GF-1 Satellite Images Research Articles

Research on Gating Fusion Algorithm for Power Grid Survey Data Based on Enhanced Mamba Spatial Neighborhood Relationship.

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This study introduces a fusion model specifically tailored for power grid surveying that significantly enhances the representation of spatial-spectral features in remote sensing images. The model comprises three main modules: a TransforRS-Mamba module that integrates the sequence processing capabilities of the Mamba model with the attention mechanism of the Transformer to effectively merge spatial and spectral features; an improved spatial proximity-aware attention mechanism (SPPAM) that utilizes a spatial constraint matrix to greatly enhance the recognition of complex object relationships; and an optimized spatial proximity-constrained gated fusion module (SPCGF) that integrates spatial proximity constraints with residual connections to boost the recognition accuracy of key object features. To validate the effectiveness of the proposed method, extensive comparative and ablation experiments were conducted on GF-2 satellite images and the QuickBird (QB) dataset. Both qualitative and quantitative analyses indicate that our method outperforms 11 existing methods in terms of fusion effectiveness, particularly in reducing spectral distortion and spatial detail loss. However, the model's generalization performance across different data sources and environmental conditions has yet to be evaluated. Future research will explore the integration of various satellite datasets and assess the model's performance in diverse environmental contexts.

Spatial-Temporal Evolution Characteristics Analysis of Color Steel Buildings in Lanzhou City

With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions.

Comparison of Extraction accuracy of Sugarcane from different resolution satellite images using Deep lab V3+ Mode

Abstract. Sugarcane is an annual or perennial persistent rooted tropical and subtropical herb that grows in tropical and subtropical regions. As China's production ranks among the world's leading, sugarcane industry is an important part of agricultural economy in China. As the largest sugarcane production center in China, Guangxi is one of the most suitable areas for sugarcane cultivation in China and even in the world. Sugarcane industry, as an agricultural advantageous industry in Guangxi, not only has a significant image to the national economy of the region, but also is closely related to the issue of security of national sugar supply. Continuous cropping of sugarcane is very common in Guangxi, which is very helpful for the concentration selection of sugarcane samples. The wide application of satellite remote sensing monitoring technology has become an indispensable means of natural resources monitoring. Using optical satellite remote sensing image to identify and extract sugarcane planting areas is of great significance to quickly and conveniently grasp the information of sugarcane distribution and yield. In this paper, the precision of sugarcane extraction from GF1 and GF2 satellite images is analyzed by using deeplab V3 + model, the effect of optical remote sensing images with different resolution on sugarcane extraction accuracy was studied to provide better data support for dynamic monitoring of sugarcane planting.

Detection and classification of Brandt’s vole burrow clusters utilizing GF-2 satellite imagery and faster R-CNN model

Most small rodent populations worldwide exhibit fascinating population dynamics, capturing the attention of numerous scholars due to their multiyear cyclic fluctuations in population size and the astonishing amplitude of these fluctuations. Hulunbuir steppe stands as a crucial global hub for livestock production, yet in recent decades, the area has faced recurring challenges from steppes rodent invasions, with Brandt’s vole (Lasiopodomys brandtii, BV) being particularly rampant among them. They not only exhibit seasonal reproduction but also strong social behavior, and are generally considered pests, especially during population outbreak years. Prior studies suggest that BV population outbreaks tend to occur across a wider geographic area, and a strong indicator for identifying rodent outbreaks is recognizing their burrow clusters (burrow systems). Hence, this paper conducts target object detection of BV burrow clusters in the typical steppes of Hulunbuir using two GF-2 satellite images from 2021 (the year of the BV outbreak). This task is accomplished by incorporating the Faster R-CNN model in combination with three detection approaches: object-based image classification (OBIC), based on vegetation index classification (BVIC), and based on texture classification (BTC). The results indicate that OBIC demonstrated the highest robustness in BV burrow cluster detection, achieving an average AP of 63.80% and an F1 score of 0.722 across the two images. BTC exhibited the second-highest level of accuracy, achieving an average AP of 55.95% and an F1 score of 0.6660. Moreover, this approach displayed a strong performance in BV burrow clusters localization. In contrast, BVIC achieved the lowest level of accuracy among the three methods, with an average AP of only 29.45% and an F1 score of 0.4370. Overall, this study demonstrates the crucial role of utilizing high-resolution satellite imagery combined with DL-based object detection techniques in effectively monitoring and managing the potential outbreaks of steppe rodent pests across larger spatial extents.

Multisource High-Resolution Remote Sensing Image Vegetation Extraction with Comprehensive Multifeature Perception

High-resolution remote sensing image-based vegetation monitoring is a hot topic in remote sensing technology and applications. However, when facing large-scale monitoring across different sensors in broad areas, the current methods suffer from fragmentation and weak generalization capabilities. To address this issue, this paper proposes a multisource high-resolution remote sensing image-based vegetation extraction method that considers the comprehensive perception of multiple features. First, this method utilizes a random forest model to perform feature selection for the vegetation index, selecting an index that enhances the otherness between vegetation and other land features. Based on this, a multifeature synthesis perception convolutional network (MSCIN) is constructed, which enhances the extraction of multiscale feature information, global information interaction, and feature cross-fusion. The MSCIN network simultaneously constructs dual-branch parallel networks for spectral features and vegetation index features, strengthening multiscale feature extraction while reducing the loss of detailed features by simplifying the dense connection module. Furthermore, to facilitate global information interaction between the original spectral information and vegetation index features, a dual-path multihead cross-attention fusion module is designed. This module enhances the differentiation of vegetation from other land features and improves the network’s generalization performance, enabling vegetation extraction from multisource high-resolution remote sensing data. To validate the effectiveness of this method, we randomly selected six test areas within Anhui Province and compared the results with three different data sources and other typical methods (NDVI, RFC, OCBDL, and HRNet). The results demonstrate that the MSCIN method proposed in this paper, under the premise of using only GF2 satellite images as samples, exhibits robust accuracy in extraction results across different sensors. It overcomes the rapid degradation of accuracy observed in other methods with various sensors and addresses issues such as internal fragmentation, false positives, and false negatives caused by sample generalization and image diversity.

The interaction between temperature and precipitation on the potential distribution range of Betula ermanii in the alpine treeline ecotone on the Changbai Mountain

Alpine treeline ecotones are highly sensitive to climate warming. The low temperature-determined alpine treeline is expected to shift upwards in response to global warming. However, little is known about how temperature interacts with other important factors to influence the distribution range of tree species within and beyond the alpine treeline ecotone. Hence, we used a GF-2 satellite image, along with bioclimatic and topographic variables, to develop an ensemble suitable habitat model based on the species distribution modeling algorithms in Biomod2. We investigated the distribution of suitable habitats for B. ermanii under three climate change scenarios (i.e., low (SSP126), moderate (SSP370) and extreme (SSP585) future emission trajectories) between two consecutive time periods (i.e., current–2055, and 2055–2085). By 2055, the potential distribution range of B. ermanii will expand under all three climate scenarios. The medium and high suitable areas will decline under SSP370 and SSP585 scenarios from 2055 to 2085. Moreover, under the three climate scenarios, the uppermost altitudes of low suitable habitat will rise to 2,329 ​m a.s.l., while the altitudes of medium and high suitable habitats will fall to 2,201 and 2,051 ​m a.s.l. by 2085, respectively. Warming promotes the expansion of B. ermanii distribution range in Changbai Mountain, and this expansion will be modified by precipitation as climate warming continues. This interaction between temperature and precipitation plays a significant role in shaping the potential distribution range of B. ermanii in the alpine treeline ecotone. This study reveals the link between environmental factors, habitat distribution, and species distribution in the alpine treeline ecotone, providing valuable insights into the impacts of climate change on high-elevation vegetation, and contributing to mountain biodiversity conservation and sustainable development.

Study on Remote Sensing Image Coverage Frequency Based on Spatial Grid

The coverage frequency analysis of remote sensing images is helpful to analyse the data acquisition capability of satellites or the vigorous users’ demand. In the current research, thermal values are mostly calculated using an individual point or surface target. In this paper, an index RSICF is proposed, which is calculated based on two shapes, image vectors, and spatial grid, to measure the frequency of remote sensing image coverage. The study uses GF-1 satellite image vectors to calculate RSICF around China to verify its availability. The result shows RSICF can reflect the distribution of historical image frequency respectively and can be introduced by creating a heatmap of remote sensing image coverage.

How can trees protect us from air pollution and urban heat? Associations and pathways at the neighborhood scale

With rapidly increasing urbanization, cities frequently suffer from the development of urban pollution and heat islands, the co-occurrence of which increases the vulnerability of cities and threatens public health. Greening practices, particularly the plantation of trees, have become an effective strategy for enhancing the quality of urban environments. However, extensive studies examining the effects of urban greening tend to focus on the singular contribution of trees to mitigating either urban air pollution or heat. In this study, the metropolis of Shanghai, China was selected as a study case to explore associations between particulate matters with aerodynamic diameters ≤10 μm (PM10) and heat mitigation by urban trees. During the summer months, we obtained hourly PM10 concentration and meteorological data from environmental monitoring stations in Shanghai, and land cover data were derived from 0.8-m-resolution GF-2 satellite images. Regression analysis indicated a negative non-linear logarithmic pattern between canopy cover and PM10 or heat indicators within neighborhoods. Mediation analysis confirmed the significant mediatory role of heat mitigation pathways, particularly extreme heat mitigation on PM10 reduction from urban trees. The proportion of indirect PM10mean mitigation effects of urban trees associated with reductions in average temperature, maximum temperature, extreme heat days, and extreme heat hours amounted to 27.11%, 27.59%, 30.61%, and 39.76%, respectively. Different results were obtained when using PM10max as the dependent variable, with only extreme heat events (EHEs) pathways being significant, whereas the proportion of indirect effects associated with extreme heat days and extreme heat hours accounted for 25.89% and 37.08%, respectively. Overall, mitigating extreme heat events makes a larger contribution to air purification than temperature regulation. Our findings enrich and extend the current understanding of associations between the air purification and cooling effects of urban trees, and highlight the potential co-benefits for effectively dealing with air pollution and urban heat in high-density cities.

An integrated approach to reconstructing snow cover under clouds and cloud shadows on Sentinel-2 Time-Series images in a mountainous area

Snow is an important type of surface cover and an important freshwater resource in the arid regions of northwest China, and optical satellites are an important means for monitoring variation in snow cover. However, the radiation values measured by satellite sensors are affected by cloud cover and cloud shadows; thus, the reconstruction of snow cover under clouds and cloud shadows becomes a bottleneck problem especially in mountains areas. Based on the digital elevation model (DEM), in this paper, an integrated method of snow cover reconstruction on Sentinel-2 images is created by combining the improved SNOWL (snow line) algorithm considering unstable snow cover areas, Fmask cloud detection algorithms, and the Sen2Cor clear sky snow cover detection tool with a geospatial processing platform, GEE (Google Earth Engine). Considering the Babao River Basin as the study area, 474 Sentinel-2 scenes (10 m) were selected to obtain the snow cover spatiotemporal variations of 158 days from November 2016 to March 2021. Compared with the high-resolution GF-2 satellite images, the integrated method can extract snow under clouds and cloud shadows well, and the improved SNOWL algorithm improves the overall accuracy from 66.05 % to 84.26 %. The experimental results show that approximately half of the Babao River Basin is unstable snow cover, and areas above 3977 m are covered with snow for at least half a year.

A Transformer-Based Neural Network with Improved Pyramid Pooling Module for Change Detection in Ecological Redline Monitoring

The ecological redline defines areas where industrialization and urbanization development should be prohibited. Its purpose is to establish the most stringent environmental protection system to meet the urgent needs of ecological function guarantee and environmental safety. Nowadays, deep learning methods have been widely used in change detection tasks based on remote sensing images, which can just be applied to the monitoring of the ecological redline. Considering the convolution-based neural networks’ lack of utilization of global information, we choose a transformer to devise a Siamese network for change detection. We also use a transformer to design a pyramid pooling module to help the network maintain more features. Moreover, we construct a self-supervised network based on a contrastive method to obtain a pre-trained model, especially for remote sensing images, aiming to achieve better results. As for study areas and data sources, we chose Hebei Province, where the environmental problem is quite nervous, and used its GF-1 satellite images to do our research. Through ablation experiments and contrast experiments, our method is proven to have significant advantages in terms of accuracy and efficiency. We also predict large-scale areas and calculate the intersection recall rate, which confirms that our method has practical values.

Land Use Classification of High-Resolution Multispectral Satellite Images With Fine-Grained Multiscale Networks and Superpixel Postprocessing

Land use recognition from multispectral satellite images is fundamentally critical for geological applications, but the results are not satisfied. The scale dimension of current multiscale learning is too coarse to account for rich scales in multispectral images, and pixel-wise classification tends to produce “salt-and-pepper” labels due to possible misclassification in heterogeneous regions. In this paper, these issues are addressed by proposing a new pixel-wise classification model with finer scales for convolutional neural networks. The model is designed to extract multiscale contextual information using multiscale networks at a fine-grained level, addressing the issue of insufficient multiscale learning for classification. Furthermore, a small-scale segmentation-combination method is introduced as a post-processing solution to smooth fragmented classification results. The proposed method is tested on GF-1, GF-2, DEIMOS-2, GeoEye-1, and Sentinel-2 satellite images, and compared with six neural-network-based algorithms. The results demonstrate the effectiveness of the proposed model in finding objects of large scale difference, improving classification accuracy, and reducing classified fragments. The discussion also illustrates that convolutional neural networks and pixel-wise inference are more practical than Transformer and patch-wise recognition.

Coastal Aquaculture Area Extraction Based on Self-Attention Mechanism and Auxiliary Loss

With the development of deep learning in satellite remote sensing image segmentation, convolutional neural networks have achieved better results than traditional methods. In some full convolutional networks, the number of network layers usually increases to obtain deep features, but the gradient disappearance problem occurs when the number of network layers deepens. Many scholars have obtained multiscale features by using different convolutional calculations. We want to obtain multiscale features in the network structure while obtaining contextual information by other means. This article employs the self-attention mechanism and auxiliary loss network (SAMALNet) structure to solve the above problems. We adopt the self-attention strategy in the atrous spatial pyramid pooling module to extract multiscale features while considering the contextual information. We add auxiliary loss to overcome the gradient disappearance problem. The experimental results of extracting aquaculture areas in the Jiaozhou Bay area of Qingdao from high-resolution GF-2 satellite images show that, in general, SAMALNet achieves better experimental results compared with UPS-Net, SegNet, DeepLabv3, UNet, DeepLabv3+, and PSPNet network structures, including recall 96.34%, precision 95.91%, F1 score 96.12%, and MIoU 92.60%. SAMALNet achieved better results extracting aquaculture area boundaries than the other network structures listed above. The high accuracy of the aquaculture area can provide data support for the rational planning and environmental protection of the coastal aquaculture area and promote more rational usage of the coastal aquaculture area.

Extraction of Winter Wheat Planting Area Based on Multi-Scale Fusion

It is difficult to accurately identify the winter wheat acreage in the Jianghuai region of China, and the fusion of high-resolution images and medium-resolution image data can improve the image quality and facilitate the identification and acreage extraction of winter wheat. Therefore, the objective of this study is to improve the accuracy of China’s medium-spatial resolution image data (environment and disaster monitoring and forecasting satellite data, HJ-1/CCD) in extracting the large area of winter wheat planted. The fusion and object-oriented classification of the 30 m × 30 m HJ-1/CCD multispectral image and 2 m × 2 m GF-1 panchromatic image (GF-1/PMS) of winter wheat at the jointing stage in the study area were studied. The GF-1/PMS panchromatic images were resampled at 8 m, 16 m and 24 m to produce panchromatic images with four spatial resolutions, including 2 m. They were fused with HJ-1/CCD multispectral images by Gram Schmidt (GS). The quality of the fused images was evaluated to pick adequate scale images for the field pattern of winter wheat cultivation in the study area. The HJ-1/CCD multispectral image was resampled to obtain an image with the same scale as the suitable scale fused image. In the two images, the training samples SFI (samples of fused image) and SRI (samples of resampled image) containing spectral and texture information were selected. The fused image (FI) and resampled image (RI) were used for winter wheat acreage extraction using an object-oriented classification method. The results indicated that the fusion effect of 16 m × 16 m fused image was better than 2 m × 2 m, 8 m × 8 m and 24 m × 24 m fused images, with mean, standard deviation, average gradient and correlation coefficient values of 161.15, 83.01, 4.55 and 0.97, respectively. After object-oriented classification, the overall accuracy of SFI for the classification of resampled image RI16m was 92.22%, and the Kappa coefficient was 0.90. The overall accuracy of SFI for the classification of fused image FI16m was 94.44%, and the Kappa coefficient was 0.93. The overall accuracy of SRI for the classification of resampled image RI16m was 84.44%, and the Kappa coefficient was 0.80. The classification effect of SFI for the fused image FI16m was the best, indicating that the object-oriented classification method combined with the fused image and the extraction samples of the fused image (SFI) could extract the winter wheat planting area with precision. In addition, the object-oriented classification method combining resampled images and the extraction samples of fused images (SFI) could extract the winter wheat planting area more effectively. These results indicated that the combination of medium spatial resolution HJ-1/CCD images and high spatial resolution GF-1 satellite images could effectively extract the planting area information of winter wheat in large regions.

Coastline extraction based on multi-scale segmentation and multi-level inheritance classification

Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.

A new method to estimate the speed of internal solitary waves based on a single optical remote sensing image

ABSTRACT It is imperative to estimate the energy of internal solitary waves (ISWs) in the real ocean. The energy of ISW is related to speed and amplitude. It is a problem to obtain the propagation speed of ISW from a single optical remote sensing image. Generally, the nonlinear phase speed (NPS) of ISW can be regarded as the propagation speed of ISW. This paper proposes a new inversion approach for NPS of ISW based on an optical remote sensing image. The simulation platform of optical remote sensing was used to conduct the ISW experiment in the laboratory, and the data were obtained by a series of experiments. Three NPS inversion models of ISW are investigated by support vector regression (SVR), Random forest (RF) and Deep neural network (DNN) based on a single optical remote sensing image. The accuracy of the inversion models was verified by the in-situ data of GF-1 satellite images, GF-4 satellite images and Moderate-resolution Imaging Spectroradiometer (MODIS) images. In the verification results, the SVR inversion model has high accuracy in different sea areas and water depths. The RF and DNN inversion models both have high inversion accuracy in the water depth range of 93–299 m in the South China Sea. Compared with the other two traditional methods for calculating ISW NPS, the SVR inversion model still has the highest accuracy. The results showed that the NPS models of ISW are suitable and effective.

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.

Machine-Learning-Based Change Detection of Newly Constructed Areas from GF-2 Imagery in Nanjing, China

Change detection of the newly constructed areas (NCAs) is important for urban development. The advances of remote sensing and deep learning algorithms promotes the high precision of the research work. In this study, we firstly constructed a high-resolution labels for change detection based on the GF-2 satellite images, and then applied five deep learning models of change detection, including STANets (BASE, BAM, and PAM), SNUNet (Siam-NestedUNet), and BiT (Bitemporal image Transformer) in the Core Region of Jiangbei New Area of Nanjing, China. The BiT model is based on transformer, and the others are based on CNN (Conventional Neural Network). Experiments have revealed that the STANet-PAM model generally performs the best in detecting the NCAs, and the STANet-PAM model can obtain more detailed information of land changes owing to its pyramid spatial-temporal attention module of multiple scales. At last, we have used the five models to analyze urbanization processes from 2015 to 2021 in the study area. Hopefully, the results of this study could be a momentous reference for urban development planning.

Automatic Extraction of Mountain River Surface and Width Based on Multisource High-Resolution Satellite Images

The extraction of high-resolution geomorphic information from remote sensing images is a key technology for supporting mountain river research. Extracting small rivers (width < 90 m) from complex backgrounds based on satellite images remains a challenging issue. In this research, we propose an improved random forest (RF) algorithm, RF-ANN (artificial neural network), by using neural networks and thermal infrared data for the extraction of river surfaces. We also develop an automated river width extraction (ARWE) method based on the central axis transformation algorithm and centerline automatic correction algorithm for the automatic extraction of the river widths across the whole basin. We chose the Huangfuchuan River Basin on the Loess Plateau, China, as a case study area. Chinese GF-1 and ZY-3 satellite images were implemented as the primary data source. We extracted the bankfull river surface and river widths of the Huangfuchuan River by using these two improved methods. The results show that the RF-ANN method has a total river surface extraction accuracy of 94.7%, and the extracted river surfaces cover more than 85% of the order 3 DEM river network. By implementing high-resolution DEM and thermal infrared data, RF-ANN effectively eliminates the disturbance of shadows of mountains and other features, which ensures the high accuracy of the extracted widths. It was verified that the maximum and minimum river widths that can be extracted in the Huangfuchuan River Basin are 297.4 m and 6.1 m, respectively. The overall error of river width extraction is 0.97 m, which is less than half of the pixel length of remote sensing images. The R2 and root mean square error (RMSE) of the estimated river width values are 0.99 and 1.49, respectively. For tiny rivers with widths narrower than 10 m, the error of river width extraction is 10.9%. The error of thin rivers whose widths range from 10 to 30 m is 4.9%. For small rivers ranging from 30 to 90 and rivers wider than 90 m, the error is 1.1% and 0.6%, respectively. The new approach provides an effective method for extracting the surface and width of mountain rivers in topographically complex regions by using high-resolution satellite images, which may provide a database for estimating river carbon emissions and related research in fluvial morphology and water resource management.

Mapping and Spatial Variation of Seagrasses in Xincun, Hainan Province, China, Based on Satellite Images

Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea.

Rapid monitoring of abandoned farmland and information on regulation achievements of government based on remote sensing technology

Farmland abandonment has become a worldwide phenomenon, and remote sensing technology has been used to monitor this phenomenon. However, information on only the final net change in abandoned farmland can be obtained based on existing methods, and the various components and contributions of government regulation cannot be identified. These methods therefore cannot meet the needs for the management of abandoned farmland or related policy-adjustments. Therefore, this study uses Longyan Prefecture in China as the study area, and GF-1 satellite images as the main data source, to propose a new rapid monitoring method for abandoned farmland that can reflect and separate the government's regulatory contribution to changes in farmland abandonment. The results show that the abandoned farmland in the study area generally expanded from 2015 to 2019. Despite this observation, government regulation has made some achievements. In 2015–2017 and 2017–2019, 41.72% and 41.67% of the new increased abandoned farmland was offset, respectively, delaying the process of farmland-abandonment. However, the achievement of government regulation decreased by 23.86% in 2017–2019 compared with that during 2015–2017. The results show that our method can reflect the process of farmland abandonment and the contribution of government regulation, can be applied on a large scale, and provides comparable results among regions. Offsetting the newly increased abandoned farmland by improving the contribution of government regulation is an important and direct way to curb farmland abandonment, but special attention should be given to the scientific nature and full implementation of policies in actual management practices. Data AvailabilityThe Chinese GF-1 Satellite images were downloaded from the China Center for Resources Satellite Date and Application website (http://www.cresda.com/EN/). The Advanced Land Observing Satellite (ALOS) data downloaded from the National Aeronautics and Space Administration (NASA) (https://search.asf.alaska.edu/#/). The vector boundary of the research area was obtained from the Chinese Resource and Environment Science and Data Center (https://www.resdc.cn/). Most of the field samples for evaluating the accuracy of the land classification results downloaded from Global Change Data & Discovery (http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=1502).