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Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User's accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions. [ABSTRACT FROM AUTHOR]

The Yellow River Delta (YRD), known for its vast and diverse wetland ecosystem, is the largest estuarine delta in China. However, human activities and climate change have significantly degraded the wetland ecosystem in recent decades in the YRD. Therefore, an understanding of the land use modifications is essential for the efficient management and preservation of ecosystems in this region. This study utilized time series of remote sensing data and the extreme gradient boosting method to generate land use maps of the YRD from 2000 to 2020. Several methods, including transition matrix, land use dynamic degree, and standard deviation ellipse, were employed to explore the characteristics of land use transitions. The results underscore significant spatial variations in land use over the past two decades. The most rapid increase was observed in built-up area, followed by terrestrial water and tidal flats, while unutilized land experienced the fastest decrease, followed by forest–grassland. The spatial distribution patterns of agricultural land, built-up area, terrestrial water, and forest–grassland demonstrated stronger directionality compared to other land use types. The wetlands have expanded in size and improved in structure. Unutilized land has been converted into artificial wetlands comprising ponds, reservoirs, salt ponds, shrimp and crab ponds, and natural wetlands featuring mudflats and forest–grassland. The wetland conservation efforts after 2008 have proven very effective, playing a positive role in ecological and environmental preservation, as well as in regional sustainable development. [ABSTRACT FROM AUTHOR]

Background: Automatic extraction of roads from remote sensing images can facilitate many practical applications. However, thus far, thousands of kilometers or more of roads worldwide have not been recorded, especially low-grade roads in rural areas. Moreover, rural roads have different shapes and are influenced by complex environments and other interference factors, which has led to a scarcity of dedicated low level category road datasets. Methods: To address these issues, based on convolutional neural networks (CNNs) and tranformers, this article proposes the Dual Path Information Fusion Network (DPIF-Net). In addition, given the severe lack of low-grade road datasets, we constructed the GaoFen-2 (GF-2) rural road dataset to address this challenge, which spans three regions in China and covers an area of over 2,300 km, almost entirely composed of low-grade roads. To comprehensively test the low-grade road extraction performance and generalization ability of the model, comparative experiments are carried out on the DeepGlobe, and Massachusetts regular road datasets. Results: The results show that DPIF-Net achieves the highest IoU and F1 score on three datasets compared with methods such as U-Net, SegNet, DeepLabv3+, and D-LinkNet, with notable performance on the GF-2 dataset, reaching 0.6104 and 0.7608, respectively. Furthermore, multiple validation experiments demonstrate that DPIF-Net effectively preserves improved connectivity in low-grade road extraction with a modest parameter count of 63.9 MB. The constructed low-grade road dataset and proposed methods will facilitate further research on rural roads, which holds promise for assisting governmental authorities in making informed decisions and strategies to enhance rural road infrastructure. [ABSTRACT FROM AUTHOR]

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands. [ABSTRACT FROM AUTHOR]

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas. [ABSTRACT FROM AUTHOR]

In recent years, grassland ecosystems have faced increasingly severe desertification, which has caused continuous changes in the vegetation composition in grassland ecosystems. Therefore, effective research on grassland plant taxa is crucial to exploring the process of grassland desertification. This study proposed a solution by constructing a UAV hyperspectral remote sensing system to collect the hyperspectral data of various species in desert grasslands. This approach overcomes the limitations of traditional grassland survey methods such as a low efficiency and insufficient spatial resolution. A streamlined 2D-CNN model with different feature enhancement modules was constructed, and an improved depth-separable convolution approach was used to classify the desert grassland plants. The model was compared with existing hyperspectral classification models, such as ResNet34 and DenseNet121, under the preprocessing condition of data downscaling by combining the variance and F-norm2. The results showed that the model outperformed the other models in terms of the overall classification accuracy, kappa coefficient, and memory occupied, achieving 99.216%, 98.735%, and 16.3 MB, respectively. This model could effectively classify desert grassland species. This method provides a new approach for monitoring grassland ecosystem degradation. [ABSTRACT FROM AUTHOR]

As one of the world's major forestry countries, accurate forest-type maps in China are of great importance for the monitoring and management of forestry resources. Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km 2 , of which 1,127,294.42 km 2 of coniferous forests account for 52% of the total area, 981,690.98 km 2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km 2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development. [ABSTRACT FROM AUTHOR]

Landslide detection is crucial for natural disaster risk management. Deep-learning-based object-detection algorithms have been shown to be effective in landslide studies. However, advanced algorithms currently used for landslide detection require high computational complexity and memory requirements, limiting their practical applicability. In this study, we developed a high-resolution dataset for landslide-prone regions in China by extracting historical landslide remote sensing images from the Google Earth platform. We propose a lightweight LP-YOLO algorithm based on YOLOv5, with a more-lightweight backbone that incorporates our designed PartitionNet and neck equipped with CSPCrossStage. We constructed and added the vertical and horizontal (VH) block to the backbone, which explores and aggregates long-range information with two directions, while consuming a small amount of computational cost. A new feature fusion structure is proposed to boost information flow and enhance the location accuracy. To speed up the model learning process and improve the accuracy, the SCYLLA-IoU (SIoU) bounding box regression loss function was used to replace the complete IoU (CIoU) loss function. The experimental results demonstrated that our proposed model achieved the highest detection performance (53.7% of Precision, 49% of AP50 and 25.5% of AP50:95) with a speed of 74 fps. Compared to the YOLOv5 model, the proposed model achieved 4% improvement for Precision, 2.6% improvement for AP50, and 2.5% for AP50:95, while reducing the model parameters and FLOPs by 38.4% and 53.1%, respectively. The results indicated that the proposed lightweight method provides a technical guidance for achieving reliable and real-time automatic landslide detection and can be used for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Rapid and accurate estimation of forest aboveground biomass (AGB) with fine details is crucial for effective forest monitoring and management, where forest height plays a key role in AGB quantification. In this study, we propose a random forest (RF)-based down-scaling method to map forest height and biomass at a 15-m resolution by integrating Landsat 8 OLI and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data. ICESat-2 photon data are used to derive canopy parameters along 15-m segments, which are considered sample plots for the extrapolation of discrete forest height. Fourteen variables associated with spectral features, textual features and vegetation index are extracted from pan-sharpened Landsat 8 images. A regression function is established between these variables and ICESat-2-derived forest height to produce a 15-m continuous forest height distribution data based on the 30-m forest height product using the RF algorithm. Finally, a wall-to-wall forest AGB at 15-m spatial resolution is achieved by using an allometric model specific to the forest type and height. The Jilin Province in northeast China is taken as the study area, and the forest AGB estimation results reveal a density of 61.15 Mg/ha with a standard deviation of 89.46 Mg/ha. The R 2 between our predicted forest heights and the ICESat-2-derived heights reaches 0.93. Validation results at the county scale demonstrate reasonable correspondence between the estimated AGB and reference data, with consistently high R 2 value exceeding 0.65. This downscaling method provides a promising scheme to estimate spatial forest AGB with fine details and to enhance the accuracy of AGB estimation, which may facilitate carbon stock measurement and carbon cycle studies. [ABSTRACT FROM AUTHOR]

Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data using the Google Earth Engine (GEE) cloud platform. Landsat and Sentinel-2 time series data from 2010–2021 were used to obtain monthly synthetic cloud-free image sets in combination with cropland plot data. The time series farmland probability dataset was generated using the random forest classification method. The LandTrendr algorithm was used to extract and analyse the time series cropland probability dataset. Finally, this study also explored the drivers of change in abandoned cropland in Fujian Province. The results show that (1) the LandTrendr algorithm can effectively extract abandoned farmland and avoid the impact of pseudovariation resulting from non-farmland categories. A total of 87.02% of the abandoned farmland was extracted in 2018; 87.50% of the abandoned farmland was extracted in 2020. (2) The abandoned area in Fujian Province fluctuated after a significant increase in 2012, with the abandoned area exceeding 30 thousand hectares. Since 2017, the abandoned area has decreased to slightly below 30 thousand hectares. (3) The regression results of the factors affecting abandoned cropland in Fujian Province show that the increase in the number of agricultural workers and the improvement in soil organic matter content will significantly reduce the area of abandoned cropland in Fujian Province, while the increase in the rate of urbanization, poor road accessibility, and insufficient irrigation conditions will increase the area of abandoned cropland. The results of this study are useful for conducting surveys of cropland abandonment and obtaining timely and accurate data on cropland abandonment. The results of this study are of great significance for the development of effective measures to stop the abandonment of cropland, and ensure the implementation of food security strategies. [ABSTRACT FROM AUTHOR]

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data. [ABSTRACT FROM AUTHOR]

High-resolution remote-sensing imagery has proven useful for building extraction. Unfortunately, due to the high acquisition costs and infrequent availability of high-resolution imagery, low-resolution images are more practical for large-scale mapping or change tracking of buildings. However, extracting buildings from low-resolution images is a challenging task. Compared with high-resolution images, low-resolution images pose two critical challenges in terms of building segmentation: the effects of fuzzy boundary details on buildings and the lack of local textures. In this study, we propose a sparse geometric feature attention network (SGFANet) based on multi-level feature fusion to address the aforementioned issues. From the perspective of the fuzzy effect, SGFANet enhances the representative boundary features by calculating the point-wise affinity of the selected feature points in a top-down manner. From the perspective of lacking local textures, we convert the top-down propagation from local to non-local by introducing the grounding transformer harvesting the global attention of the input image. SGFANet outperforms competing baselines on remote-sensing images collected worldwide and multiple sensors at 4 and 10 m resolution, thereby, improving the IoU by at least 0.66%. Notably, our method is robust and generalizable, which makes it useful for extending the accessibility and scalability of building dynamic tracking across developing areas (e.g., the Xiong'an New Area in China) by using low-resolution images. [ABSTRACT FROM AUTHOR]

Accurate and efficient extraction of cultivated land data is of great significance for agricultural resource monitoring and national food security. Deep-learning-based classification of remote-sensing images overcomes the two difficulties of traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) when extracting the cultivated land: (1) the limited performance when extracting the same land-cover type with the high intra-class spectral variation, such as cultivated land with both vegetation and non-vegetation cover, and (2) the limited generalization ability for handling a large dataset to apply the model to different locations. However, the "pooling" process in most deep convolutional networks, which attempts to enlarge the sensing field of the kernel by involving the upscale process, leads to significant detail loss in the output, including the edges, gradients, and image texture details. To solve this problem, in this study we proposed a new end-to-end extraction algorithm, a high-resolution U-Net (HRU-Net), to preserve the image details by improving the skip connection structure and the loss function of the original U-Net. The proposed HRU-Net was tested in Xinjiang Province, China to extract the cultivated land from Landsat Thematic Mapper (TM) images. The result showed that the HRU-Net achieved better performance (Acc: 92.81%; kappa: 0.81; F1-score: 0.90) than the U-Net++ (Acc: 91.74%; kappa: 0.79; F1-score: 0.89), the original U-Net (Acc: 89.83%; kappa: 0.74; F1-score: 0.86), and the Random Forest model (Acc: 76.13%; kappa: 0.48; F1-score: 0.69). The robustness of the proposed model for the intra-class spectral variation and the accuracy of the edge details were also compared, and this showed that the HRU-Net obtained more accurate edge details and had less influence from the intra-class spectral variation. The model proposed in this study can be further applied to other land cover types that have more spectral diversity and require more details of extraction. [ABSTRACT FROM AUTHOR]

Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R2 = 0.8485, RMSE = 12.25 Mg/hm2, and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 107 Mg, and the estimation result with the sampling population was 1.24 × 107 Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm2 and 67.51 Mg/hm2, with a relative precision of 98.39%. The estimation results of the two sample groups were consistent. [ABSTRACT FROM AUTHOR]

Timely and accurate survey of construction and demolition waste (C&DW) distribution is of significance for managing C&DW and enhancing the quality of the urban environment, especially in rapidly urbanizing country of China. Automatic C&DW recognition using high-resolution remote sensing images is an extremely important surveying method. Existing C&DW recognition methods can only obtain a rough spatial distribution of C&DW. Mapping precise C&DW distribution on large-scale remote sensing images is still challenging. In this paper, to solve the problem of precise C&DW recognition, a novel deep learning algorithm based on high-resolution remote sensing images is proposed, which we refer to as the cross-channel multi-scale gated fusion network (CCMGNet). CCMGNet extracts depth features of C&DW from RGB and NIR bands by using two independent encoding streams. A gated fusion layer and a multi-scale attention module are designed to effectively fuse the features from two encoding streams. We manually labelled the sample set used for training and testing on 26 remote sensing images captured by the Gaofen-2 (GF-2) satellite. These images were taken in nine typical cities in China over the time period from 2019 to 2021. The results showed that the proposed method is effective in recognizing C&DW, realizing 89.42% and 84.52% in precision and IoU, and is superior to other state-of-the-art deep learning algorithms and existing C&DW recognition methods. The effectiveness of all significant components in CCMGNet was confirmed by ablation experiments. The proposed method was applied to extract C&DW regions in large-scale remote sensing images, with an example of a GF-2 image of Beijing. The high extraction efficiency and satisfactory visual effect demonstrate the potential of the proposed method for surveying the spatial distribution of C&DW. The novel method will play a crucial role in automatically surveying the spatial distribution and size of C&DW across the country. [ABSTRACT FROM AUTHOR]

Mountain areas in China account for 69% of the total land area; however, it is still an urgent that we grasp the special ecological structure of mountain areas and maximize the resource advantages of mountain areas under the principle of maintaining a certain ecological level. In this paper, Landsat 5, Landsat 8 and Sentinel-2A images were used as data sources to monitor and analyze land development and ecological change in Gui 'an in 2010, 2013, 2016, 2019 and 2021, so as to explore ecological maintenance mechanisms. Firstly, random forest classification based on multi-source remote sensing data was used to classify land, and the five phases of land-use change were assessed using quantitative analysis, in order to analyze the mountain region's land-use-change characteristics at different stages of development. The results show that Gui 'an has the "two-stage", rapid-development, rapid-recovery mode. Each stage includes a development-and-expansion period and a construction-and-protection period. In the construction period, ecological recovery construction will be emphasized, and the change intensity and rate of the second stage are lower than that of the first stage. Secondly, using a remote sensing ecological index, vegetation coverage, and a landscape index, an ecological evaluation model of the study area was constructed to analyze changes in the ecological environment and its protection in the process of land development. The ecological maintenance status of the five stages was quantitatively monitored using the analysis methods of difference change and coefficient of variation. The results showed that in the first stage of land development and expansion, the landscape pattern and ecological quality fluctuated greatly, and the proportion of ecological quality of an excellent grade decreased by 28.46%. However, in the second stage, the change slowed down and remained unchanged, and gradually moved to the middle and upper level. The results show that there is a close relationship between ecological maintenance and the land development mode, and new mountain towns can maintain ecological quality and achieve sustainable development through a reasonable land development mode. At the end of this paper, the factors affecting the ecological maintenance capacity of Gui 'an are discussed, providing effective reference material and development models for the development of mountainous areas. [ABSTRACT FROM AUTHOR]

The article evaluates the use of remote sensing based technology for hydrothermal uranium mineralization in South China. Topics discussed include finding the faults with the verification of geological and geophysical information through the visual interpretation of remote sensing image, texture feature of images and structure evaluation and using bright temperature to find the thermal feature of fractures and siliceous vein.

High-resolution optical cameras have always been important scientific payloads in Mars exploration missions, which can obtain detailed images of Martian surface for the study of geomorphology, topography and geological structure. At present, there are still many challenges for Mars high-resolution images in terms of global coverage, stereo coverage (especially for colour images), and data processing methods. High Resolution Imaging Camera (HiRIC) is a high-quality, multi-mode, multi-functional, multi-spectral remote sensing camera that is suitable for the deep space developed for China's first Mars Exploration Mission (Tianwen-1), which was successfully launched in July 2020. Here we design special experiments based on the in-orbit detection conditions of Tianwen-1 mission to comprehensively verify the detection capability and the performance of HiRIC, from the aspects of image motion compensation effect, focusing effect, image compression quality, and data preprocessing accuracy. The results showed that the performance status of HiRIC meets the requirements of obtaining high resolution images on the Martian surface. Furthermore, proposals for HiRIC in-orbit imaging strategy and data processing are discussed to ensure the acquisition of high-quality HiRIC images, which is expected to serve as a powerful complementation to the current Mars high-resolution images. [ABSTRACT FROM AUTHOR]

The article discusses a study on remote sensing image for the recognition of uranium ore district in the Northern Part of North China Craton. It mentions fractal is used in image classification and object identification. It states that ETM images of four uranium ore districts and their neighbor areas revealed districts with the different scale in visual field. It also mentions that texture of remote sensing image represents the faults and gullies of rocks.

Satellite image classification is the core of satellite image interpretation. The traditional shallow learning classification algorithm is difficult to extract remote sensing image features effectively, resulting in low classification accuracy. The deep neural network can automatically realize the feature analysis. However, due to the similarity of the optical properties of the cloud and snow, the existing deep learning model is difficult to obtain satisfactory results for the cloud and snow classification in the plateau. Meanwhile, too many parameters of deep learning lead to slow detection speed, which is difficult to meet the real-time requirements of detection. Furthermore, the deep learning method is prone to model degradation. In view of the existing problems, this paper proposes a dilated multi-scale cascade forest method to realize the classification of satellite cloud images. Multi-scale scanning increases the diversity of feature vector extraction and improves the separability of features. The dilated structure is designed to expand the feature receptive field, it improves the efficiency of feature extraction without losing the texture and spatial correlation information of the original image. In addition, the number of layers of network cascading does not need to be customized, it can be determined automatically according to the performance of the system. The proposed method effectively improves the accuracy of cloud/snow classification. Experiments on the imagery of HuanJing satellites A and B (HJ-1A/1B) in China demonstrate that the proposed model can get better classification results than existing studies. The time complexity of the proposed model is lower than existing works, hence it is practical in real-time and accurate remote sensing image classification. [ABSTRACT FROM AUTHOR]

Terraces are important objects of soil and water conservation and land use surveys in the Loess Plateau and considerably impact soil erosion and agricultural production. Obtaining the spatial distribution of terraces has great significance for soil and water conservation research and government policy-making. A new method of terrace extraction was proposed in this study based on remote sensing images and digital elevation model (DEM) used as experimental data. The extraction method combines Fourier transform and digital terrain analysis. First, by analyzing the spatial and frequency domain characteristics of the terraced image, we determined the analysis window to be 50 × 50, whereas the area having a direction maximum energy value greater than 0.25 and image standard deviation greater than 2000 was considered as the terraced candidate. Then, the positive terrain was automatically extracted from the DEM based on the regional growth method, and the terraces on the positive terrain were obtained. After a series of morphological image processing methods were conducted, the final terraces were extracted. The results showed that the accuracy of drawing and user accuracy of terrace extraction were 79.0% and 73.5%, respectively. Compared with the object-oriented method, it was found that the proposed method was more reliable and accurate. This method developed in this study has the characteristics of simple operation, strong universality, and high precision and can be applied to soil and water conservation monitoring and land use surveys in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images. [ABSTRACT FROM AUTHOR]

Abstract: Humans benefit from multiple ecosystem services of wetlands, but massive wetland loss has occurred worldwide due to rapid urbanization. To assess the problem, it is necessary to quantify the spatial extent of urbanization‐induced wetland loss. Here, we investigated the amount and pattern of wetland loss in China due to urbanization from 1990 to 2010, based on the China National Land Cover Database (ChinaCover). Our results show that, during these 20 years, China lost 2,883 km2 of wetlands to urban expansion, of which about 2,394 km2 took place in the eastern regions (Northeast China, North China, Southeast China, and South China). The rate of urbanization‐induced wetland loss was 2.8 times higher between 2000 and 2010 (213 km2 year−1) than between 1990 and 2000 (75 km2 yr−1). Of all wetland categories, reservoirs/ponds and marshes suffered the most severe losses. Most of the wetland loss was due to the expansion of urban built‐up areas rather than industrial or transportation lands. Four hotspots of urbanization‐induced wetland loss in China were identified: the Beijing–Tianjin metropolitan region, the Yangtze River Delta, the Jianghan Plain, and the Pearl River Delta. Urbanization and industrialization continue to unfold in China for the next few decades, and the rapid expansion of small‐ and middle‐sized cities and urban traffic networks is expected to encroach on more wetlands. Although great efforts have been made towards wetland conservation in recent years, China must prevent more wetlands from being wiped out by urbanization if the country is to ahieve its sustainable development goals. [ABSTRACT FROM AUTHOR]

Wetlands are important ecosystems as they are known as the "kidney of the earth". Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use conversion, air and water pollution, especially those in developing countries undergoing rapid industrialization and urbanization. Therefore, it is of great necessity to derive timely biomass information for optimal design, management and protection of urban wetlands. In this paper, we develop a set of models for estimating above ground biomass (AGB) in Xixi National Wetland Park in Hangzhou, China by using optical images and Synthetic Aperture Radar (SAR) images. A series of vegetation indices (VIs) derived from optical data is introduced along with spectral data. The modeling methods consist of (1) curve estimation; (2) linear regression for multivariable model; (3) Back Propagation Neural Network (BPNN) modeling. Curve estimation is a combination of linear and nonlinear regressions. It is applied to generate AGB models from a single variable including Normalized Difference Vegetation Index (NDVI) and radar backscatter coefficient. The models are then compared via three accuracy metrics. According to the results, SAR models generally show better accuracy than optical models and BPNN models show the greatest accuracy among all the models. The BPNN model from the combination of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and European Remote-Sensing Satellite-2 (ERS-2) SAR (Synthetic Aperture Radar) image has the least root mean square (RMSE, 0.396 kg/m2), least mean absolute error (MAE, 0.256 kg/m2) and the greatest correlation coefficient (0.974). The results indicate that AGB can be estimated by integrating optical and SAR imagery. Four maps of AGB are derived to illustrate the distribution of AGB in the study area. The total AGB in the study area is estimated to be between 165,000 and 210,000 kg/m2. [ABSTRACT FROM AUTHOR]

Poyang Lake, the largest freshwater wetland in China, provides critical habitat for wintering waterbirds from the East Asian Flyway; however, landscape drivers of non-uniform bird diversity and abundance are not yet well understood. Using a winter 2006 waterbird survey, we examined the relationships among metrics of bird community diversity and abundance and landscape characteristics of 51 wetland sub-lakes derived by an object-based classification of Landsat satellite data. Relative importance of predictors and their sets was assessed using information-theoretic model selection and the Akaike Information Criterion. Ordinary least squares regression models were diagnosed and corrected for spatial autocorrelation using spatial autoregressive lag and error models. The strongest and most consistent landscape predictors included Normalized Difference Vegetation Index for mudflat (negative effect) and emergent grassland (positive effect), total sub-lake area (positive effect), and proportion of submerged vegetation (negative effect). Significant spatial autocorrelation in linear regression was associated with local clustering of response and predictor variables, and should be further explored for selection of wetland sampling units and management of protected areas. Overall, results corroborate the utility of remote sensing to elucidate potential indicators of waterbird diversity that complement logistically challenging ground observations and offer new hypotheses on factors underlying community distributions. [ABSTRACT FROM AUTHOR]

Salt cedar is prominent plant species for wind-erosion resistance and saline–alkaline tolerance in arid desert and saline–alkali soil in China. Although some studies have been published for documenting salt cedar dynamics in its invasive habitat in the United States, the study of salt cedar in its native habitat, e.g. China, is relative scarce. Recently, the reduction of salt cedar is remarkable in western China that effective methods for monitoring salt cedar dynamics are essential for ecological environment preservation. As such, this study aims to develop a support vector machine (SVM) to better mapping salt cedar with high spatial resolution remote sensing image. Specifically, our objectives are twofold: (1) to examine the role that textures play in the SVM classification and further derive the optimal window size for extracting textures and (2) to determine optimal values for two vital parametersγandCin the SVM method. To test the effectiveness of the developed SVM method, we compared the classification performance of SVM with three different combinations of input features, i.e. spectra only; spectra and textures; and spectra, textures, and Normalized Differential Vegetation Index (NDVI). In addition, for comparison purposes, a maximum likelihood classification method based on spectra, textures, and NDVI was adopted. Results indicate that (1) incorporation of textures and NDVI, especially textures, refines the SVM classification accuracy to 84.26%; (2) the optimal window size for texture calculation is 31 × 31 pixels; and (3) the optimal parameters of SVM model areC = 32 andγ = 8. Overall, the SVM method incorporating spectra, texture, and NDVI can accurately map salt cedar in our study site. [ABSTRACT FROM AUTHOR]

Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas. [ABSTRACT FROM AUTHOR]

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding-tillering, overwintering, reviving, jointing-heading, and flowering-maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries-Matusita (J-M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing-heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing-heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing-heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation. [ABSTRACT FROM AUTHOR]

It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 104 hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer's accuracy of 89.97%, user's accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest. [ABSTRACT FROM AUTHOR]

Urbanization has been an important component of land use and land cover change. Quantifying land use patterns and their changes is essential for monitoring and assessing the impact of urbanization. Data on existing and changing land use provide crucial clues for future development. Combining the application of remote sensing with geographic information systems (GIS) data, this study accomplishes (i) the creation of time-series land use maps in the core urban area of the city of Hangzhou, China; (ii) the examination of the land use cross-tabulation matrix to assess the transitions between land use types; and (iii) the introduction of a new asymmetric relation analysis method to explore the dependency relationship of different times in land use. The investigation was based on a 12-year time series dataset (1990-2002), compiled from fine-resolution satellite imagery and GIS-based maps. The results of the study showed that, first, there are strong associations between different stages (1990, 1996, and 2002) in land use development, and, secondly, the dependency between land use states was asymmetric in both stages of development. In addition, urbanization in Hangzhou has resulted in notable increases in the surface area of urban land and sharp decreases in the surface area of cropland. [ABSTRACT FROM AUTHOR]

Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users' demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies. [ABSTRACT FROM AUTHOR]

An imageodesy study has been carried out, using pre‐ and post‐event Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) images, to reveal regional co‐seismic displacement caused by the Ms 8.1 Kunlun earthquake in November 2001. The two Landsat scenes, Kusai Lake and Buka Daban, cover an area of some 57 600 km 2 (320 km W–E and about 180 km N–S), which includes most of the fault rupture zone. The co‐seismic displacement measured in the Kusai Lake scene shows that the average left‐lateral shift along the Kunlun fault is 4.8 m (ranging from 1.5 to 8.1 m) and the maximum shift appears west of the Kusai Lake. The splayed nature of the fault to the west of Buka Daban, where the fault splits into three branches, causes the displacement pattern to become complicated. Here the average left‐lateral shift, between the south side of the southern branch and the north side of the northern branch, is 4.6 m (ranging from 1.0 to 8.2 m). Our results also illustrate that the south side of the fault is the ‘active’ block, moving significantly in an east–south‐easterly direction, relative to the largely ‘stable’ northern block. [ABSTRACT FROM AUTHOR]

Plant diversity (PD) plays an important role in maintaining the healthy function of an ecosystem through affecting the productivity, stability, and nutrient utilization of a terrestrial ecosystem. Remote sensing is a vital way to monitor the status and changes of PD. Most of the existing methods rely on a field botany survey to construct a statistical relationship between PD and remote sensing observations. However, a field botany survey is too costly to be applied widely. In this study, we constructed a new remote sensing index of PD (RSPD), combining the spectral variation hypothesis and productivity hypothesis. Concretely, the RSPD integrated the multi-band spectral reflectance and several spectral greenness, moisture, and red-edge vegetation indices with the principles of Shannon information entropy and Euclidean distance. The RSPD was evaluated by comparing the classical coefficient of variation (CV) method and the Shannon and Simpson diversity indices based on vegetation classification results. Two cases were selected, where Case I was in Beijing and Case II was located in part of Huai'an, China. Sentinel-2 data in three years of 2016, 2018, and 2020 and higher-resolution Pléiades-1 data in 2018 were also utilized. The results demonstrate that: (1) the RSPD is basically consistent with the CV in spatiotemporal variation; (2) the RSPD outperforms the CV as compared with Shannon and Simpson diversity indices that are based on vegetation classification results with Sentinel-2 and Pléiades-1 data; (3) the RSPD outperforms the CV as compared with visual interpretations with Google Earth image. The suggested index can reflect the richness and evenness of plant species, which is inherent in its calculation formula. Moreover, it has a great potential for large-scale regional and long-term series monitoring. [ABSTRACT FROM AUTHOR]

Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection. [ABSTRACT FROM AUTHOR]

The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies. [ABSTRACT FROM AUTHOR]

Aquaculture plays an important role in China's total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases. [ABSTRACT FROM AUTHOR]

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications. [ABSTRACT FROM AUTHOR]

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy. [ABSTRACT FROM AUTHOR]

The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R2 = 0.79 and RMSE = 22.82 g/m2) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m−2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management. [ABSTRACT FROM AUTHOR]

The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.–220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.–618 A.D.) and Ming Dynasty (1368 A.D.–1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future. [ABSTRACT FROM AUTHOR]

Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs. [ABSTRACT FROM AUTHOR]