","authors":"R. Dileep ,&nbsp;J. Jayanth ,&nbsp;A.L. Choodarathnakar ,&nbsp;H.K. Ravikiran","doi":"10.1016/j.rsase.2025.101562","DOIUrl":"10.1016/j.rsase.2025.101562","url":null,"abstract":"<div><div>The fusion of Panchromatic (PAN) and Multispectral (MS) images is critical for enhancing spatial and spectral resolution in remote sensing, especially in agriculture. However, traditional methods face limitations, such as spectral distortion in the Multiplicative Transform (MT), over-enhanced spatial details in the Brovey Transform (BT), and trade-offs in wavelet-based approaches. This study introduces the Walrus Optimization Algorithm (WAOA), which dynamically optimizes spectral weights and spatial adjustments while refining wavelet coefficients to balance spatial and spectral quality. Comparative analysis was conducted using BT, MT, Wavelet Transform (WT), and their WaOA-optimized versions on PAN and MS images from one agricultural region. Metrics such as Average Difference (AD), Root Mean Squared Error (RMSE), correlation coefficient (r), and Structural Similarity Index (SSIM) were evaluated. WT-WaOA emerged as the best method with an AD of 0.00007, RMSE of 0.03882, SSIM of 0.84561, and band means closest to the MS benchmark (e.g., Band 1: 89.57 for WT-WaOA vs. 99.86 for MS). SD analysis highlights WT-WaOA's ability to preserve contrast and variability, ranking second only to MS and PAN across all bands. These findings position WT-WaOA as a reliable fusion method for balanced spatial and spectral detail integration.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101562"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859510","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":"Reconstruction of the lost Saraswati river course and its associated palaeochannels using multi-resolution SAR and MSS images in the northwestern desertic plains of Rajasthan, India","authors":"Raja Biswas ,&nbsp;V.S. Rathore ,&nbsp;A.P. Krishna ,&nbsp;Anup Kumar Das ,&nbsp;Avik Bhattacharya ,&nbsp;Alok Porwal","doi":"10.1016/j.rsase.2025.101559","DOIUrl":"10.1016/j.rsase.2025.101559","url":null,"abstract":"<div><div>The lost Saraswati River offers a significant challenge for geomorphologists and archaeologists. It is believed that the river used to flow through the present-day Thar Desert during the Vedic age. It desiccated between ∼7000 and ∼1200 BC due to tectonic and climatic changes, leaving behind palaeochannels and playas in northwestern Rajasthan. This study aims to delineate the ancient Saraswati River and its associated palaeochannels using multi-sensor satellite data, including SAR (Sentinel-1A, ALOS PALSAR), multispectral (Sentinel-2A), and DEM. Multiple fusion algorithms (IHS, GS, PCA, Wavelet, and Ehlers) were used to fuse SAR and optical data, enhancing the visibility of the river course and palaeochannels. Various image indices assessing surface moisture and vegetation patterns further helped in palaeochannel detection. Among the fused images, the IHS, GS, and PCA techniques, combining Sentinel-2 and ALOS PALSAR data, were found to be the most effective in highlighting palaeochannels. Further, image indices such as NDVI, NDWI and NDMI led to confirm palaeochannels and the old river course by showing linearly oriented vegetation and soil moisture. The study successfully traced two major palaeo-courses of the Saraswati River, originating from the Ghaggar River near Anupgarh and flowing through Beriyawali, Bahla, Tanot, and Jaisalmer before emptying into the Great Rann of Kutch. Additionally, three major palaeo-drainage systems of the Saraswati River could be delineated. Moreover, the association of the Harappan archaeological sites distribution along with the delineated Saraswati River course and its paleochannels, evidence from the historical maps, and bore-well drilling data (groundwater levels and lithologs) also confirm the results of this study.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101559"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847451","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":"LOD1 3D city model from LiDAR: The impact of segmentation accuracy on quality of urban 3D modeling and morphology extraction","authors":"Fatemeh Chajaei,&nbsp;Hossein Bagheri","doi":"10.1016/j.rsase.2025.101534","DOIUrl":"10.1016/j.rsase.2025.101534","url":null,"abstract":"<div><div>Three-dimensional reconstruction of buildings, particularly at Level of Detail 1 (LOD1), plays a crucial role in various applications such as urban planning, urban environmental studies, and designing optimized transportation networks. This study focuses on assessing the potential of LiDAR data for accurate 3D building reconstruction at LOD1 and extracting morphological features from these models. Four deep semantic segmentation models — U-Net, Attention U-Net, U-Net3+, and DeepLabV3+ — were used, applying transfer learning to extract building footprints from LiDAR data. The results showed that U-Net3+ and Attention U-Net outperformed the others, achieving IoU scores of 0.833 and 0.814, respectively. Various statistical measures, including maximum, range, mode, median, and the 90th percentile, were used to estimate building heights, resulting in the generation of 3D models at LOD1. As the main contribution of the research, the impact of segmentation accuracy on the quality of 3D building modeling and the accuracy of morphological features like building area and external wall surface area was investigated. The results showed that the accuracy of building identification (segmentation performance) significantly affects the 3D model quality and the estimation of morphological features, depending on the height calculation method. Overall, the UNet3+ method, utilizing the 90th percentile and median measures, leads to accurate height estimation of buildings and the extraction of morphological features.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101534"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785925","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":"On the occurrence and causes of long-term declines in MODIS NDVI within the savanna environment of central Brazil","authors":"Yan Breno Azeredo Gomes da Silva ,&nbsp;Lênio Soares Galvão ,&nbsp;Ieda Del'Arco Sanches ,&nbsp;Lucas Batista de Oliveira","doi":"10.1016/j.rsase.2025.101558","DOIUrl":"10.1016/j.rsase.2025.101558","url":null,"abstract":"<div><div>The trajectory analysis of the Normalized Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), can reveal long-term declines potentially linked to land degradation or decreasing vegetation productivity. In this study, we investigated the occurrence and causes of MODIS NDVI declines in the Southwest Goiás Microregion, one of the oldest agricultural areas in the Brazilian savanna environment (Cerrado). Before conducting the NDVI trajectory analysis with the Trends.Earth tool to identify changing patterns from 2000 to 2020, we first examined land use dynamics from 1985 to 2020 using Landsat imagery. We then employed binary logistic regression to statistically examine various potential factors contributing to NDVI declines. In the logistic regression model, the Aggregate NDVI Trend Indicator was used as the response variable, recoded as a binary outcome: 0 for no decline in NDVI and 1 for decline in NDVI, the long-term event of interest. Fourteen categorical and five continuous predictor variables were considered, encompassing land use and land cover changes, duration of pasture and crop use, fire frequency, precipitation, soil composition, and topography. The results showed a significant overall increase in NDVI across 66% of the study area, with 28% remaining stable. However, statistically significant NDVI declines covered 3364 km², or approximately 6% of the study area, as shown by Trends.Earth analysis. Logistic regression indicated that NDVI declines were primarily driven by two factors: the conversion of savanna to pastures and the soil composition or texture. Approximately 50% of the declines occurred in pastures converted from native savanna vegetation, while 25% were observed in savannas and 14% in crops. NDVI declines were predominantly observed in pastures situated over soils with more than 500 g/kg of sand content. Given the recent expansion of crop areas over existing pastures, detected in our study with Landsat data, the number of recorded declines in NDVI or land degraded areas is likely to increase in near future, particularly if this expansion occurs on sandy soils without adoption of adequate soil and crop management practices. Our study highlights the importance of time series analysis of satellite data in assessing land conditions in the Brazilian savanna environment.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101558"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855846","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 Novel Architecture for building rooftop extraction using remote sensing and deep learning","authors":"Zeenat khadim Hussain ,&nbsp;Jiang Congshi ,&nbsp;Muhammad Adrees ,&nbsp;Hamza Chaudhary ,&nbsp;Rafia Shafqat","doi":"10.1016/j.rsase.2025.101551","DOIUrl":"10.1016/j.rsase.2025.101551","url":null,"abstract":"<div><div>Enhancing the accuracy of building rooftop extraction from UAV and remote sensing imagery is crucial for urban planning, disaster management, 3D modeling, and solar resource assessment. In response to this need, a high-quality, open-source dataset focused on rooftop segmentation in Wuhan's Hongshan District has been developed, presenting over 14,000 annotated images. These images are accurately labeled using the Efficient Interactive Segmentation tool (EISeg) for precise superpixel identification. Moreover, to address challenges related to high acquisition costs and reliance on single data sources, a novel framework is proposed that utilizes open-source, high-resolution Google Earth imagery and UAV data. Furthermore, this framework employs tile segmentation techniques for efficient large-scale data management and leverages an advanced EISeg tool for high-precision annotations. Also, four deep learning models were evaluated for semantic segmentation, including the Asymmetric Neural Network (ANN), DeepLabv3, PP-LiteSeg, and Dual Attention Network (DANet). Consequently, the ANN model achieved the highest accuracy at 96 percent, outperforming DANet at 95.09 percent, PP-LiteSeg at 94.54 percent, and DeepLabv3 at 81.61 percent. Furthermore, an intelligent mosaicking algorithm based on GDAL, combined with post-processing optimization, improved processing efficiency by 3.2 times while preserving image accuracy. This research provides a precise and cost-effective solution for building rooftop detection in complex urban environments, significantly improving the scalability and reliability of remote sensing data processing. These improvements enable more efficient large-scale urban analysis, ultimately supporting critical applications in smart city development, disaster response, and solar energy assessment.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101551"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828656","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":"Urban growth unveiled: Deep learning with satellite imagery for measuring 3D building-stock evolution in Urban China","authors":"Sebastiano Papini ,&nbsp;Susie Xi Rao ,&nbsp;Sapar Charyyev ,&nbsp;Muyang Jiang ,&nbsp;Peter H. Egger","doi":"10.1016/j.rsase.2025.101523","DOIUrl":"10.1016/j.rsase.2025.101523","url":null,"abstract":"<div><div>Time-series information on building stock is of paramount importance to study cities in a host of disciplines ranging from economics to urban planning. Such data are lacking in a consistently measured way and especially among dynamically growing cities in developing countries. Due to their rapid change, building stock data in these cities can offer insights into the determinants and consequences of urbanization. To be able to analyze urban structures effectively, the building stock needs to be measured with sufficient detail – at a resolution that makes individual buildings or small conglomerates thereof visible – and it needs to consider building height (or volume) with a satisfactory scope across cities to cover both large numbers and multi-year sequences of data. This study aims to develop a comprehensive pipeline for predicting building volume – including both footprint and height – across 1,537 urban areas in mainland China, covering more than 60% of the Chinese population over a seven-year period (2017–2023). With the advancement of deep learning in remote sensing, we can leverage state-of-the-art techniques to efficiently produce large-scale data for Chinese cities across years, which could be very time-consuming with traditional remote-sensing techniques. We compare the performance of several deep learning architectures for the task at hand. We demonstrate that the best performing approach leads to credible metrics of both footprint and height predictions and performs very competitively with respect to existing building-volume predictions. We also benchmark our results against other data sources such as real-estate listings and demonstrate the out-of-sample prediction capability of the proposed model.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101523"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851605","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":"ASTER-based alteration mapping and structural analysis of the Saheb Divan hydrothermal system, NW Iran: Implications for exploration programs","authors":"Behnam Gholipour ,&nbsp;Nematollah Rashidnejad Omran ,&nbsp;Ahmad Rabiee ,&nbsp;Mir Ali Asghar Mokhtari ,&nbsp;Shahrouz Babazadeh","doi":"10.1016/j.rsase.2025.101532","DOIUrl":"10.1016/j.rsase.2025.101532","url":null,"abstract":"<div><div>The Saheb Divan area, situated within the Arasbaran metallogenic belt in northwest Iran, exhibits extensive hydrothermal alterations indicative of potential porphyry Cu-Mo-Au mineralization. This study integrates remote sensing on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data and field-based investigations to map alteration zones and structural lineaments. A comprehensive set of image processing techniques, including False Color Composite (FCC), Band Ratio (BR), Minimum Noise Fraction (MNF), Least Squares Fit (LS-Fit), and Spectral Feature Fitting (SFF), was applied to ASTER Visible and Near Infrared (VNIR) and Short Wavelength Infrared (SWIR) bands to detect key alteration minerals and delineate alteration zones.</div><div>Among these techniques, SFF emerged as the most effective method, providing high accuracy in mapping phyllic, argillic, advanced argillic, and silicification zones. SFF achieved detection accuracies of 90 % for argillic and 88 % for advanced argillic alterations, outperforming LS-Fit (85 % and 80 %). For phyllic zones, both methods showed comparable accuracy (SFF: 86 %, LS-Fit: 84 %), while LS-Fit performed better in iron oxide-bearing zones (78 % vs. 70 % for SFF). For propylitic zones, SFF had a slight advantage (82 % vs. 78 %), demonstrating its strength in capturing detailed spectral features. The combination of these techniques facilitated the identification of alteration zones, with validation achieved through fieldwork and X-ray diffraction (XRD) analysis. These analyses confirmed distinct mineral assemblages: sericite, illite, kaolinite, alunite, chlorite, and quartz associated with phyllic, argillic, and advanced argillic alterations, respectively. The final integrated map revealed three major alteration zones: a large western zone, a medium-sized central zone, and a smaller southeastern zone. These zones displayed overlapping phyllic and advanced argillic alterations with peripheral propylitic halos, indicative of potential porphyry systems. The western zone, characterized by intense alteration, was identified as the highest-priority target for exploration drilling. Field observations further highlighted the role of structural controls, with faults acting as conduits for hydrothermal fluid circulation.</div><div>This study highlights the efficiency of ASTER-based techniques, especially SFF and LS-Fit, in detecting hydrothermal alteration zones by a quantitative comparison approach. By integrating remote sensing with laboratory analyses, the research provides a comprehensive framework for reducing exploration costs and enhancing targeting precision in challenging terrains. The findings underscore the potential for porphyry mineralization in the Saheb Divan area and offer valuable insights for future exploration programs.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101532"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783049","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 monitoring method for pine wilt disease infected discolored and deceased pine trees removal information based on DDPTnet network and Bi-temporal UAV imagery","authors":"Xiaocheng Zhou ,&nbsp;Huageng Zeng ,&nbsp;Pai Wang ,&nbsp;Chongcheng Chen ,&nbsp;Hao Wu","doi":"10.1016/j.rsase.2025.101530","DOIUrl":"10.1016/j.rsase.2025.101530","url":null,"abstract":"<div><div>Pine Wilt Disease (PWD) is spreading globally, and the failure to timely detect and clear Discolored and Deceased Pine Trees (DDPT) carrying the pine wilt will further exacerbate the spread of PWD in affected regions. To address the necessity of recognizing the DDPT removal information, this research constructs a highly robust Discolored and Deceased Pine Trees network (DDPTnet) for DDPT detection. Additionally, by integrating detection results from DDPTnet with dual-period UAV Digital Orthophoto Models (DOM), Digital Surface Models (DSM), and a scene classification algorithm (DDPTnet-cls) based on DDPT, the current gap in DDPT removal information monitoring has been addressed. In the three monitoring areas, DDPTnet achieved an average F1 score (F1) of 87.31 % for DDPT detection. By integrating the detection results of DDPTnet with dual-period DSM, the average F1 score (F1-la) for logged area extraction was 94.59 %, and the average F1 score (F1-l) for identifying logged DDPT was 82.08 %. The DDPTnet_cls classification method, after fine-tuning, achieved an average classification accuracy (Acc) of 77.91 %. Finally, based on the above results, The “DDPT Removal Information Thematic Map” were produced. These outcomes can provide objective and effective decision support for the prevention and control of PWD outbreaks.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101530"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760186","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}