Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneit...Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.展开更多
Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at diff...Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.展开更多
While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based regi...This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.展开更多
The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great...The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.展开更多
The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has ...The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.展开更多
The components of urban surface cover are diversified,and component temperature has greater physical significance and application values in the studies on urban thermal environment.Although the multi-angle retrieval a...The components of urban surface cover are diversified,and component temperature has greater physical significance and application values in the studies on urban thermal environment.Although the multi-angle retrieval algorithm of component temperature has been matured gradually,its application in the studies on urban thermal environment is restricted due to the difficulty in acquiring urban-scale multi-angle thermal infrared data.Therefore,based on the existing multi-source multi-band remote sensing data,access to appropriate urban-scale component temperature is an urgent issue to be solved in current studies on urban thermal infrared remote sensing.Then,a retrieval algorithm of urban component temperature by multi-source multi-band remote sensing data on the basis of MODIS and Landsat TM images was proposed with expectations achieved in this work,which was finally validated by the experiment on urban images of Changsha,China.The results show that：1） Mean temperatures of impervious surface components and vegetation components are the maximum and minimum,respectively,which are in accordance with the distribution laws of actual surface temperature; 2） High-accuracy retrieval results are obtained in vegetation component temperature.Moreover,through a contrast between retrieval results and measured data,it is found that the retrieval temperature of impervious surface component has the maximum deviation from measured temperature and its deviation is greater than 1 ℃,while the deviation in vegetation component temperature is relatively low at 0.5 ℃.展开更多
[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IR...[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.展开更多
Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing.Complex ground environments such as construction and demolition(C&D)waste landfil...Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing.Complex ground environments such as construction and demolition(C&D)waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns.Conventional convolutional neural networks(CNNs)are limited by their local receptive fields,whereas Transformer-based architectures often lose fine spatial detail,resulting in incomplete delineation of heterogeneous remote sensing targets.To address these issues,we propose a global-local collaborative network(GLC-Net),which is designed for intelligent remote sensing image segmentation.The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details.Furthermore,a multi-scale spatial aggregation and enhancement(MSAE)module is introduced to strengthen contextual representation and suppress background noise.Deep supervision facilitates hierarchical feature learning.Experiments on two high-resolution remote sensing datasets(Changping and Daxing)demonstrate that GLC-Net surpasses state-of-the-art baselines by 1.5%-3.2%in mean intersection over union(mIoU),while achieving superior boundary precision and semantic consistency.These results confirm that global-local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.展开更多
Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric featur...Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.展开更多
Remote sensing technologies play a vital role in our understanding of earthquakes and their impact on the Earth's surface.These technologies,including satellite imagery,aerial surveys,and advanced sensors,contribu...Remote sensing technologies play a vital role in our understanding of earthquakes and their impact on the Earth's surface.These technologies,including satellite imagery,aerial surveys,and advanced sensors,contribute significantly to our understanding of the complex nature of earthquakes.This review highlights the advancements in the integration of remote sensing technologies into earthquake studies.The combined use of satellite imagery and aerial photography in conjunction with geographic information systems(GIS)has been instrumental in showcasing the significance of fusing various types of satеllitеdata sourcеs for comprеhеnsivееarthquakеdamagеassеssmеnts.However,remote sensing encounters challenges due to limited pre-event imagery and restricted postearthquake site access.Furthеrmorе,thеapplication of dееp-lеarning mеthods in assеssingеarthquakе-damagеd buildings dеmonstratеs potеntial for furthеr progrеss in this fiеld.Overall,the utilization of remote sensing technologies has greatly enhanced our comprehension of earthquakes and their effects on the Earth's surface.The fusion of remote sensing technology with advanced data analysis methods holds tremendous potential for driving progress in earthquake studies and damage assessment.展开更多
Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing tech...Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.展开更多
This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lin...This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.展开更多
The Lake Chad located in the west-central Africa in the Sahel region at the edge of the Sahara experienced severe drought during 1970s and 1980s and overexploitation (unintegrated and unsustainable use), which is a re...The Lake Chad located in the west-central Africa in the Sahel region at the edge of the Sahara experienced severe drought during 1970s and 1980s and overexploitation (unintegrated and unsustainable use), which is a result of variant land uses and water management practices during the last 50 years. This resulted in a decline of the water level in the Lake and surrounding rivers. The present study analyzed satellite images of Lake Chad from Landsat-MSS, Landsat-OLI to investigate the change of the open water surface area during the years of 1973, 1987, 2001, 2013, and 2017. Supervised classifications were performed for the land cover analysis. The open water area in 1973 was covering 16,157.34 km<sup>2</sup> approximately, and that was 64.6% of the total lake area in the 1960s. As an ultimate result of the extreme drought that the study area witnessed through 1970s-1980s, the open water area has decreased to 1831.44 km<sup>2</sup>, <i>i.e.</i> around 11.33%, compared to that in 1973. The dilemma that the study area is suffering from is believed to be a catastrophic complication of the aforementioned drought crisis, which arose as an ultimate result the climate change, global warming, and the unintegrated and unsustainable use of water challenges the study area is still encountering.展开更多
UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface info...UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.展开更多
Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow ...Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition®. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.展开更多
Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose ...Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.展开更多
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications...Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.展开更多
Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carb...Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.展开更多
基金Supported by the National Natural Science Foundation of China(42401488,42071351)the National Key Research and Development Program of China(2020YFA0608501,2017YFB0504204)+4 种基金the Liaoning Revitalization Talents Program(XLYC1802027)the Talent Recruited Program of the Chinese Academy of Science(Y938091)the Project Supported Discipline Innovation Team of the Liaoning Technical University(LNTU20TD-23)the Liaoning Province Doctoral Research Initiation Fund Program(2023-BS-202)the Basic Research Projects of Liaoning Department of Education(JYTQN2023202)。
文摘Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.
基金supported by the National Natural Science Foundation of China(42030102,42371321).
文摘Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
文摘This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.
基金This work was supported by the National Advance Research Program(Item No.Y1601-1).
文摘The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.
文摘The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.
基金Projects(41171326,40771198)supported by the National Natural Science Foundation of ChinaProject(08JJ6023)supported by the Natural Science Foundation of Hunan Province,China
文摘The components of urban surface cover are diversified,and component temperature has greater physical significance and application values in the studies on urban thermal environment.Although the multi-angle retrieval algorithm of component temperature has been matured gradually,its application in the studies on urban thermal environment is restricted due to the difficulty in acquiring urban-scale multi-angle thermal infrared data.Therefore,based on the existing multi-source multi-band remote sensing data,access to appropriate urban-scale component temperature is an urgent issue to be solved in current studies on urban thermal infrared remote sensing.Then,a retrieval algorithm of urban component temperature by multi-source multi-band remote sensing data on the basis of MODIS and Landsat TM images was proposed with expectations achieved in this work,which was finally validated by the experiment on urban images of Changsha,China.The results show that：1） Mean temperatures of impervious surface components and vegetation components are the maximum and minimum,respectively,which are in accordance with the distribution laws of actual surface temperature; 2） High-accuracy retrieval results are obtained in vegetation component temperature.Moreover,through a contrast between retrieval results and measured data,it is found that the retrieval temperature of impervious surface component has the maximum deviation from measured temperature and its deviation is greater than 1 ℃,while the deviation in vegetation component temperature is relatively low at 0.5 ℃.
基金Supported by Science and Technology Project of Lianyungang City(SH0917)
文摘[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.
基金supported by the“Fourteenth Five-Year”National Key R&D Program of Chi-na(No.2024YFC3906501)the New Cornerstone Sci-ence Foundation through the XPLORER PRIZE.
文摘Intelligent interpretation of high-resolution remote sensing imagery is a fundamental challenge in aerospace information processing.Complex ground environments such as construction and demolition(C&D)waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns.Conventional convolutional neural networks(CNNs)are limited by their local receptive fields,whereas Transformer-based architectures often lose fine spatial detail,resulting in incomplete delineation of heterogeneous remote sensing targets.To address these issues,we propose a global-local collaborative network(GLC-Net),which is designed for intelligent remote sensing image segmentation.The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details.Furthermore,a multi-scale spatial aggregation and enhancement(MSAE)module is introduced to strengthen contextual representation and suppress background noise.Deep supervision facilitates hierarchical feature learning.Experiments on two high-resolution remote sensing datasets(Changping and Daxing)demonstrate that GLC-Net surpasses state-of-the-art baselines by 1.5%-3.2%in mean intersection over union(mIoU),while achieving superior boundary precision and semantic consistency.These results confirm that global-local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.
基金financially supported by the Youth Innovation Promotion Association CAS(No.2021325)the National Natural Science Foundation of China(Nos.52179117,U21A20159)the Research project of Panzhihua Iron and Steel Group Mining Co.,Ltd.(No.2021-P6-D2-05)。
文摘Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.
基金funded through an appointment with the Agricultural Research Service,managed by the Oak Ridge Institute for Science and Education。
文摘Remote sensing technologies play a vital role in our understanding of earthquakes and their impact on the Earth's surface.These technologies,including satellite imagery,aerial surveys,and advanced sensors,contribute significantly to our understanding of the complex nature of earthquakes.This review highlights the advancements in the integration of remote sensing technologies into earthquake studies.The combined use of satellite imagery and aerial photography in conjunction with geographic information systems(GIS)has been instrumental in showcasing the significance of fusing various types of satеllitеdata sourcеs for comprеhеnsivееarthquakеdamagеassеssmеnts.However,remote sensing encounters challenges due to limited pre-event imagery and restricted postearthquake site access.Furthеrmorе,thеapplication of dееp-lеarning mеthods in assеssingеarthquakе-damagеd buildings dеmonstratеs potеntial for furthеr progrеss in this fiеld.Overall,the utilization of remote sensing technologies has greatly enhanced our comprehension of earthquakes and their effects on the Earth's surface.The fusion of remote sensing technology with advanced data analysis methods holds tremendous potential for driving progress in earthquake studies and damage assessment.
基金Under the auspices of the National Key Research and Development Program of China(No.2023YFC3208500)Shanghai Municipal Natural Science Foundation(No.22ZR1421500)+3 种基金National Natural Science Foundation of China(No.U2243207)National Science and Technology Basic Resources Survey Project(No.2023FY01001)Open Research Fund of State Key Laboratory of Estuarine and Coastal Research(No.SKLEC-KF202406)Project from Science and Technology Commission of Shanghai Municipality(No.22DZ1202700)。
文摘Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.
文摘This paper presents algorithmic components and corresponding software routines for extracting shoreline features from remote sensing imagery and LiDAR data. Conceptually, shoreline features are treated as boundary lines between land objects and water objects. Numerical algorithms have been identified and de-vised to segment and classify remote sensing imagery and LiDAR data into land and water pixels, to form and enhance land and water objects, and to trace and vectorize the boundaries between land and water ob-jects as shoreline features. A contouring routine is developed as an alternative method for extracting shore-line features from LiDAR data. While most of numerical algorithms are implemented using C++ program-ming language, some algorithms use available functions of ArcObjects in ArcGIS. Based on VB .NET and ArcObjects programming, a graphical user’s interface has been developed to integrate and organize shoreline extraction routines into a software package. This product represents the first comprehensive software tool dedicated for extracting shorelines from remotely sensed data. Radarsat SAR image, QuickBird multispectral image, and airborne LiDAR data have been used to demonstrate how these software routines can be utilized and combined to extract shoreline features from different types of input data sources: panchromatic or single band imagery, color or multi-spectral image, and LiDAR elevation data. Our software package is freely available for the public through the internet.
文摘The Lake Chad located in the west-central Africa in the Sahel region at the edge of the Sahara experienced severe drought during 1970s and 1980s and overexploitation (unintegrated and unsustainable use), which is a result of variant land uses and water management practices during the last 50 years. This resulted in a decline of the water level in the Lake and surrounding rivers. The present study analyzed satellite images of Lake Chad from Landsat-MSS, Landsat-OLI to investigate the change of the open water surface area during the years of 1973, 1987, 2001, 2013, and 2017. Supervised classifications were performed for the land cover analysis. The open water area in 1973 was covering 16,157.34 km<sup>2</sup> approximately, and that was 64.6% of the total lake area in the 1960s. As an ultimate result of the extreme drought that the study area witnessed through 1970s-1980s, the open water area has decreased to 1831.44 km<sup>2</sup>, <i>i.e.</i> around 11.33%, compared to that in 1973. The dilemma that the study area is suffering from is believed to be a catastrophic complication of the aforementioned drought crisis, which arose as an ultimate result the climate change, global warming, and the unintegrated and unsustainable use of water challenges the study area is still encountering.
基金Supported by College Students Innovation and Entrepreneurship Training Program of Jilin University(No.202010183695)。
文摘UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.
文摘Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition®. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.
基金funded by the Chongqing Normal University Startup Foundation for PhD(22XLB021)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2023B40).
文摘Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.
基金Supported by the National Natural Science Foundation of China(Nos.42376185,41876111)the Shandong Provincial Natural Science Foundation(No.ZR2023MD073)。
文摘Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.
基金supported by the CAS Strategic Priority Research Program(No.XDA19030402)the National Key Research and Development Program of China(No.2016YFD0300101)+2 种基金the Natural Science Foundation of China(Nos.31571565,31671585)the Key Basic Research Project of the Shandong Natural Science Foundation of China(No.ZR2017ZB0422)Research Funding of Qingdao University(No.41117010153)
文摘Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.
