Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

Feature extraction and analysis of reclaimed vegetation in ecological restoration area of abandoned mines based on hyperspectral remote sensing images

The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.

A REMOTE SENSING MODEL FOR DETERMINING CHLOROPHYLL CONTENT AND ITS DISTRIBUTION USING LANDSAT IMAGES

The uncertainty in the estimatation of chlorophyll content with the use of normalized difference vegetation index (NDVI) has been described. To determine the chlorophyll content, model 1 for LANDSAT and model 2 for NOAA AVHRR wavebands were presented and have been verified by field experiments. Model 1 was also validated by the distribution of chlorophyll content using LANDSAT images around the Yucheng remote sensing experimental station. Using these models to estimate the chlorophyll content in the vegetation community is benefitiated by the increased precision and decreased uncertainty.

Forest Resources Management Information System for Forest Farms Based on Remote Sensing Images and Web GIS

This study was to estabIish the forest resources management information system for forest farms based on the B/S structural WebGIS with trial forest farm of Hunan Academy of Forestry as the research field, forest resources field survey da-ta, ETM＋ remote sensing data and basic geographical information data as research material through the extraction of forest resource data in the forest farm, require-ment analysis on the system function and the estabIishment of required software and hardware environment, with the alm to realize the management, query, editing, analysis, statistics and other functions of forest resources information to manage the forest resources.

Remote sensing of air pollution incorporating integrated-path differential-absorption and coherent-Doppler lidar

An innovative complex lidar system deployed on an airborne rotorcraft platform for remote sensing of atmospheric pollution is proposed and demonstrated.The system incorporates integrated-path differential absorption lidar(DIAL) and coherent-doppler lidar(CDL) techniques using a dual tunable TEA CO_(2)laser in the 9—11 μm band and a 1.55 μm fiber laser.By combining the principles of differential absorption detection and pulsed coherent detection,the system enables agile and remote sensing of atmospheric pollution.Extensive static tests validate the system’s real-time detection capabilities,including the measurement of concentration-path-length product(CL),front distance,and path wind speed of air pollution plumes over long distances exceeding 4 km.Flight experiments is conducted with the helicopter.Scanning of the pollutant concentration and the wind field is carried out in an approximately 1 km slant range over scanning angle ranges from 45°to 65°,with a radial resolution of 30 m and10 s.The test results demonstrate the system’s ability to spatially map atmospheric pollution plumes and predict their motion and dispersion patterns,thereby ensuring the protection of public safety.

A New Fusion Technique of Remote Sensing Images for Land Use/Cover

In China, accelerating industrialization and urbanization followinghigh-speed economic development and population increases have greatly impacted land use/coverchanges, making it imperative to obtain accurate and up to date iufbimation on changes soas toevaluate their environmental effects. The major purpose of this study was to develop a new method tofuse lower spatial resolution multispectral satellite images with higher spatial resolutionpanchromatic ones to assist in land use/cover mapping.An algorithm of a new fusion method known asedge enhancement intensity modulation (EEIM) was proposed to merge two optical image data sets ofdifferent spectral ranges. The results showed that the EEIM image was quite similar in color tolower resolution multispectral images, and the fused product was better able to preserve spectralinformation. Thus, compared to conventional approaches, the spectral distortion of the fused imageswas markedly reduced. Therefore, the EEIM fusion method could be utilized to fuse remote sensingdata from the same or different sensors, including TM images and SPOT5 panchromatic images,providing high quality land use/cover images.

Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China

Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.

Experimental investigation on the optical remote sensing images of internal solitary waves with a smooth surface

The parameter inversion of internal solitary waves (ISWs) based on optical remote sensing images is a key work. A new approach is proposed and demonstrated for simulating the optical remote sensing images of ISWs with a smooth surface in the laboratory. An optical remote sensing simulation system used to detect ISWs is constructed by a two-dimensional ISW flume, a LED (light emitting diode) light source and two CCD (charge coupled device) cameras. The optical remote sensing images of the horizontal surface and ISWs propagation images of a vertical side are detected simultaneously, which aims to explore the response of optical remote sensing corresponding to ISWs with the smooth surface. The results show that during the propagation of ISWs, dark pattern images are obtained by CCD 1 camera. The characteristics of the dark patterns vary along with the incident angle of the light source. The characteristic parameters of the optical remote sensing images correspond to the wave factors of vertical profiles. The experiment also shows a positive correlation between the dark pattern width and the half wave width under different amplitudes of ISWs. The system has the advantages of clear phenomenon and high repeatability, which provides the scientific basis for quantitative investigation on imaging mechanism of ISW by optical remote sensing.

A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images.The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space,cost savings.However,the openness of cloud brings challenges for image data security.In this paper,we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment,which takes the weights of participants(i.e.,cloud service providers)into consideration.An extended Mignotte sequence is constructed according to the weights of participants,and we can generate image shadow shares based on the hash value which can be obtained from gray value of remote sensing images.Then we store the shadows in every cloud service provider,respectively.At last,we restore the remote sensing image based on the Chinese Remainder Theorem.Experimental results show the proposed scheme can effectively realize the secure storage of remote sensing images in the cloud.The experiment also shows that no matter weight values,each service providers only needs to save one share,which simplifies the management and usage,it also reduces the transmission of secret information,strengthens the security and practicality of this scheme.

Improved Region Merging Algorithm for Remote Sensing Images

To segment high-resolution remote sensing images(RSIs)accurately on an object level and meet the precise boundary dividing requirement,an improved superpixel segmentation and region merging algorithm is proposed.Simple linear iterative clustering(SLIC)is widely used because of its advantages in performance and effect;however,it causes over-segmentation,which is very disadvantageous to information extraction.In this proposed method,SLIC is firstly adopted for initial superpixel partition.The second stage follows the iterative merging procedure,which uses a hierarchical clustering algorithm and introduces a local binary pattern(LBP)texture feature operator during the process of merging.The experimental results indicate that the proposed method achieved a good segmentation and region merging performance,and worked effectively on cloud detection preprocessing in high-resolution RSIs with cloud and snow overlap situations.

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various domains.Dehazing methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing imagery.This enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target iden-tification.Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images.In response to this challenge,a novel UNet Residual Attention Network(URA-Net)is proposed.This paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump connections.The essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual demands.The intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze removal.Empirical validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image defogging.On the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 dB.Particularly noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yielding defogged images characterized by consistent visual quality.This underscores the contribution of the research to the advancement of remote sensing technology,providing a robust and efficient solution for alleviating the adverse effects of haze on image quality.

Analysis of Remote Sensing Images of Ground Ruptures Resulting from the Kunlun Mountain Pass Earthquake in 2001

On November 14, 2001, an earthquake measuring a magnitude of 8.1 occurred to the west of the Kunlun Mountain Pass which is near the border between Xinjiang and Qinghai of China. Since its epicenter is located in an area at an elevation of 4900 m where the environment is extremely adverse, field investigation to this event seems very difficult. We have performed interpretation and analysis of the satellite images of ETM, SPOT, Ikonos, and ERS-1/2SAR to reveal the spatial distribution and deformation features of surface ruptures caused by this large earthquake. Our results show that the rupture zone on the ground is 426 km long, and strikes N90-110°E with evident left-lateral thrusting. In spatial extension, it has two distinct sections. One extends from the Bukadaban peak to the Kunlun Mountain Pass, with a total length of 350 km, and trending N95-110°E. Its fracture plane is almost vertical, with clear linear rupture traces and a single structure, and the maximum left-lateral offset is 7.8 m. This section is the main rupture zone caused by the earthquake, which is a re-fracturing along an old fault. The other is the section from Kushuihuan to the Taiyang Lake. It is 26 km long, trending N90-105°E, with the maximum strike-slip displacement being 3 m, and is a newly-generated seismic rupture. In a 50 km-long section between the Taiyang Lake and the Bukadaban peak, no rupture is found on the ground. The eastern and western rupture zones may have resulted from two earthquakes. The macroscopic epicenter is situated at 65 km east of the Hoh Sai Lake. The largest coseismic horizontal offset in the macroscopic epicenter ranges from 7 m to 8 m. Based on the dislocation partition of the whole rupture zone, it is suggested that this rupture zone has experienced a process of many times of intensification and fluctuation, exhibiting a remarkable feature of segmentation.

Dynamic monitoring of surface water in the Tibetan Plateau from 1980s to 2019 based on satellite remote sensing images

The Tibetan Plateau is known as the“Asian water tower”,and changes in its surface water distribution are important indicators of global climate change and the regional response to these changes.Dynamic monitoring of the surface water on the Tibetan Plateau is an important part of the research on the functions of the“third pole”of the earth and the Asian water tower.With the support of the Google Earth Engine cloud platform,this study used a spectral index-based fast extraction method to obtain surface water data from multi-temporal Landsat(Landsat 4,5,and 8)satellite remote sensing images.Based on the extracted surface water data,we analyzed the spatiotemporal variations in the surface water of the Tibetan Plateau from 1980s to 2019.In this study,surface water area refers to the maximum coverage area of the surface water extracted from remote sensing images for one year,hereafter referred to as the surface water area.The results show that since 1980s,the overall surface water area of the Tibetan Plateau has increased,but not in a linear fashion.After a slight decrease from 1980s to 1995,the surface water area of the Tibetan Plateau increased steadily,except for a slight decrease in 2015,which may have been caused by the El Niño phenomenon.In terms of spatiotemporal distribution,different patterns exist in the various ecological regions of the Tibetan Plateau.The Inner ecological region had the greatest changes of surface water area among the ten ecological regions,accounting for 71.0%of the total surface water area increase from 1980s to 2019.The surface water bodies in the cold desert and the dry-winter subtropical climatic regions underwent the most changes,with their coefficients of variation being more than 20%.This study can provide data support for dynamic monitoring of surface water in the Tibetan Plateau.

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

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.

A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

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.