A new high-resolution global topographic factor dataset calculated based on SRTM.

Topography is an important factor affecting soil erosion and is measured as a combination of the slope length and slope steepness (LS-factor) in erosion models, like the Chinese Soil Loss Equation. However, global high-resolution LS-factor datasets have rarely been published. Challenges arise when attempting to extract the LS-factor on a global scale. Furthermore, existing LS-factor estimation methods necessitate projecting data from a spherical trapezoidal grid to a planar rectangle, resulting in grid size errors and high time complexity. Here, we present a global 1-arcsec resolution LS-factor dataset (DS-LS-GS1) with an improved method for estimating the LS-factor without projection conversion (LS-WPC), and we integrate it into a software tool (LS-TOOL). Validation of the Himmelblau-Orlandini mathematical surface shows that errors are less than 1%. We assess the LS-WPC method on 20 regions encompassing 5 landform types, and R2 of LS-factor are 0.82, 0.82, 0.83, 0.83, and 0.84. Moreover, the computational efficiency can be enhanced by up to 25.52%. DS-LS-GS1 can be used as high-quality input data for global soil erosion assessment.

Effects of Content of Soil Rock Fragments on Soil Erodibility in China.

Soil erosion is serious in China-the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.

Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in the Wei and Jing River Basins.

This study aimed to explore the long-term vegetation cover change and its driving factors in the typical watershed of the Yellow River Basin. This research was based on the Google Earth Engine (GEE), a remote sensing cloud platform, and used the Landsat surface reflectance datasets and the Pearson correlation method to analyze the vegetation conditions in the areas above Xianyang on the Wei River and above Zhangjiashan on the Jing River. Random forest and decision tree models were used to analyze the effects of various climatic factors (precipitation, temperature, soil moisture, evapotranspiration, and drought index) on NDVI (normalized difference vegetation index). Then, based on the residual analysis method, the effects of human activities on NDVI were explored. The results showed that: (1) From 1987 to 2018, the NDVI of the two watersheds showed an increasing trend; in particular, after 2008, the average increase rate of NDVI in the growing season (April to September) increased from 0.0032/a and 0.003/a in the base period (1987-2008) to 0.0172/a and 0.01/a in the measurement period (2008-2018), for the Wei and Jing basins, respectively. In addition, the NDVI significantly increased from 21.78% and 31.32% in the baseline period (1987-2008) to 83.76% and 92.40% in the measurement period (2008-2018), respectively. (2) The random forest and classification and regression tree model (CART) can assess the contribution and sensitivity of various climate factors to NDVI. Precipitation, soil moisture, and temperature were found to be the three main factors that affect the NDVI of the study area, and their contributions were 37.05%, 26.42%, and 15.72%, respectively. The changes in precipitation and soil moisture in the entire Jing River Basin and the upper and middle reaches of the Wei River above Xianyang caused significant changes in NDVI. Furthermore, changes in precipitation and temperature led to significant changes in NDVI in the lower reaches of the Wei River. (3) The impact of human activities in the Wei and Jing basins on NDVI has gradually changed from negative to positive, which is mainly due to the implementation of soil and water conservation measures. The proportions of areas with positive effects of human activities were 80.88% and 81.95%, of which the proportions of areas with significant positive effects were 11.63% and 7.76%, respectively. These are mainly distributed in the upper reaches of the Wei River and the western and eastern regions of the Jing River. These areas are the key areas where soil and water conservation measures have been implemented in recent years, and the corresponding land use has transformed from cultivated land to forest and grassland. The negative effects accounted for 1.66% and 0.10% of the area, respectively, and were mainly caused by urban expansion and coal mining.

The pattern, change and driven factors of vegetation cover in the Qin Mountains region.

The Qin Mountains region is one of the most important climatic boundaries that divide the North and South of China. This study investigates vegetation covers changes across the Qin Mountains region over the past three decades based on the Landsat-derived Normalized Difference Vegetation Index (NDVI), which were extracted from the Google Earth Engine (GEE). Our results show that the NDVI across the Qin Mountains have increased from 0.624 to 0.776 with annual change rates of 0.0053/a over the past 32 years. Besides, its abrupt point occurred in 2006 and the change rates after this point increased by 0.0094/a (R2 = 0.8159, p < 0.01) (2006-2018), which is higher than that in 1987-1999 and 1999-2006. The mean NDVI have changed in different elevation ranges. The NDVI in the areas below 3300 m increased, such increased is especially most obviously in the cropland. Most of the forest and grassland locate above 3300 m with higher increased rate. Before 2006, the temperature and reference evapotranspiration (PET) were the important driven factors of NDVI change below 3300 m. After afforestation, human activities become important factors that influenced NDVI changes in the low elevation area, but hydro-climatic factors still play an important role in NDVI increase in the higher elevations area.

Hydrological Response to Precipitation and Human Activities-A Case Study in the Zuli River Basin, China.

Precipitation and human activities are two essential forcing dynamics that influence hydrological processes. Previous research has paid more attention to either climate and streamflow or vegetation cover and streamflow, but rarely do studies focus on the impact of climate and human activities on streamflow and sediment. To investigate those impacts, the Zuli River Basin (ZRB), a typical tributary basin of the Yellow River in China, was chosen to identify the impact of precipitation and human activities on runoff and sediment discharge. A double mass curve (DMC) analysis and test methods, including accumulated variance analysis, sequential cluster, Lee-Heghnian, and moving t-test methods, were utilized to determine the abrupt change points based on data from 1956 to 2015. Correlation formulas and multiple regression methods were used to calculate the runoff and sediment discharge reduction effects of soil conservation measures and to estimate the contribution rate of precipitation and soil conservation measures to runoff and sediment discharge. Our results show that the runoff reduction effect of soil conservation measures (45%) is greater than the sediment discharge reduction effect (32%). Soil conservation measures were the main factor controlling the 74.5% and 75.0% decrease in runoff and sediment discharge, respectively. Additionally, the contribution rate of vegetation measures was higher than that of engineering measures. This study provides scientific strategies for water resource management and soil conservation planning at catchment scale to face future hydrological variability.

Landscape dynamitic change in Mu Us Desert derived from Landsat TM data.

Landscape ecology emphasizes large areas and ecological effects of the spatial patterning of ecosystem. Recent developments in landscape ecology have emphasized the important relationship between spatial patterns and many ecological processes. Quantitative methods in landscape ecology link spatial patterns and ecological processes at broad spatial and temporal scales. In turn the increased attention on temporal change of ecosystem has highlighted the need for quantitative methods that can analyze patterns. This research applies quantitative methods--change detection to assess the ecosystem temporal change in the arid and semiarid area. Remote sensing offers the temporal change of ecosystem on landscape characteristics.

[Spatial and temporal distributions of rainfall erosivity in the Yanhe River Basin].

Rainfall erosivity (R) is a measure of effects of the rainfall factor on the potential capacity for soil water erosion. The characteristic of spatial and temporal of R value is the basis for soil erosion prediction. The spatial and temporal distributions of rainfall erosivity were analyzed using daily rainfall data collected between 1980 and 2003 from 22 rainfall stations located across the Yanhe River Basin. The results showed that the seasonal distribution of R values formed a single peak, concentrated between May and September and accounted for 91.61% of the total annual R value. The changes in annual average rainfall erosivity followed a similar trend to those for the annual average rainfall and erosive rainfall. The average annual R value was 1580.58 MJ x mm x (hm2 x h x a)(-1), the maximum annual rainfall erosivity (1981) was 2417.70 MJ x mm x (hm2 x h x a)(-1) and the minimum (1999) was 585.29 MJ x mm x (hm2 x h x a)(-1). The variation coefficient of annual rainfall erosivity was the medium with a value of 0.32. The annual rainfall erosivity was the greatest at the Shaofangbian station with a value of 2190.33 MJ x mm x (hm2 x h x a)(-1), while the least erosivity was found at the Liandaowan and Yangshan station with values of 1151.37 MJ x mm (hm2 x h x a)(-1) and 1146.87 MJ x mm x (hm2 x h x a)(-1), respectively. The annual average R values had a similar trend of spatial distribution with annual average erosive rainfall. The annual changes in R values increased at the rainfall stations of north, while those were found to decrease at other stations of Yanhe River Basin. Overall, the annual rainfall erosivity tended to decrease in the Yanhe River Basin with the trend coefficient value of -0.004.

[Land use/land cover change and their ecological effect in small watershed on gully region of the Loess Plateau].

Based on land use data of 1994 and 2004 in Wangdonggou watershed, through developing dynamic model of LUCC and indices of regional ecological environment, this paper quantified the characteristics of LUCC and its ecological effect. The results showed that from 1994 to 2004, farmland decreased while grassland and orchard increased greatly, forest and nonproductive land changed little. The speed of individual land use changes was in the order of grassland > orchard > nonproductive land > farmland > forest land. As to the spatial change, a total of 11 major land use change types were identified, among which the change from farmland to others and form others to forest were the most important land use change. The gravitational center of farmland and orchard moved to the northwest tableland, while those of forest land and grassland moved to the southeast gully land. From 1994 to 2004, owing to LUCC, the ecological environment was improved, but the type of land use and their change took effect differently with both improving and decreasing effect. The decrease of farmland affected ecological environment negatively, while the increase of forest and grassland and orchard improved the ecological environment and its ecosystem service values. Those improving the ecological environment were from other types of land use to forest and orchard, while those worsening the ecological environment were from forest to others and from farmland to nonproductive land.

[Dynamic monitoring on land cover in loess hilly and gully area by the methods of RS and GIS].

By the integration of RS and GIS, with the TM data obtained from Landsat 5 in 1990 and the ETMplus data from Landsat 7 in 2000, and after geometrical rectifying and amplifying and man-machine screen decoding data image, the map of eco-environment and land use/cover in Yanhe watershed and its vicinities in 1990 and 2000 was obtained respectively, and the results of statistic analyses showed that in comparing with 1990, the artificial land in 2000 increased by 31%, the area of agricultural land was stable and that of orchard was 2.82 times more, and the area of artificial forest increased remarkably and that of shrub was 6 times more. The coverage rate increased to 13.88%, and the area of wilderness was 44.8% of that in 1990. The quality of eco-environment was improved remarkably in this area.