Egyptian Journal of Remote Sensing and Space Sciences最新文献

{"title":"Developing models to detect maize diseases using spectral vegetation indices derived from spectral signatures","authors":"Basani Lammy Nkuna ,&nbsp;Johannes George Chirima ,&nbsp;Solomon W. Newete ,&nbsp;Adolph Nyamugama ,&nbsp;Adriaan Johannes van der Walt","doi":"10.1016/j.ejrs.2024.07.005","DOIUrl":"10.1016/j.ejrs.2024.07.005","url":null,"abstract":"<div><p>Maize, a vital global crop, faces numerous challenges, including outbreaks. This study explores the use of spectral vegetation indices for the early detection of maize diseases in individual leaves based on crop phenology at the vegetative, tasselling, and maturity stages. The research was conducted in rural areas of Giyani in the Limpopo province, South Africa, where smallholder farmers heavily rely on maize production for sustenance. Fungal and viral diseases pose significant threats to maize crops, necessitating precise and timely disease detection methods. Hyperspectral remote sensing, with its ability to capture detailed spectral information, offers a promising solution. The study analysed spectral reflectance data collected from healthy and diseased maize leaves. Various vegetation indices derived from spectral signatures, including the Normalized difference vegetation index (NDVI), Anthocyanin Reflectance Index (ARI), photochemical Reflectance Index (PRI), and Carotenoid Reflectance Index (CRI) were investigated for their ability to show disease-related spectral variations. The results indicated that during the tasselling stage, the spectral differences had minimum absorption in the blue region. However, a distinct shift in spectral reflectance was observed during the vegetative stage with 70 % increase in reflectance. First derivative reflectance analysis revealed peaks at approximately 715 nm and 722 nm, which were useful in the discrimination of the different growth stages. Generalized Linear Models (GLM) with binomial link functions and Akaike Information Criterion (AIC) showed that individual vegetation indices performed equally well. NDVI (P&lt;0.001) and CRI (P&lt;0.000) showed the lowest AIC values across all growth stages, suggesting their potential as effective disease indicators. These findings underscores the significance of employing remote sensing technology and spectral analysis as essential tools in the endeavours to tackle the difficulties encountered by maize growers, especially those operating small-scale farms, and to advance sustainable farming practices and ensure food security.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 597-603"},"PeriodicalIF":3.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000577/pdfft?md5=4be1ca5c0f48641305e8a13b7486c590&pid=1-s2.0-S1110982324000577-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRISMA vs. Landsat 9 in lithological mapping − a K-fold Cross-Validation implementation with Random Forest","authors":"Ali Shebl ,&nbsp;Dávid Abriha ,&nbsp;Maher Dawoud ,&nbsp;Mosaad Ali Hussein Ali ,&nbsp;Árpád Csámer","doi":"10.1016/j.ejrs.2024.07.003","DOIUrl":"10.1016/j.ejrs.2024.07.003","url":null,"abstract":"<div><p>The selection of an optimal dataset is crucial for successful remote sensing analysis. The PRISMA hyperspectral sensor (with 240 spectral bands) and Landsat OLI-2 (boasting high dynamic resolution) offer robust data for various remote sensing applications, anticipating their increased demand in the coming years. However, despite their potential, we have not identified a rigorous evaluation of both datasets in geological applications utilizing Machine Learning Algorithms. Consequently, we conduct a comprehensive analysis using Random Forest, a widely-recommended machine learning algorithm, and employ K-fold cross-validation (with <em>K</em> = 2, 5, 10) with grid-search hyperparameter tuning for enhanced performance. Toward this aim, diverse image-processing approaches, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were applied to enhance feature selection and extraction. Subsequently, to ensure better performance of the RF algorithm, this study utilized well-distributed points instead of polygons to represent each target, thereby mitigating the effects of spatial autocorrelation. Our results reveal dataset-hyperparameter dependencies, with PRISMA mainly influenced by <em>max_depth</em> and Landsat 9 by <em>max_features</em>. Employing grid-search optimally balances dataset characteristics and data splitting (folds), generating accurate lithological maps across all K values. Notably, a significant hyperparameter shift at <em>K</em> = 10 produces the best lithological maps. Fieldwork and petrographic investigations validate the lithological maps, indicating PRISMA’s slight superiority over Landsat OLI-2. Despite this, given the dataset nature and band count difference, we still advocate Landsat 9 as a potent multispectral input for future applications due to its superior radiometric resolution.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 577-596"},"PeriodicalIF":3.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000553/pdfft?md5=cd78548dacf563f3d654cb587e5c2940&pid=1-s2.0-S1110982324000553-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-automated mangrove mapping at National-Scale using Sentinel-2, Sentinel-1, and SRTM data with Google Earth Engine: A case study in Thailand","authors":"Surachet Pinkeaw ,&nbsp;Pawita Boonrat ,&nbsp;Werapong Koedsin ,&nbsp;Alfredo Huete","doi":"10.1016/j.ejrs.2024.07.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.07.001","url":null,"abstract":"<div><p>Mangroves are a crucial part of the coastal ecosystem; thus, precise and up-to-date monitoring is essential to guide regional policies and inform conservation strategies. This study investigates the capabilities of semi-automated remote sensing approaches within a Google Earth Engine framework for national-scale mangrove mapping in Thailand. Remote sensing data from 2018—10,000 data points acquired from Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM)—was used to train several machine learning models. The Gradient Tree Boost (GTB) proved to be the most reliable, with the least variation in validity (the lowest IQR) and the highest average Overall Accuracy of 96.75 ± 0.63 % compared to the others—96.64 ± 0.72 % for Random Forest (RF); 96.12 ± 0.80 %for Classification and Regression Trees (CART); and 95.43 ± 0.74 % for Support Vector Machines (SVM). Thus, the GTB was instrumental in mapping mangrove distribution with 10-m spatial resolution across Thailand from 2016 to 2022, the period in which the mangrove areas increased by 11 %, reflecting successful conservation efforts over the past decade. The developed framework establishes the foundation for semi-automated mangrove mapping that can be developed for other geographical contexts.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 555-564"},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111098232400053X/pdfft?md5=bdd650004ec791bfac1bc83b674714e2&pid=1-s2.0-S111098232400053X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of the usability of Göktürk-2 data and UAV data for pond construction project","authors":"Huseyin Karatas ,&nbsp;Aydan Yaman","doi":"10.1016/j.ejrs.2024.07.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.07.002","url":null,"abstract":"<div><p>Today, many professions need maps that can be produced quickly, precisely, and in detail, as well as the data from these maps. Land data is very important, especially in mapping engineering, both in the public and private sectors. Providing these data and maps is seen as an important expense for individuals or institutions in terms of time, cost and labor force. This study aims to investigate the usability of the data obtained by satellite images and Unmanned Aerial Vehicles (UAV), which can be easily obtained for the design of the pond/dam body within the scope of the pond construction project for irrigation purposes. Within the scope of the study, the data obtained by adding digital terrain models to Göktürk-2 satellite images were compared with the data obtained from the flight study conducted with the UAV; two separate ponds were designed using the created orthophoto and elevation data. As a result, benefit/cost ratios were calculated. The benefit/cost ratio calculated from remote sensing satellite data was 1.32, while the benefit/cost ratio calculated according to the project created with the UAV was 1.48, and the difference between the two data was calculated as 10.73%. According to this result, it was concluded that satellite images could be used in works such as ponds, closed system irrigation works, and land slope analysis, especially in preliminary project design studies. In contrast, data produced by UAV photogrammetry should be used in processes requiring higher precision. With this study, it is aimed that 25 households in the study area will benefit from the irrigation system. Furthermore, the findings of this study will enable institutions to select and utilise data that is appropriate to the purpose of the study and the desired accuracy, taking into account the benefit/cost ratios, without the need for prior fieldwork. By selecting and using the most economical data in accordance with the purpose of the work in engineering projects, optimum benefit will be obtained by saving time and labor.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 565-576"},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000541/pdfft?md5=39a637b96b918f5094f5b44edc69ba0d&pid=1-s2.0-S1110982324000541-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of anthropogenic disturbances on mangrove aboveground biomass in Malaysian Borneo using airborne LiDAR data","authors":"Charissa J. Wong,&nbsp;Lee Ting Chai,&nbsp;Daniel James,&nbsp;Normah Awang Besar,&nbsp;Kamlisa Uni Kamlun,&nbsp;Mui-How Phua","doi":"10.1016/j.ejrs.2024.06.004","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.06.004","url":null,"abstract":"<div><p>Mangroves are known for their carbon storage capacity, yet they are under immense pressure from human activities. This study assessed anthropogenic disturbances on mangroves’ aboveground biomass (AGB) in northern Borneo, Malaysia, using airborne light detection and ranging (LiDAR) data. Three global or pantropical allometries were compared in the development of an AGB estimation model by regressing LiDAR metrics against the AGB. The best model predicted AGB from Saenger and Snedaker allometry with an <em>R</em>² of 0.85 and a root mean square error (RMSE) of 14.59 Mg/ha (relative RMSE: 7.24 %). The high-resolution AGB map revealed a natural AGB gradient in intact mangroves from the coast to the interior. However, only a weak correlation between the distance from shoreline and AGB in disturbed mangroves was found. The LiDAR estimated AGBs were 196.36 Mg/ha and 157.27 Mg/ha for intact mangroves and disturbed mangroves, respectively. Relatively high AGB areas were abundant in the intact mangroves but scarce in the disturbed mangroves. The LiDAR-based AGB assessment is accurate and high-resolution, supporting carbon stock conservation and sustainable management activities under climate change mitigation programs such as REDD + .</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 547-554"},"PeriodicalIF":3.7,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000516/pdfft?md5=b0eaab31894a5a6ec4dd7196797ec530&pid=1-s2.0-S1110982324000516-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Analysis of AlAinSat-1 CubeSat","authors":"Abdalla Elshaal ,&nbsp;Mohamed Okasha ,&nbsp;Erwin Sulaeman ,&nbsp;Abdul Halim Jallad ,&nbsp;Wan Faris Aizat ,&nbsp;Abu Baker Alzubaidi","doi":"10.1016/j.ejrs.2024.06.006","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.06.006","url":null,"abstract":"<div><p>This paper presents the process of conducting the structural analysis of AlAinSat-1 CubeSat through a numerical solution using Siemens NX. AlAinSat-1 is a 3U remote-sensing CubeSat carrying two earth observation payloads. The CubeSat is scheduled for launch on SpaceX Falcon 9 rocket. To ensure the success of the mission and its ability to withstand the launch environment, several scenarios should be analyzed. For AlAinSat-1 model the finite element analysis (FEA) method is used, and four types of structural analyses are considered: modal, quasi-static, buckling, and random vibration analyses. The workflow cycle includes idealizing, meshing, assembling, applying connections and boundary conditions, and eventually running the simulation utilizing the Siemens Nastran solver. The simulation results of all analysis types indicate that the model can safely withstand the loads exerted during launch. Also, the numerical results of the Command and Data Handling Subsystem (CDHS) module of AlAinSat-1 are experimentally validated through a vibration test conducted using an LV8 shaker system. The module successfully passed the test based on the test success criteria provided by the launcher.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 532-546"},"PeriodicalIF":3.7,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000528/pdfft?md5=275e6d7bf7342baae7acd383ef566938&pid=1-s2.0-S1110982324000528-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric vs spectral content of Remotely Piloted Aircraft Systems images in the Precision agriculture context","authors":"Filippo Sarvia,&nbsp;Samuele De Petris,&nbsp;Alessandro Farbo,&nbsp;Enrico Borgogno-Mondino","doi":"10.1016/j.ejrs.2024.06.003","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.06.003","url":null,"abstract":"<div><p>In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 524-531"},"PeriodicalIF":6.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000498/pdfft?md5=d6fcd092e52b40b7f169fa7af5edf8e2&pid=1-s2.0-S1110982324000498-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141423149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SHAP-PDP hybrid interpretation of decision-making mechanism of machine learning-based landslide susceptibility mapping: A case study at Wushan District, China","authors":"Deliang Sun ,&nbsp;Yuekai Ding ,&nbsp;Haijia Wen ,&nbsp;Fengtai Zhang ,&nbsp;Junyi Zhang ,&nbsp;Qingyu Gu ,&nbsp;Jialan Zhang","doi":"10.1016/j.ejrs.2024.06.005","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.06.005","url":null,"abstract":"<div><p>For landslide prevention and control, it is essential to establish a landslide susceptibility prediction framework that can explain the model’s decision-making process. Wushan County, Chongqing was selected as the study area, and seventeen landslide conditioning factors were initially chosen for this investigation. GeoDetector was used to remove noise factors and reduce the latitude of the data. The research investigates the use of three machine learning methods for assessing landslide susceptibility: SVM, RF, and XGBoost, and finally explains the decision mechanism of the model by SHAP-PDP. The results indicate that XGBoost has better evaluation results than RF and SVM. And XGBoost uncertainty is lower. The integrated interpretation framework based on SHAP-PDP can evaluate and interpret landslide susceptibility models both globally and locally, which is of great practical significance for the application of machine learning in landslide prediction.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 508-523"},"PeriodicalIF":6.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000504/pdfft?md5=d6e19e038f59fc8a7194ef596756506a&pid=1-s2.0-S1110982324000504-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MICAnet: A Deep Convolutional Neural Network for mineral identification on Martian surface","authors":"Priyanka Kumari ,&nbsp;Sampriti Soor ,&nbsp;Amba Shetty ,&nbsp;Shashidhar G. Koolagudi","doi":"10.1016/j.ejrs.2024.06.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.06.001","url":null,"abstract":"<div><p>Mineral identification plays a vital role in understanding the diversity and past habitability of the Martian surface. Mineral mapping by the traditional manual method is time-consuming and the unavailability of ground truth data limited the research on building supervised learning models. To address this issue an augmentation process is already proposed in the literature that generates training data replicating the spectra in the MICA (Minerals Identified in CRISM Analysis) spectral library while preserving absorption signatures and introducing variability. This study introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture for mineral identification using the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) hyperspectral data. MICAnet is inspired by the Inception-v3 and InceptionResNet-v1 architectures, but it is tailored with 1-dimensional convolutions for processing the spectra at the pixel level of a hyperspectral image. To the best of the authors’ knowledge, this is the first DCNN architecture solely dedicated to mineral identification on the Martian surface. The model is evaluated by its matching with a TRDR (Targeted Reduced Data Record) dataset obtained using a hierarchical Bayesian model. The results demonstrate an impressive f-score of at least .77 among different mineral groups in the MICA library, which is on par with or better than the unsupervised models previously applied to this objective.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 501-507"},"PeriodicalIF":6.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000474/pdfft?md5=571ed6384d90f85a6a7247fab174e509&pid=1-s2.0-S1110982324000474-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the green guardians: Mapping and identification of Azadirachta indica trees with semantic segmentation deep learning neural network technique","authors":"Pankaj Lavania ,&nbsp;Ram Kumar Singh ,&nbsp;Pavan Kumar ,&nbsp;Savad K. ,&nbsp;Garima Gupta ,&nbsp;Manmohan Dobriyal ,&nbsp;A.K. Pandey ,&nbsp;Manoj Kumar ,&nbsp;Sanjay Singh","doi":"10.1016/j.ejrs.2024.06.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.06.002","url":null,"abstract":"<div><p>The high spatial resolution data presents a problem when it comes to mapping and identifying distinct tree species based on the characteristics of their canopies. The deep learning Semantic Segmentation approach based on U-Network (U-Net.) artificial intelligence model that we provide here can recognize, and map <em>Azadirachta indica</em> trees canopy cover. This method trains its model by making use of image chips and labels of the item being segmented. The new testing images processed for multiple stages of pixel level of convolution and pooling operations. The sampling methods allow increase to make complete to make the recognized object on the image. The model’s ability to identify items based on canopy shape, structure, and pixel data makes it very useful for mapping and recognizing a single tree species as well as several tree species. The model validation results indicated an accuracy of 84–89 percent, which is regarded to be rather good. Based on ground census data, the overall accuracy of identification is 89 percent, F1 score 0.91–0.94, while the complete tree canopy validation (Intersection to Union) for canopy matching area is 0.79–0.89. The method has the potential to be utilised for identification, mapping of tree canopy. The approach has the potential to be used for important research initiatives <em>i.e</em> tree censuses and the identification and mapping of crop plant identification. The deep learning model used as inferences for automatization of the identification of the tree species helps to resolve identification and mapping based complex problems in agro-forestry allied fields.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 491-500"},"PeriodicalIF":6.4,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000486/pdfft?md5=7d5fbcbdeb07eaaffc4a98fa4ea681e3&pid=1-s2.0-S1110982324000486-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141294869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}