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Global food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner. [ABSTRACT FROM AUTHOR]

Many coal mining areas overlapped with agricultural land in the world. However, surface subsidence and waterlogging brought on by coal mining inexorably harm the agricultural land. Soil moisture can reflect the variation of mining impact significantly according to the geographic features of the high underground water table, which shows significant spatial gradient variation within the subsidence basin. Previous studies have used site survey and unmanned aerial vehicle (UAV), which are not applicable to large‐scale assessments of the ecological impacts and defining the scale of impacts. Here, we used the Google Earth Engine (GEE) platform and an optical trapezoid model (OPTRAM) to estimate the soil moisture distribution around 50 mining subsidence waterbody samples in eastern China. Based on the spatial variation in soil moisture, the impact boundary of coal mining was determined using buffer analysis and trend fitting. The results showed that: (1) The OPTRAM can well reflect the soil moisture distribution which shows a good correlation between estimated and measured values, with R = 0.83 and RMSE = 0.054. (2) The spatial trend fitting of soil moisture is suitable for evaluating mining impact, and the impact distances are in the range of 96–762 m, 80% of which are located at 128–436 m. We also find that 28% of subsidence waterbody buffer basins were heavily influenced by human activities, and the coal mining impact boundaries cannot be effectively identified. The study provides a new idea to quantify the ecological and agricultural impact boundary of coal mining, which could be applied to other similar regions across the globe. [ABSTRACT FROM AUTHOR]

Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands' status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km2, within which 9252 km2 of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner. [ABSTRACT FROM AUTHOR]

Currently, it is a great challenge for remote sensing technology to accurately map mangrove forests owing to periodic inundation. A submerged mangrove recognition index (SMRI) using two high- and low-tide images was recently proposed to remove the influence of tides and identify mangrove forests. However, when the tidal height of the selected low-tide image is not at the lowest tidal level, the corresponding SMRI does not function well, which results in mangrove forests below the low tidal height being undetected. Furthermore, Spartina alterniflora Loisel (S. alterniflora) was introduced to China in 1979 and rapidly spread to become the most serious invasive plant along the Chinese coastline. The current SMRI has failed to distinguish S. alterniflora from submerged mangrove forests because of their similar spectral signatures. In this study, an SMRI-based mangrove forest mapping method was developed using the time series of Sentinel-2 images to mitigate the two aforementioned issues. In the proposed method, quantile synthesis was applied to the time series of Sentinel-2 images to generate a lowest-tide synthetic image for creating SMRI to identify submerged mangrove forests. Unsubmerged mangrove forests were classified using a support vector machine, and a preliminary mangrove forest map was created by merging them. In addition, S. alterniflora was distinguished from the mangrove forests by analyzing their phenological differences. Finally, mangrove forest mapping was performed by masking S. alterniflora. The proposed method was applied to the entire coastline of the Guangxi Province, China. The results showed that it can reliably and accurately identify submerged mangrove forests derived from SMRI by synthesizing low- and high-tide images using quantile synthesis, and the differentiation of S. alterniflora using phenological differences results in more accurate mangrove mapping. This work helps to improve the accuracy of mangrove forest mapping using SMRI and its feasibility for coastal wetland monitoring. It also provides data for sustainable management, ecological protection, and restoration of vegetation in coastal zones. [ABSTRACT FROM AUTHOR]

The development of coal resources is necessary, but it has a huge negative impact on land, ecology, and the environment. With the increasing awareness of environmental protection and the requirements of related regulations, the design and practice of reclamation projects run through the mining life cycle and continue for a long time after the coal production. High-precision monitoring of mining disturbance and reclamation, quantifying the degree and time of vegetation disturbance and restoration, is of great significance to minimize the environmental effect of mining. Remote sensing, widely used as efficient monitoring tool, but there is not enough research on disturbance and reclamation monitoring taking into account large-scale areas and high temporal and spatial accuracy. Especially when mining sites remain unknown, how to distinguish the disturbance of coal mining and other human activities affecting the surface land cover has become a challenge. Therefore, this paper proposed a method to reconstruct the time series of mining disturbance and reclamation in a large area by using the POI (point of interest) and Landsat time series images using multiple buffer analysis methods. The process includes: (1) Retrieval of POI in the study area based on the public mining list using Python crawler, and buffering 100 km for preliminary extraction of potential mining areas; (2) Using spectral index mask and random forest algorithm to accurately extract the exposed coal on the Google Earth Engine (GEE) platform; (3) Buffering 10 km to identify the occurrence of disturbance and reclamation, using pixel-based temporal trajectory identification of LandTrendr algorithm under GEE. The method successful detect the change points of surface coal mining disturbance and reclamation in eastern Inner Mongolia of China. The results show that: (1) The method can effectively identify the extent of surface coal mining disturbance and reclamation, and the overall extraction accuracy is 81%. (2) Surface coal mining disturbance in eastern Inner Mongolia was concentrated in 2006–2011. By 2020, the total disturbed area is 627.8 km2, with an average annual disturbance of 18.5 km2, and the annual maximum disturbance to the ground reached 64.6 km2 in 2008. With the total reclaimed area being 236.3 km2, the reclamation rate is about 37.6%. This study provides a systematic solution and process for monitoring the disturbance and reclamation of surface coal mining in a large range with little known about the mines' location. It can effectively identify the mining disturbance and reclamation process which can also be extended to other areas, providing a quantitative assessment of mining disturbance and reclamation, which can support further ecological restoration decision-making. [Display omitted] • A remote sensing procedure of surface coal mine classification, disturbance and reclamation monitoring is developed. • Landsat, random forest, web scraping, and Google Earth Engine has been used to detect 73 surface coal mining sites. • 0.94% of the total study area has been disturbed by surface mining. • The developed procedure is able to reveal the surface coal mine reclamation pattern. [ABSTRACT FROM AUTHOR]

Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines and mining management. Thus, it is the basement of formulating policies related to mining management and implementing reclamation that detection of mining disturbance rapidly and accurately. This research carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the process of distance and reclamation. Based on the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining in the 1986–2020 period has been detected using the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the overall accuracy of damage and recovery is 92% and 88%, respectively, and the Kappa coefficient is 85% and 84% respectively. This means that we obtained an ideal detection effect; (2) the surface-mining area was increasing from 1986–2020 in the Dexing copper mine, and the accumulation of mining damage is approximately 2865.96 ha with an annual area of 81.88 ha. We also found that the area was fluctuating with the increase. The detected natural restoration was appraised at a total of 544.95 ha in the 1988–2020 period with an average restoration of 16.03 ha. This means that it just restores less in general; (3) it has always been the case that the Dexing mine is damaged by mining and reclamation in the whole year (it is most frequently damaged month is July). All imageries in the mine are detected by the CCDC algorithm, and they are classified as four types by disturbing number in pixel scale (i.e., 0, 1, 2, more than 2 times). Based on that, we found that the only once disturbed pixels account for 64.75% of the whole disturbed pixels, which is the majority in the four classes; (4) this method provides an innovative perspective for obtaining the mining disturbed dynamic information timely and accurately and ensures that the time and number of surface mining disturbed areas are identified accurately. This method is also valuable in other applications including the detection of other similar regions. [ABSTRACT FROM AUTHOR]

In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions. [ABSTRACT FROM AUTHOR]

The eastern plain of China is one of the most important grain production areas in China. Meanwhile, the plain is also an important coal production area with a large number of "coal-grain composite regions". Coal mining causes land subsidence and waterlogging, which destroys a significant amount of cultivated land. However, there is no dataset for the spatio-temporal distribution of subsidence water to assist related decision-making on a regional scale. Accurate monitoring of subsidence water is still a challenge, especially distinguishing among subsidence water, natural water and artificially excavated water by only using remote sensing data. Here, A new method to generate a dynamic map for the subsidence ponding year and the restoration year using the Google Earth Engine platform with 33-year-old Landsat imagery was created. The time segmentation method was used to first extract the change water pixels corresponding to subsidence water and artificially excavated water with similar trajectory features. Then, the morphological characteristics of the two types of change water at the beginning year of water accumulation are used to construct 13 landscape indexes. This approach utilizes the Random Forest algorithm to distinguish between subsidence water and artificially excavated water. The Huang-Huai-Hai plain area in eastern China was selected as the study area and extracted the subsidence water areas from 1986 to 2018. The identification accuracies for subsidence ponding year and restoration year are 88% and 85%. 79% of the subsidence water areas are located in cultivated land, which shows significant impacts to agricultural activities. The method proposed could be applied to other similar areas, the results could provide reference and data for decision-making and related land reclamation planning. • A novel methodology was developed for mapping mining subsidence water. • Map of the subsidence ponding year and restoration year. • Random forest algorithm can effectively extract the patch of subsidence water. • Contribute to monitor annual subsidence water at a regional scale. • This paper belongs to change detection using Landsat time series. [ABSTRACT FROM AUTHOR]

Underground coal mining with high groundwater levels causes many environmental problems, one of the main ones being subsidence waterlogging. After the subsidence waterlogging, the land faces many problems, such as insufficient land development and difficult and costly land restoration. Therefore, it is necessary to monitor the spatial range and temporal trajectory pattern of surface subsidence in a timely manner. When information about underground mining is lacking, it is difficult by using remote sensing alone to identify and distinguish (i) natural water, (ii) engineering water, and (iii) subsidence waterlogging. In this study, we used the Google Earth Engine platform to develop a method to detect subsidence waterlogging area and the disturbance year. The method includes pixel-based trajectory extraction and object-based water type recognition. First, LandTrendr is used to extract the change water (engineering water and subsidence waterlogging) pixels and its disturbance year. Then, the morphological method to further eliminate engineering water patches, so as to extract subsidence waterlogging. We selected the Panxie mining area in Huainan, China as the study area. Using 33 years of Landsat time-series data to generate values of the annual water frequency index, maps of the year of water accumulation caused by underground coal mining and the year of restoration during 1989–2016 are drawn with accuracies of 86.5% and 80.7%, respectively. The results show that from 1989 to 2016, the accumulated area of subsidence waterlogging was 7715.25 ha, accounting for 14.5% of the total area of the study area, of which 75.8% occurred from 2008 to 2016. Furthermore, the accumulated area of restoration was 207.18 ha, which occurred after 2007 and accounts for 2.7% of the total area of subsidence waterlogging. Based on the analysis results of the changes of waterlogging types, the best time window for restoration of the waterlogging area is 3y after water accumulation. The main innovation of this paper is to make use of the temporal heterogeneity (Number of year in patches, Y_num) and morphological index (Landscape shape index in patches, LSI) of the change water to distinguish engineering water and subsidence waterlogging. • Map of subsidence waterlogging caused by mining coal using Landsat time-series. • The annual water frequency index is capable to capture the water variability. • A spatial-temporal algorithm identifies the timing of subsidence waterlogging • The algorithm identifies the timing of backfill after subsidence waterlogging. • The algorithm is applicable to other mining areas too. [ABSTRACT FROM AUTHOR]

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km2 of land from grassland, water, etc., to barren earth, and only 23.54 km2 was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas. [ABSTRACT FROM AUTHOR]

• A new index was proposed to monitor surface mining disturbance. • The new index enhance the sensitivity to improve the detection accuracy. • Compared with five products, the detection accuracy is superior. • Mine Landscape Damage/Reclamation Index can be widely used in the detection of open-pit mining. The exploitation and utilization of mineral resources have significantly contributed to economic and social development; however, the procession has also negatively impacted the ecological environment. Thus, it is necessary to effectively monitor coalfields, an important basis for reasonable development and utilization of coalfields and following ecological reclamation. Based on MODIS Global Damage Index, We couple the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), and proposal the Mine Landscape Damage Index (MLDI) and Mine Landscape Reclamation Index (MLRI) to supervise the disturbed vegetation of mines. Huolinhe coalfield, one of the largest mines in China, is monitored in this research. 3675 Landsat imageries from 1990 to 2020 are analyzed using the LandTrendr algorithm with MLDI and MLRI time series data on Google Earth Engine. The results demonstrate that this method could distinguish the region and disturbing process of damage and reclamation due to its effectiveness proved by visual interpretation of high-resolution imageries and verification of field investigation. The total detected area of vegetation damage was 16505.10 ha, and the vegetation damage in the mining was active from 2006 to 2011, with average annual damage of more than 1000 ha. The total area of vegetation restoration in the mine is 8640.81 ha, accounting for 52.35% of the vegetation damage. Vegetation restoration has increased gradually since 2010. In the past three years, vegetation restoration accounted for 47.61% of the total reclamation area. A novel index for monitoring the effects of surface damage in multi-open-pit mining areas is provided to us, and the detection is reliable. It can give the government an effective practical data reference in the sustainable development of utilization management and ecological reclamation in coalfield. [ABSTRACT FROM AUTHOR]