Regional scale terrace mapping in fragmented mountainous areas using multi-source remote sensing data and sample purification strategy.

As a stepped cross section of farmland built along the contour lines, terrace is widely distributed on hill-slopes. It changes the original surface slope and runoff coefficient, reduces soil nutrient loss, and has become the most important soil erosion control measure in China. Accurate terrace mapping at regional scale is crucial for soil conservation, agriculture sustainability and ecological planning. Due to the influence of cloudy and rainy weather, poor data availability makes it difficult to identify terrace distribution only using optical remote sensing images in mountainous areas. In this study, we incorporated multi-spectral optical and SAR data, features of terrain, texture and time sequence information, and proposed a pixel-based supervised classification method based on sample purification strategy to obtain a 10 m resolution terraced map in a plateau mountainous region. With 610 terrace/non-terrace validation sample data, 10-fold cross-validation was used to test the classification results. For identified terrace, the values of Overall Accuracy (OA), Producer's Accuracy (PA) and User's Accuracy (UA) stay stable above 90 %, the F1 score and Kappa coefficient show the smallest fluctuation and is stable in the range of 0.90-0.93 and 0.81-0.87, respectively. The accuracy evaluation of grid units show that the uncertainty of the terrace distribution is mainly concentrated in the north and south of the study area. Slope cultivated land, low-slope terrace and non-agricultural vegetation are easily mixed due to the heterogeneity of terrace features and the spectrum similarity among these land types. It should be noted that the features of time series and texture play a key role in the terrace recognition process, rather than terrain factors, which is different from previous studies. The sample purification strategy proposed provides a more reliable regional scale terrace distribution map compared to the existing product, and is potentially transferable to other mountainous areas as a robust approach for rapid identification of terrace.

Dynamic monitoring of aboveground biomass in inner Mongolia grasslands over the past 23 Years using GEE and analysis of its driving forces.

Aboveground biomass (AGB) in grasslands directly reflects the net primary productivity, making it a sensitive indicator of grassland resource quality and ecological degradation. Accurately estimating AGB over large regions to reveal long-term AGB evolution trends remains a formidable challenge. In this study, we divided Inner Mongolia Autonomous Region (IMAR) grasslands into three study regions based on their spatial distribution of grassland types. We combined remote sensing data with ground-based sample data collected over the past 19 years from 6114 field plots using the Google Earth Engine platform. We constructed random forest (RF) and traditional regression AGB inversion models for each region and selected the best-performing model through accuracy assessment to estimate IMAR grassland AGB for the period 2000-2022. We also examined the trends in AGB changes and identified the driving forces affecting IMAR grasslands through the application of Theil-Sen estimation, Mann-Kendall trend analysis, and the Geodetector model. The main findings are as follows: (1) Compared with the univariate parametric traditional regression model, the AGB monitoring accuracy of the multivariate non-parametric RF model in the three study regions increased by 5.94%, 5.08% and 19.14%, respectively. (2) The average AGB per unit area of IMAR grasslands from 2000 to 2022 was 731.41 kg/hm2, with alpine meadow having the highest average AGB (1271.70 kg/hm2) and temperate grassland desertification having the lowest (469.06 kg/hm2). IMAR grasslands exhibited an overall increasing trend in AGB over the past 23 years (6.01 kg/hm2•yr), with the increasing trend covering 83.52% of the grassland area and the decreasing trend covering 16.48%. (3) Spatially, IMAR grassland AGB showed a gradual decline from northeast to southwest and exhibited an increasing trend with increasing longitude (45.423 kg/hm2 per degree) and latitude (71.9 kg/hm2 per degree). (4) Meteorological factors were the most significant factors affecting IMAR grassland AGB, with precipitation (five-year average q value of 0.61) being the most prominent. In the western part of IMAR, where precipitation is consistently limited throughout the year, the primary drivers of influence were human activities, with particular emphasis on the number of livestock (with a five-year average q value of 0.44). It is evident that reducing human activity disturbance and pressure in fragile grassland areas or implementing near-natural restoration measures will be beneficial for the sustainable development of grassland ecosystems. The results of this research hold substantial reference importance for the protection and restoration of grasslands, the supervision and administration of grassland resources, as well as the development of policies related to grassland management.

Chinese cropland losses due to urban expansion in the past four decades.

Since 1970s, China has experienced the large-scale losses of croplands during urban expansion process, which has drawn great attentions to Chinese government. Although in-depth studies about cropland losses have been executed widely, relatively little attention has been paid to describe long term and high frequency influences of urban expansion on it and reveal its differences systematically. Based on remote sensing and GIS technology, we quantified, analyzed, and mapped cropland losses in China due to urban expansion from the national, administrative-level, population-size, and city scales. Results indicated that (1) Since the 1970s, croplands were the primary contributor to urban expansion in China, and their losses due to urban expansion underwent five obvious stages. The consciousness of cropland protection is being strengthened continuously and has developed from the initial to the deep execution stages. (2) Cropland losses were unbalanced in China, with the loss magnitude, rate, and influences on urban expansion positively related to the administrative-level and large population-size. That is, obvious losses always emerged in cities with high administrative-level and large population-size. (3) Seven basic trends of cropland losses were quantitatively recognized, which was conducive to the formulation of different policies or strategies for cropland protection for different cities.