Remote Sensing Applications-Society and Environment最新文献

{"title":"Hedgerow map of Bavaria, Germany, based on orthophotos and convolutional neural networks","authors":"Verena Huber-García ,&nbsp;Jennifer Kriese ,&nbsp;Sarah Asam ,&nbsp;Mariel Dirscherl ,&nbsp;Michael Stellmach ,&nbsp;Johanna Buchner ,&nbsp;Kristel Kerler ,&nbsp;Ursula Gessner","doi":"10.1016/j.rsase.2025.101451","DOIUrl":"10.1016/j.rsase.2025.101451","url":null,"abstract":"<div><div>Hedgerows play a significant role in biodiversity preservation, carbon sequestration, soil stability and the ecological integrity of rural landscapes. Understanding their current condition and future development is therefore crucial for a range of stakeholders such as municipalities, state agencies or environmental organizations. The wall-to-wall mapping and characterization of hedgerows in-situ is, however, very labour-, time- and cost-intensive. This impedes a regular monitoring at adequate intervals. In the Federal State of Bavaria, Germany, the hedgerow biotope mapping is repeated every 20–30 years for each district. State-wide consistent and up-to-date data are hence not available. In this study we present an approach for mapping all hedgerows in Bavaria using orthophotos and deep learning. We used hedgerow polygons of the federal in-situ biotope mapping from 5 focus districts as well as additional manually digitized polygons as training and test data and orthophotos as input in a DeepLabV3 Convolutional Neural Network (CNN). The CNN has a Resnet50 Backbone and was optimized using the Dice loss as cost function. The orthophotos were acquired in 2019–2021. They have a spatial resolution of 20 cm and were fed to the CNN at tiles of 125 × 125 m. The generated hedgerow probability tiles were post-processed through merging and averaging the overlapping tile boarders, shape simplification and filtering. The resulting hedgerow vector data set achieved medium overall accuracies (precision = 0.43, recall = 0.53, F1-score = 0.48). The model generally overestimated the number of hedgerows, and hedgerows were often confused with riparian as well as urban vegetation. Looking at each hedgerow polygon individually, the mapping accuracy varied considerably, with a median F1-score of 0.51 for all detected objects. In addition, we found differences in accuracies among districts in different landscapes. For example, the Hassberge district, a landscape rich of hedgerows, reached a F1-score of 0.61. A comprehensive comparison with the Copernicus High Resolution Layer (HRL) Small Woody Features (SWF) revealed significant differences between the datasets. About 43 % of the hedgerows in our data set were not present in the SWF layer. Especially narrow, elongated vegetated structures are not captured in the SWF product. This highlights the potential to use our state-wide hedgerow map of Bavaria in combination with the SWF dataset, but also by itself, for a range of administrative, statistical and nature conservation applications.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101451"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing and gravity investigations for barite detection in Neoproterozoic rocks in the Ariab area, Red Sea Hills, Sudan","authors":"Abazar M.A. Daoud ,&nbsp;Ali Shebl ,&nbsp;Mohamed M. Abdelkader ,&nbsp;Ali Ahmed Mohieldain ,&nbsp;Árpád Csámer ,&nbsp;Albarra M.N. Satti ,&nbsp;Péter Rózsa","doi":"10.1016/j.rsase.2024.101416","DOIUrl":"10.1016/j.rsase.2024.101416","url":null,"abstract":"<div><div>The increasing global demand for barite, driven by its geological importance and various industrial applications, advises the scientific community to improve attempts to identify and explore its deposits in different geological settings. This boost in interest aims to ensure sustainable supply by locating new sources and better understanding the conditions in which barite forms. This study presents an integrated approach using multispectral (Landsat 8 &amp; 9, Sentinel-2, and ASTER) and hyperspectral (PRISMA) remote sensing data, along with geophysical gravity data, to improve the localization of barite deposits. Several image processing methods, including false colour composites, principal component analysis, band ratios, minimum noise fraction, and spectral analysis, were employed for the discrimination of barite deposits, revealing their association with felsic rocks (referred to as group C). Additionally, lineament extraction was performed using the recent and advanced different filters like Tilt Angle Horizontal Gradient (TAHG) and Enhanced Horizontal Gradient Amplitude (EHGA) on Bouguer anomalies, highlighting the structural control of barite deposits by the D3 deformation phase. Field investigations were conducted to validate our findings. Based on these field observations, the integrated methodology successfully mapped the distribution of barite and its host rocks, resulting in an updated geological map for barite distribution that can be used in further exploration phases. We strongly recommend the adopted approach and the newly proposed image combinations for preliminary explorations of barite in similar arid terrains.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101416"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated rice mapping using multitemporal Sentinel-1 SAR imagery using dynamic threshold and slope-based index methods","authors":"Aishwarya Hegde A. ,&nbsp;Pruthviraj Umesh ,&nbsp;Mohit P. Tahiliani","doi":"10.1016/j.rsase.2024.101410","DOIUrl":"10.1016/j.rsase.2024.101410","url":null,"abstract":"<div><div>Rice cultivation plays a crucial role in food security and economic development, particularly in regions like India, due to its vast population and position as the top rice producer globally. This work introduces a novel framework, the Rice Mapping Method (RMM), which leverages Multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery for automated rice mapping. Contrary to the traditional approaches, RMM combines the Dynamic Threshold Method (DTM) for robust rice field identification and a slope-based index for classifying single and double cropping practices. By analyzing VH backscatter patterns and employing specific thresholds, DTM separates rice pixels from the other background pixels. The DTM, which relies on VH backscatter values during the growing season, has been tested across various rice cultivation landscapes, demonstrating high accuracy up to 0.95. DTM is also tested on different rice-growing areas such as the hilly Kodagu district, with an F1 Score of 0.96, and in the flooded delta region of Kuttanad, achieving an F1 Score of 0.93. The Slope-based Index <span><math><msub><mrow><mi>I</mi></mrow><mrow><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>c</mi><mo>)</mo></mrow></mrow></msub></math></span> is introduced to differentiate the single and double cropping pixels by calculating the index for the second season of cropping and gives F1 Score of 0.81. The DTM’s effectiveness in rice field identification is evaluated by comparing it to the classification of the Bi-directional Gated Recurrent Unit (Bi-GRU) network. Similarly, the Slope-based Index is compared with other established automated rice mapping methods to assess its accuracy in distinguishing cropping patterns. RMM was successfully applied in mapping rice-growing areas in the Udupi district for 2021, estimating Kharif and Rabi season areas, the estimated rice area is compared to official statistics by the Directorate of Economics and Statistics, Karnataka State. The proposed RMM approach offers a robust solution for mapping rice fields, particularly in regions with complex cropping landscapes, and enhances agricultural monitoring and decision-making processes contributing to sustainable rice production and food security initiatives.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101410"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092313","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":"Seasonality and post fire recovery in a wetland dominated region: Insights from satellite data analysis in northern Argentina","authors":"Griselda Isabel Saucedo ,&nbsp;Ditmar Bernardo Kurtz","doi":"10.1016/j.rsase.2025.101480","DOIUrl":"10.1016/j.rsase.2025.101480","url":null,"abstract":"<div><div>Scientific literature indicates that climate change is driving an increase in wildfires globally. This study was done on a wetland dominated area in Northern Argentina and aims to, i) analyze the monthly and annual variability of burned areas between 2001 and 2022; ii) identify the fire frequency considering inter annual variability; iii) characterize the frequency of fires by season and the affected vegetation cover; and iv) evaluate the ecosystems recovery following the mega fire events of 2022. We found that 80,728 km² burned during the study period, with a seasonal concentration of patchy fires at the end of winter. However, larger burned areas were observed in summer, following dry periods. The highest concentration of burned areas was recorded in the central-east and northwest of the province. 71% of the burned areas experienced at least one fire, while 29% showed increased recurrence. Differences in fire activity based on vegetation cover and seasonal changes revealed that grasslands and wetlands are particularly prone to burning during the summer and winter. The atypical fires of 2022, which coincided with the peak of the growing season, caused phenological shifts of the typical vegetation pattern. Likewise, an analogous pattern was observed in unburned vegetation, attributable to the prevailing climatic conditions. Post-fire precipitation spurred on vegetation recovery depending on the prevailing land cover as follows, grasslands, wetlands, and native forests showed exponential post-disturbance recovery, characterized by an initial rapid recovery phase. In contrast, cultivated forests exhibited very low recovery. As climate change trends intensify in the future, anthropogenic and natural wildfires may exhibit varying impacts on different types of land cover. This research provides novel insights into the spatial and temporal variability of fires and recovery dynamics for the region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101480"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091983","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":"Hyperspectral classification of ancient cultural remains using machine learning","authors":"Rongji Luo ,&nbsp;Peng Lu ,&nbsp;Panpan Chen ,&nbsp;Hongtao Wang ,&nbsp;Xiaohu Zhang ,&nbsp;Shugang Yang ,&nbsp;Qingli Wei ,&nbsp;Tao Wang ,&nbsp;Yongqiang Li ,&nbsp;Tao Liu ,&nbsp;Deyang Jiang ,&nbsp;Jun Du ,&nbsp;Yan Tian ,&nbsp;Zhen Wang ,&nbsp;Hui Wang ,&nbsp;Duowen Mo","doi":"10.1016/j.rsase.2025.101457","DOIUrl":"10.1016/j.rsase.2025.101457","url":null,"abstract":"<div><div>The application of remote sensing in archaeology has recently gained widespread recognition, leading to the discovery of numerous significant cultural remains. However, the lack of theoretical data on spectral classification severely constrains the practicability of remote sensing archaeological investigations. In this study, we have collected a comprehensive dataset comprising over 15,000 spectral curves acquired from eight distinct categories of typical archaeological remains in Central China. Machine learning is utilized to conduct an in-depth analysis and classification of the hyperspectral attributes of cultural remains. The feature spectra are preprocessed using the Standard Normal Variable Transform (SNV) and Principal Component Analysis (PCA). A spectral classification model is proposed to improve the accuracy of typical archaeological remains using Support Vector Machines (SVM). The evaluation demonstrated that the SVM exhibited the highest classification accuracy of 99.82%. It was ultimately determined that the most distinguishable bands from ancient cultural remains were in the ranges of 524–553 nm, 663–686 nm, 974–1000 nm, 1092–1114 nm, and 2161–2185 nm. The research provides an important theoretical basis and a scientific method for remote sensing archaeology investigations, which is of great significance in understanding the past and facilitating present sustainable development.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101457"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091976","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":"Analyzing Urban Heat Islands in Pokhara Metropolitan City-Nepal through Remote Sensing Techniques","authors":"Utsav Jamarkattel ,&nbsp;Badri Raj Lamichhane ,&nbsp;Saurav Gautam ,&nbsp;Niraj K.C. ,&nbsp;Bikash Sherchan ,&nbsp;Teerayut Horanont","doi":"10.1016/j.rsase.2025.101479","DOIUrl":"10.1016/j.rsase.2025.101479","url":null,"abstract":"<div><div>This study provides a comprehensive analysis of the temporal and spatial dynamics of Surface Urban Heat Islands (SUHI) in Pokhara Metropolitan City, Nepal, over the period from 2013 to 2022. Utilizing advanced satellite data and various indices to quantify Land Surface Temperature (LST) variations, this research uniquely focuses on a rapidly urbanizing region in the context of a developing country facing the consequences of climate change. The results reveal significant temperature disparities between urban and suburban areas, with urban zones exhibiting markedly higher maximum (39.13 °C), mean (33.23 °C), and minimum (28.48 °C) LST values compared to their suburban counterparts (34.43 °C, 29.49 °C, and 25.90 °C, respectively). Temporal assessments indicate a consistent increase in LST and an expansion of thermal hotspots, particularly during warmer months, underscoring the intensifying SUHI effect. Correlation analyses further elucidate a moderate negative relationship between the Normalized Difference Vegetation Index (NDVI) and LST (r = -0.58), highlighting the cooling influence of vegetation, while a strong positive correlation with the Normalized Difference Built-up Index (NDBI) (r = 0.82) emphasizes the impact of urbanization on rising temperatures. These findings underscore an urgent need for sustainable urban planning that integrates green spaces and adaptive design strategies to mitigate SUHI effects, reduce thermal stress on residents, and enhance urban resilience against climate change impacts, thereby advocating for increased vegetation cover, sustainable construction practices, and innovative cooling solutions to improve overall urban living conditions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101479"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376572","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":"Utility of Earth Observation data in mapping post-disaster impact: A case of Hurricane Dorian in the Bahamas","authors":"Mohammed Ozigis ,&nbsp;Oluropo Ogundipe ,&nbsp;Samuel J. Valman ,&nbsp;Jessica L. Decker Sparks ,&nbsp;Helen McCabe ,&nbsp;Rebekah Yore ,&nbsp;Bethany Jackson","doi":"10.1016/j.rsase.2025.101466","DOIUrl":"10.1016/j.rsase.2025.101466","url":null,"abstract":"","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101466"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tillage direction analysis in agricultural fields from Digital Orthophotos and Sentinel-2 imagery","authors":"Sebastian Goihl","doi":"10.1016/j.rsase.2025.101486","DOIUrl":"10.1016/j.rsase.2025.101486","url":null,"abstract":"<div><div>For questions of soil and water protection, knowledge about agricultural management is relevant, especially in hilly and mountainous areas. In sloping areas, an area-wide knowledge of whether farming is done with or across the contour line would be very valuable for use in regional soil conservation management. In order to ascertain the prevalence of farming practices conducted with or against the slope in a given region, it is necessary to obtain data on the direction in which fields are cultivated. This information can be derived from remote sensing data through the application of geographic information system (GIS) methods. While previous studies have attempted to provide knowledge primarily through the use of small-scale but high-resolution Unmanned Aerial Vehicle (UAV) imagery, this study used medium-resolution imagery from satellite imagery (Sentinel-2 at 10 m × 10 m) and high resolution imagery (0.2 m × 0.2 m) Digital Orthophotos (DOP) from aircraft flights.</div><div>The use of medium-resolution satellite images (such as Sentinel-2) has yet to be explored in the context of addressing this research question, and this study represents their preliminary application in this domain. For this purpose, two GIS-based methods of analysis were proposed, which mainly made use of high-pass filtering, reclassification, vectorization, and compass orientation calculation. The results are promising, as in the best cases the correlation, between processing and ground truth orientation of the field tillage direction, for the DOP is R² of 0.867 for 170 fields and 2.687 ha. For the Sentinel-2 evaluation, an R² of 0.833 was obtained for 141 fields with 2.611 ha. Despite the different spatial resolution of both systems, the results are very comparable in terms of spatial coverage and accuracy of validation. However, for these two cases, this also meant that less than 50% of the total agricultural area and less than 20% of all fields in the study area could be covered. The data obtained from the DOP and Sentinel-2 sensors were collected at different times, resulting in the identification of distinct preferences for specific crop types. These preferences were observed to yield both accurate and less accurate evaluations, respectively. For instance, wheat exhibited favorable outcomes. Overall, the proposed approach demonstrated the capacity to derive area-wide information on farming direction with satisfactory results. Especially the temporarily high data availability of Sentinel-2 should be used to generate an overall picture using crop rotation and different phenological stages of arable crops in the long term.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101486"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of subsidence factors and modeling of susceptibility under coupled geohydrological conditions - A case study of Jiangsu Yangtze River section","authors":"Wen-Jiang Long ,&nbsp;Xue-Xiang Yu ,&nbsp;Ming-Fei Zhu","doi":"10.1016/j.rsase.2025.101491","DOIUrl":"10.1016/j.rsase.2025.101491","url":null,"abstract":"<div><div>Ground subsidence along the riverbanks near the Yangtze River Delta has been accelerating due to human activities and other factors, seriously impacting various aspects of social development. Mapping susceptibility patterns and analyzing subsidence factors are crucial for effective management. This study focused on the Yangtze River riparian perimeter in Jiangsu Province, our study area. We assessed the importance of different factors using the random forest regression (RFR) model and the temporal convolution network (TCN). Additionally, we used GeoDetector to analyze the spatial relationship between sedimentation and potential drivers. Finally, we utilized the RFR and Maxent model to map susceptibility to sedimentation patterns in different risk zones. The study results show that the method effectively depicts the susceptibility to subsidence in each risk zone (44.18% and 32.56% for high and average risk zones, respectively). Anthropogenic factors mainly drive the subsidence-prone areas around the Yangtze River in Jiangsu. Groundwater extraction and soft soil thickness are the primary drivers of subsidence patterns in high-risk areas. In contrast, the main drivers of subsidence in other risk areas vary. These differences reflect the delayed effects of natural and anthropogenic factors on subsidence and the significant differences in how anthropogenic drivers affect the marginal effects of subsidence. Through susceptibility modeling and driver evaluation, this study reveals that establishing risk zones has improved our understanding of the impact of regional variations in environmental variables on subsidence. This understanding will facilitate the development of subsidence management strategies tailored to different regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101491"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison between machine learning classification and trajectory-based change detection for identifying eucalyptus areas in Landsat time series","authors":"Debora da Paz Gomes Brandão Ferraz,&nbsp;Raúl Sánchez Vicens","doi":"10.1016/j.rsase.2024.101444","DOIUrl":"10.1016/j.rsase.2024.101444","url":null,"abstract":"<div><div>In forestry, where species management has inter-annual durations, time series longer than one year have been used to map planted areas, estimate biophysical parameters, and determine planting age. Eucalyptus plantations have characteristics that enhance the use of time series in their classification, such as clear-cutting before planting or previous rotation and the rapid growth of large vegetation cover, which remains stable throughout the rotation period. This article compares the performance of two classification methods for mapping Eucalyptus areas: an object-oriented classification (GEOBIA) using products generated by the LandTrendr change detection algorithm based on trajectories and a classification using machine learning (random forest). Using a Landsat time series from 1985 to 2020, tests were conducted in a pilot area in Rio de Janeiro, Brazil. Both methods showed high accuracy in detecting Eucalyptus areas. However, the trajectory-based classification proved slightly superior, achieving a global accuracy of 0.988 and an F-Score of 0.975, while the classification using the random forest algorithm achieved a global accuracy of 0.954 and an F-score of 0.849. Regarding identifying the initial year of planting, both methods proved effective without showing significant differences (p-value = 0.1003). However, detecting the initial year using the LandTrendr algorithm proved more assertive. Both methods revealed periods of increase and stabilization in Eucalyptus planting throughout the time series, proving promising for determining the location and age of each stand and, thus, obtaining information about the time of use of that area for Eucalyptus cultivation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101444"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101042","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}