In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment.

Vegetation-covered water bodies (VCW) are a vital component of wetlands, and their distribution information is crucial for studying the dynamic interactions between vegetation and water. However, due to vegetation obstruction, optical remote sensing has limitations in extracting such water bodies, as it typically identifies only open water areas effectively. In contrast, microwave remote sensing, with its vegetation-penetrating capability and specular reflection characteristics, provides a more comprehensive identification of wetland water bodies. Previous studies have shown that the additional water body areas (SW) identified by SAR but not by optical sensors are often accompanied by significant vegetation cover. However, a systematic assessment of SW’s potential in mapping VCW is still lacking. This study uses the Caohai Wetland in Guizhou, China, as an example, leveraging Sentinel-2A and RadarSat-2 imagery from adjacent periods and multiple water body extraction methods to extract SW and explore its performance in mapping VCW during the dry season. Results show that during the initial stage of vegetation senescence (7 January 2019), the use of SW achieved high accuracy in mapping VCW, with overall accuracy, kappa coefficient, and F1 score reaching 84.2%, 68.4%, and 85.3%, respectively. However, as vegetation senescence deepened (12 January 2020), these metrics dropped to 76.2%, 60.7%, and 87%, respectively, indicating a significant decline in accuracy. During the vegetation regrowth stage (7 April 2020), the overall accuracy, kappa coefficient, and F1 score were 71.1%, 57.2%, and 70.9%, respectively. As vegetation continued to grow (21 April 2019), these metrics improved to 79.4%, 67.2%, and 86.6%. In summary, SW extracted from high-resolution optical and SAR imagery can preliminarily map VCW during the dry season. Furthermore, its identification accuracy improves significantly with increasing vegetation density. This study provides a novel perspective for wetland water body monitoring and the study of vegetation-water interactions.

Nitrogen (N) is a key nutrient for sustaining ecosystem productivity and agricultural sustainability; however, achieving high-precision monitoring in wetlands with highly heterogeneous surface types remains challenging. This study focuses on Caohai, a representative karst plateau wetland in China, and integrates Sentinel-2 multispectral and Zhuhai-1 hyperspectral remote sensing data to develop a soil nitrogen inversion model based on spectral indices, texture features, and their integrated combinations. A comparison of four machine learning models (RF, SVM, PLSR, and BPNN) demonstrates that the SVM model, incorporating Zhuhai-1 hyperspectral data with combined spectral and texture features, yields the highest inversion accuracy. Incorporating land-use type as an auxiliary variable further enhanced the stability and generalization capability of the model. The study reveals the spatial enrichment of soil nitrogen content along the wetland margins of Caohai, where remote sensing inversion results show significantly higher nitrogen levels compared to surrounding areas, highlighting the distinctive role of wetland ecosystems in nutrient accumulation. Using Caohai Wetland on the Chinese karst plateau as a case study, this research validates the applicability of integrating spectral and texture features in complex wetland environments and provides a valuable reference for soil nutrient monitoring in similar ecosystems.

In order to clarify the response relationship between rocky desertification and land use under different lithology background in karst area. We obtained the land use distribution in 2005, 2010 and 2015 by using supervised classification, and then carried out superimposed analysis with the rocky desertification and lithology data in the same period. The result shows that woodland and shrubbery are mainly distributed on limestone interbedded with clastic rock, with an area of approximately 51.73 km2. Grasslands and arable land are mainly distributed on continuous limestone and limestone interbedded with clastic rock. Rocky desertification area in the limestone interbedded with clastic rock is the largest, and extremely severe rocky desertification (ESKRD) of 6.70 km2, due to the difference of lithologic types. Different levels of rocky desertification are correlated with different land use types and lithology types. The type of rocky desertification in forest land is mainly light rocky desertification (LKRD), contributing more than 40

The Relief Degree of Land Surface (RDLS) is an important index to evaluate regional environment. It has a significant effect on the local climate, geologic hazards, the path and speed of fire spreading, the migrations of wild animals, and the runoff path and speed of precipitation. The forest-steppe ecotone in northern China is one of ecological fragile zones. In-depth study of the RDLS of the forest-steppe ecotone in northern China will help to implement ecological projects scientifically and promote the construction of the national ecological security barrier. The Shuttle Radar Topography Mission (SRTM-GL1 30 m) data were used to determine the optimal analysis window for RDLS based on the mean change-point method, and the elevation difference was extracted based on the window analysis method. The RDLS model was used to extract RDLS of the forest-steppe ecotone and analyzed with the help of a spatial auto-correlation model. The correlation between mean elevation, relative elevation difference, and RDLS was also analyzed. The results show that the optimal analysis window size for RDLS was 29 × 29, corresponding to an area of 0.76 km2. The RDLS under the optimal analysis window extracted from SRTM-GL1 (30 m) ranged from 0.084 to 3.516. The RDLS had significant spatial clustering, with high RDLS mainly distributed in the mountainous areas and low RDLS mainly distributed in mountain-to-plain transition zone; the RDLS between different administrative units and different watersheds had obvious variability. Overall, the RDLS was characterized as decreasing, increasing, and then decreasing from the south to north, while it was high in the west and low in the east. And the RDLS was linearly positively correlated with mean elevation and relative elevation difference. In the future, the implementation of major ecological projects in the forest-steppe ecotone in northern China, such as soil and water conservation, afforestation tree species selection, ecological corridor design, ecological management, geological disaster prevention, and forest fire prevention, should fully consider the local topographic conditions. These research results can provide topographic references for the implementation of ecological planning and engineering in this area and similar areas. It contributes to sustainable development and maximization of ecological benefits and promotes the establishment of a national ecological security barrier.

Abstract Context: As an index of ecological well-being, Gross Ecosystem Product (GEP) estimates the value of final ecosystem services or the direct benefits people derive from the ecosystem. Objectives: In this research, we accounted for GEP and quantified the impacts of human activities on GEP in Shanxi, an ecologically fragile area in China, from 1990 to 2020. Methods: We associated all kinds of non-spatial data with spatial data and employed the local indicators of spatial association, the Sankey diagram, and the empirical orthogonal decomposition (EOF) to explore the spatio-temporal dynamic properties of GEP. The transfer matrix and gravity model were used to measure the response of the GEP to disturbance from human activities due to urbanization. Results: The results show that: (1) excluding 2010, the GEP possesses a growth trend and increased from 117.65 billion Chinese yuan (CNY) to 4594.89 billion CNY; (2) contrary to the steady growth of the GEP, the regions with high GEP generally tended to decrease, and the Green Gold Index (GGI) tended to increase and then decrease; (3) the spatial distribution of GEP in Shanxi is restricted, and there is a tendency for this restriction to decrease over time; (4) the decade from 2005 to 2015 has the fewest changes in the GEP of Shanxi; (5) the GEP field has a globally consistent type and a high-value-low-value inverse phase-type in the variation of the spatial distribution, and the first type accounts for 61.74% of the total variance in the EOF; (6) the variation of GEP in different cities may differ significantly over time, and the cities with more disturbance from human activities have lower GEP or higher variance in GEP; (7) the disturbance of residential land has a more significant impact on the GEP than the disturbance of industrial and mining land in Shanxi. Conclusion: Our research could provide important insights into ecological assessment in an ecologically fragile region, thus providing a policy basis for the conservation and better use of environmental resources.

Landslide susceptibility maps (LSMs) play an important role in landslide hazard risk assessments, urban planning, and land resource management. While states of motion and dynamic factors are critical in the landslide formation process, these factors have not received due attention in existing LSM-generation research. In this study, we proposed a valuable method for dynamically updating and refining LSMs by combining soil moisture products with Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data. Based on a landslide inventory, we used time-series soil moisture data to construct an index system for evaluating landslide susceptibility. MT-InSAR technology was applied to invert the displacement time series. Furthermore, the surface deformation rate was projected in the direction of the steepest slope, and the data was resampled to a spatial resolution consistent with that of the LSM to update the generated LSM. The results showed that varying soil moisture conditions were accompanied by dynamic landslide susceptibility. A total of 22% of the analyzed pixels underwent significant susceptibility changes (either increases or decreases) following the updating and refining processes incorporating soil moisture and MT-InSAR compared to the LSMs derived based only on static factors. The relative landslide density index obtained based on actual landslides and the analyses of Dongfeng, Haila town, and Dajie township confirmed the improved slow landslide prediction reliability resulting from the reduction of the false alarm and omission rates.

Remote sensing image with high spatial and temporal resolution is very important for rational planning and scientific management of land resources. However, due to the influence of satellite resolution, revisit period, and cloud pollution, it is difficult to obtain high spatial and temporal resolution images. In order to effectively solve the “space–time contradiction” problem in remote sensing application, based on GF-2PMS (GF-2) and PlanetSope (PS) data, this paper compares and analyzes the applicability of FSDAF (flexible spatiotemporal data fusion), STDFA (the spatial temporal data fusion approach), and Fit_FC (regression model fitting, spatial filtering, and residual compensation) in different terrain conditions in karst area. The results show the following. (1) For the boundary area of water and land, the FSDAF model has the best fusion effect in land boundary recognition, and provides rich ground object information. The Fit_FC model is less effective, and the image is blurry. (2) For areas such as mountains, with large changes in vegetation coverage, the spatial resolution of the images fused by the three models is significantly improved. Among them, the STDFA model has the clearest and richest spatial structure information. The fused image of the Fit_FC model has the highest similarity with the verification image, which can better restore the coverage changes of crops and other vegetation, but the actual spatial resolution of the fused image is relatively poor, the image quality is fuzzy, and the land boundary area cannot be clearly identified. (3) For areas with dense buildings, such as cities, the fusion image of the FSDAF and STDFA models is clearer and the Fit_FC model can better reflect the changes in land use. In summary, compared with the Fit_FC model, the FSDAF model and the STDFA model have higher image prediction accuracy, especially in the recognition of building contours and other surface features, but they are not suitable for the dynamic monitoring of vegetation such as crops. At the same time, the image resolution of the Fit_FC model after fusion is slightly lower than that of the other two models. In particular, in the water–land boundary area, the fusion accuracy is poor, but the model of Fit_FC has unique advantages in vegetation dynamic monitoring. In this paper, three spatiotemporal fusion models are used to fuse GF-2 and PS images, which improves the recognition accuracy of surface objects and provides a new idea for fine classification of land use in karst areas.

Introduction Open-pit coal mining could disrupt the ecosystem and lead to the loss of service values for the ecosystem through direct occupation or indirect impacts on adjacent ecosystems. Methods In this research, we combined a new accounting system, gross ecosystem product (GEP), with spatial–temporal analyses to quantify the ecological variation and explore its driving factors in Pingshuo, a large-scale open-pit coal mining area in China. GEP is an aggregate accounting system that can summarize the value of provisioning, regulating, and cultural ecosystem services (ES) in a single monetary metric. The spatial–temporal approaches used in our study were known as exploratory spatial data analyses and interpretable models in machine learning. Both spatial and non-spatial data, including remote sensing images, meteorological data, and official statistics, were applied in the research. Results The results indicated the following: (i) From 1990 to 2020, the annual average growth rates of GEP decreased from 30.78 to 9.1%. Furthermore, the classified results of GEP revealed that the regions with rich ES quality rapidly reduced from 51.90 to 32.18%. (ii) Spatial correlation of GEP was significant, and the degree of spatial clustering was relatively high in the mining areas. Moreover, the mining areas also continually presented concentrated high-density and hot spot areas of GEP changes. (iii) The spatial–temporal effects were notable in the relationship between GEP and three socioeconomic factors, i.e., the mining effects, human activity intensity, and gross domestic product (GDP). (iv) The win–win development for both the economy and ecological environment in Pingshuo could be realized by restricting the annual growth rate of mining areas to between 4.56 and 5.03%. Discussion The accounting results and spatial–temporal analyses of GEP will contribute to the future regional sustainable development and ecosystem management in Pingshuo.

Due to the influence of atmospheric phase delays and terrain fluctuation in complex mountainous areas, traditional PS-InSAR technology often fails to select enough measurement points (MPs) and loses effective MPs during phase unwrapping. To solve this problem, this paper proposes an adaptive network construction algorithm, which combines the permanent scatterer (PS) points with the distributed scatterer (DS) points. Firstly, to ensure the extraction quality of the DS points, the covariance matrix of DS points is estimated robustly. Secondly, based on the traditional Delaunay triangulation network, an adaptive network construction method is proposed, which can adaptively increase edge redundancy and network connectivity by considering the edge length, edge coherence, edge number, and spatial distribution. Finally, a total of 31 RADARSAT-2 SAR images that cover the Zongling landslide group in Guizhou Province were used to prove the effectiveness of proposed method. The results show that the quantity of available DS points can be increased by 23.6%, through the robust estimation of the covariance matrix. In addition, it is demonstrated that the proposed network construction algorithm can balance the number, distribution, and quality of edges in the dense and sparse areas of MPs adaptively. This adaptive network construction approach can maintain good connectivity and avoid losing effective MPs to the greatest extent, especially when the scattering points are far away from the reference points. In short, the proposed algorithm improves the number of effective MPs and accuracy of phase unwrapping.

Landslides are a common and costly geological hazard, with regular occurrences leading to significant damage and losses. To effectively manage land use and reduce the risk of landslides, it is crucial to conduct susceptibility assessments. To date, many machine-learning methods have been applied to the landslide susceptibility map (LSM). However, as a risk prediction, landslide susceptibility without good interpretability would be a risky approach to apply these methods to real life. This study aimed to assess the LSM in the region of Nayong in Guizhou, China, and conduct a comprehensive assessment and evaluation of landslide susceptibility maps utilizing an explainable artificial intelligence. This study incorporates remote sensing data, field surveys, geographic information system techniques, and interpretable machine-learning techniques to analyze the sensitivity to landslides and to contrast it with other conventional models. As an interpretable machine-learning method, generalized additive models with structured interactions (GAMI-net) could be used to understand how LSM models make decisions. The results showed that the GAMI-net model was valid and had an area under curve (AUC) value of 0.91 on the receiver operating characteristic (ROC) curve, which is better than the values of 0.85 and 0.81 for the random forest and SVM models, respectively. The coal mining, rock desertification, and rainfall greater than 1300 mm were more susceptible to landslides in the study area. Additionally, the pairwise interaction factors, such as rainfall and mining, lithology and rainfall, and rainfall and elevation, also increased the landslide susceptibility. The results showed that interpretable models could accurately predict landslide susceptibility and reveal the causes of landslide occurrence. The GAMI-net-based model exhibited good predictive capability and significantly increased model interpretability to inform landslide management and decision making, which suggests its great potential for application in LSM.

Soil erosion is a major global soil degradation problem that threatens land, freshwater and oceans. Rainfall erosivity has led to an increasing in the global soil erosion rate, while vegetation restoration is a safeguard measure to reduce soil erosion. Therefore, probing the influence of precipitation and vegetation on the spatial distribution of soil erosion is important for understanding the mechanism of erosion. In order to assess the degree of global soil erosion, based on the RUSLE model, a global soil erosion data set from 2000 to 2015 (0.25 degrees x 0.25 degrees) was created, showing that soil erosion was increasing in 70.80% of the study area, where precipitation was the dominant factor. Different grades of erosion showed that the soil erosion area of mild and above mild erosion increased by 44.88 x 10(6) ha, an increase of 5.39%. Spatial erosion is mainly distributed in Asia and North America. The difference from North America is that the erosion in Asia showed a decreasing trend during the study period. Different climatic zones show that erosion mainly occurs in the temperate zone, accounting for 39.97% of the area. Precipitation and vegetation increasing signifi- cantly in 24.43% and 16.74% of the regions. However, the proportion of regions where precipitation and vegetation had a negative contribution to erosion was 29.12% and 53.81%. Above results will deepen our understanding of the mechanism of erosion.

将内嵌有面阵相机及IMU 的智能手机作为硬件系统,单目SLAM 技术获取多视图几何深度图、位姿等为数据源,构建了单目SLAM 增强现实森林测树系统.设计了基于平滑度高鲁棒性过滤胸高圆柱体表面点云及切线的方法;然后,基于点到圆柱体表面距离及圆柱体切线到圆柱体表面距离构建了胸径与立木位置精确估计算法;最后,以该算法为基础在智能手机端开发了增强现实测树系统,即利用智能手机实时测树、并通过增强现实场景实时人工监督测量结果.新型测树系统在5 块32 m ×32 m 方形样地中进行了测试,以评估新型测树系统的测量精度;此外,每块样地使用了单次观测、正交观测、对称观测及环绕观测4 种不同的观测方法对立木胸高圆柱体观测,以评估不用观测方式对测树精度的影响.结果显示:立木位置估计值在X、Y轴方向的平均误差范围为-0.014～0.020 m,X、Y轴方向均方根误差范围为0.04～0.08 m;立木胸径估计值偏差为-0.85～-0.03 cm(相对偏差为-3.60％～-0.04％),均方根误差为1.32～2.51 cm(相对均方根误差为6.41％～12.33％);相比于单次观测方法,其他观测方法获取位置及胸径估计精度均有提高(特别是不可近似为圆柱体的立木树干),从精度与效率角度而言,正交观测及对称观测为最佳观测方法.结果表明,单目SLAM 增强现实测树系统是一种可精确进行森林样地调查的潜在解决方案.

The landscape pattern of the Black-necked Crane (Grus nigricollis) habitat in China changed at different spatial scales and long-term periods due to natural factors and human activities, and habitat reduction and fragmentation threatened the survival of Black-necked Cranes. The factors driving the habitat landscape pattern and individual population changes of Black-necked Cranes remain to be studied. In this paper, based on remote sensing data of land use from 1980 to 2020, the changes in landscape pattern and fragmentation of the Black-necked Crane habitat in China over 40years were analyzed from two different spatial scales using the land cover transfer matrix and landscape index. The correlation between landscape and Black-necked Crane individual population was analyzed. The most obvious observations were as follows: (1) Although transformation between landscapes occurred to varying degrees, the area of wetlands and arable land in the breeding and the wintering areas (net) increased significantly from 1980 to 2020. (2) Habitat fragmentation existed in the breeding and the wintering area and was more obvious in the wintering area. (3) The number of individuals of Black-necked Cranes increased period by period, and habitat fragmentation did not inhibit their population growth. (4) The number of individuals of Black-necked Crane was closely related to the wetland and arable land. The increasing area of wetlands and arable and the increasing landscape shape complexity all contributed to the growth of the individual population. The results also suggested that the number of individuals of Black-necked Crane was not threatened by the expanding arable land in China, and they might benefit from arable landscapes. The conservation of Black-necked Cranes should focus on the relationship between individual Black-necked Cranes and arable landscapes, and the conservation of other waterbirds should also focus on the relationship between individual waterbirds and other landscapes.

Landslides are very complicated natural phenomena that create significant losses of life and assets throughout China. However, previous studies mainly focused on monitoring the development trend of known landslides in small areas, and few studies focused on the identification of new landslides. In addition, karst areas, where the vegetation is dense, the mountains are high, the slopes are steep, and the time incoherence is serious, have difficulty in tracking Differential Interferometric Synthetic Aperture Radar (DInSAR) landslides. Therefore, based on DInSAR technology, we use ALOS-2 PALSAR data to conduct continuous monitoring of existing hazards and identify new geological hazards in karst areas. The major results are as follows: 1) From June 11 to 6 August 2017, it was discovered that a hidden point of landslides occurred on the 420 m northwest mountain near the town of Zongling. It was determined that the landslide hidden point had been slipping for two consecutive years, with an average slip of 6.0 cm. From 4 September 2016 to 22 January 2017, undiscovered hidden points in the landslide account were found in Yinjiazhai. On 13 September 2016 and 22 November 2016, the discovered potential hazards in the landslide log book were the mountain hazards in southwestern Shiping village, and the deformation was 7.8 cm. 2) The DInSAR monitoring results from September to November 2016 showed that large deformations occurred in the landslide area of Shiping village. During a field visit, large cracks on the surface were found. The length of surface cracks in the southwest direction of Shiping village was 2.8 m. On 13 July 2017, Shiping collapsed as a result of the collapse of the mountainous area where the disaster occurred. The average slope of the landslide in the landslide area was approximately 65°, the height was 95 m, the length and width were 150 m and 25 m, respectively, and the thickness was 5 m. The method has shown great potential in precisely identifying some new geological hazards sites, as well as tracking and monitoring the potential hazards of geological disasters listed on the landslide account.

Detecting the terrain surface cracks caused by earthquakes, which are termed coseismic ruptures, has important significance for discovering concealed faults, monitoring their movements, and forecasting possible follow-on earthquakes. On May 22, 2021, Maduo County in Qinghai province, China, suffered an earthquake with a magnitude of 7.4, which created densely distributed cracks. In this study, we designed an automatic crack detection framework based on remote sensing technology. With the use of low-altitude unmanned aerial vehicles (UAVs), we obtained very high-resolution aerial images of the area affected by the earthquake, which were further processed by photogrammetric software to produce digital orthophoto maps (DOMs). We then designed a novel terrain surface crack detection neural network, which differs from the previous methods that focus on detecting cracks in man-made object surfaces such as flat roads. We investigated the spatial property of the sinuous linear cracks and handled this by introducing adaptive deformable convolutions with a context-channel-space boosted mechanism. The feature extraction stage, feature optimization stage, and upsampling stage were embedded with the deformable convolutions to form a compact and powerful crack detector, named Crack-CADNet [the Context-chAnnel-space boosted Deformable convolutional neural network (CNN) for crack detection]. The postprocessing included filtering out the nontectonic cracks, aided by annotations from experts, and grouping and vectorizing the generated binary segmentation map as crack polygons, which were evaluated at the instance level. In addition to the first in-depth investigation of detecting earthquake cracks with aerial remote sensing and a deep learning-based process, the crack detection network we propose outperformed the recent CNN-based methods designed for general semantic segmentation and crack detection. Source code and the Maduo earthquake crack dataset will be available at http://gpcv.whu.edu.cn/data/.

Forests are an important part of the ecological environment, and changes in forests not only affect the ecological environment of the region but are also an important factor causing landslide disasters. In order to correctly evaluate the impact of forest cover on landslide susceptibility, in this paper, we build an evaluation model for the contribution of forests to the landslide susceptibility of different grades based on survey data for forest land change in Bijie City and landslide susceptibility data, and discuss the effects of forest land type, origin, age group, and dominant tree species on landslide susceptibility. We find that forests play a certain role in regulating landslide susceptibility: compared with woodland, the landslide protection ability of shrubland is stronger. Furthermore, natural forests have a greater inhibitory effect on landslides than artificial forests, and compared with young forest, mature forest and over-mature forest, middle-aged forest and near-mature forest have stronger landslide protection abilities. In addition, the dominant tree species in different regions have different impacts on landslides. Coniferous forests such as Chinese fir and Cryptomeria fortunei in Qixingguan and Dafang County have a low ability to prevent landslides. Moreover, the soft broad tree species found in Qianxi County, Zhijin County, Nayong County and Jinsha County are likely to cause landslides and deserve further research attention. Additionally, a greater focus should be placed on the landslide protection of walnut economic forests in Hezhang County and Weining County. Simultaneously, greater attention should be paid to the Cyclobalanopsis glauca tree species in Weining County because the area where this tree species is located is prone to landslides. Aiming at addressing the landslide susceptibility existing in different forests, we propose forest management strategies for the ecological prevention and control of landslides in Bijie City, which can be used as a reference for landslide susceptibility prevention and control.

Although the exploitation of mineral areas brings wealth to society, it inevitably leads to the degradation of the surrounding natural environment. To understand and assess the influences of mining activities on the geological and ecological environment, land cover classification in open-pit mine areas (LCCMA) is of great significance. This research proposes an intelligent classification framework for LCCMA based on an object-oriented method and multitask learning (MTL), named the MTL Classification Framework (MTLCF). With the help of MTL, each land cover type in open-pit mine areas obtains its exclusive and receivable object-oriented feature sets using the model-agnostic method. After that, the feature sets are fused with the original images. EfficientNet, a spatial pyramid pooling module, and a global attention upsample module are assembled as the segmentation models with the structure of the encoder and decoder to classify intelligently each land cover type in open-pit mine areas. Finally, the models were trained, and ablation experiments were performed. The experimental results show that our proposed framework -MTLCF was effective for classification in LCCMA, and the overall accuracy and the mean of F1 score for the MTLCF in LCCMA were 85.6% and 86.06%, respectively. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

Forests play an important role in the global carbon cycle, and the growth of forest trees is essential to forest carbon sinks. It is necessary to explore the diameter at breast height (DBH) growth of trees and to quantify the effects of different factors on their growth to predict forest development. This study constructed an individual-tree annual growth-rate model of 41 dominant tree species in China to explore the effects of genetic characteristics and the environment on tree growth and the relationship between forest carbon sink capacity and the geographical environment. The model was estimated and evaluated based on 492,555 samples of 7801 permanent plots (covering 31 provinces in mainland China) and then decomposed into a general annual growth-rate component and an environmental modifier component. The results showed that tree species and size were the dominant factors that affected the growth rate, showing an inverse J-curve trend, and temperature was the most important of all environmental factors. In addition, there was a significant correlation between the potential aboveground carbon sink (PACS) and the geographic growth pattern index (GPI) (Spearman's = 0.445, p-value < 0.001) and a significant correlation between the geographic growth structure index PACS and the geographic growth structure index (GSI) (Spearman's = 0.014, p-value < 0.001). Areas with high GPI and GSI may have better environmental conditions for tree growth, leading to higher PACS. In China, the GPI was higher in the southeastern regions than in the northwestern regions and was also higher in the more humid regions than in the dry regions, resulting in a higher PACS. In conclusion, in forest resource management and future afforestation planning, we should first consider the suitability of the geographical environment, and then take the topographical structure into consideration to determine the proportion of suitable tree species and forest structure.