Remote sensing estimation of the soil erosion cover‐management factor for China's Loess Plateau (original) (raw)
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Due to the impoundment of the Yangtze River, the Three Gorges Dam in China fosters high land-use dynamics. Soil erosion is expected to increase dramatically. One of the key factors in soil erosion control is the vegetation cover and crop type. However, determining these factors adequately for the use in soil erosion modeling is very time-consuming especially for large mountainous areas, such as the Xiangxi (香溪) catchment in the Three Gorges area. In our study, the crop and management factor C was calculated using the fractional vegetation cover (C FVC ) based on Landsat-TM images from 2005, 2006, and 2007 and on literature studies (C LIT ). In 2007, the values of C FVC range between 0.001 and 0.98 in the Xiangxi catchment. The mean C FVC value is 0.05. C LIT values are distinctly higher, ranging from 0.08 to 0.46 with a mean value of 0.32 in the Xiangxi catchment. The mean potential soil loss
Vegetation fraction is an input parameter to scale the vegetation cover on the ground for soil loss equation. In order to calculate the yearly volume of losing soil in the Upper Basin of Miyun Reservoir in the north of Beijing, China, it is necessary to develop a model to estimate vegetation fraction using remote sensing technology. Based on the analysis on the existing methods of measuring vegetation fraction, an improved Dimidiate Pixel Model was developed to estimate vegetation fraction from normalized difference vegetation index (NDVI) derived from Landsat TM images, and a new method was also brought forward to quantify NDVI thresholds of soil and vegetation. The vegetation fraction data have been estimated using improved model in the study area, and was validated with the field survey data. The result shows that the improved model could satisfy with the need for quantifying vegetation cover parameter from TM imagery for soil loss equation.
Environmental Management, 2010
Land degradation due to erosion is one of the most serious environmental problems in China. To reduce land degradation, the government has taken a number of conservation and restoration measures, including the Sloping Land Conversion Program (SLCP), which was launched in 1999. A logical question is whether these measures have reduced soil erosion at the regional level. The objective of this article is to answer this question by assessing soil erosion dynamics in the Zuli River basin in the Loess Plateau of China from 1999 to 2006. The MMF (Morgan, Morgan and Finney) model was used to simulate changes in runoff and soil erosion over the period of time during which ecological restoration projects were implemented. Some model variables were derived from remotely sensed images to provide improved land surface representation. With an overall accuracy rate of 0.67, our simulations show that increased ground vegetation cover, especially in forestlands and grasslands, has reduced soil erosion by 38.8% on average from 1999 to 2006. During the same time period, however, the change in rainfall pattern has caused a 13.1% ± 4.3% increase in soil erosion, resulting in a net 25.7% ± 8.5% reduction in soil erosion. This suggests that China's various ecological restoration efforts have been effective in reducing soil loss.
Remote Sensing Letters, 2016
This letter explores factors affecting the quantification and mapping of forest canopy fractional cover (CFC), and explores causes of CFC change. CFC was quantified using a simple linear mixture model based on the modified soil-adjusted vegetation index (MSAVI) derived from Landsat TM surface reflectance data of Fanjingshan National Nature Reserve (FNNR) in China. Different soil and vegetation endmembers were tested to analyse the sensitivity of the mixture model. Illumination effects due to topographical variability are found to influence MSAVI and therefore CFC estimates. Implementing an illumination stratification that selects different closed canopy endmembers for different topographicrelated illumination strata generally minimizes these effects. The spatial distribution and possible causes of CFC change were examined. Most changes in CFC over the 15-year study period appear to have resulted from anthropogenic activities, at least based on the precision constraints of Landsat-derived CFC change estimates and limited high spatial resolution imagery used in a mostly visual verification of patches with low CFC and reduction in CFC between image dates.
Environmental Earth Sciences, 2014
In soil and water conservation research, vegetation is considered to be a primary factor affecting soil erosion. Many studies focus on the relationship between soil erosion and a given attribute of vegetation. Few studies have attempted a comprehensive analysis of vegetation attributes. Thus, the aim of this study is to explain the relationship between vegetation and soil erosion in detail. We studied 104 vegetation plots and 104 soil samples in the Yangjuangou catchment, Loess Plateau, Shaanxi Province, China. According to a correlation analysis of the vegetation attributes and soil 137 Cs inventories, vegetation cover exerts a positive effect on soil erosion. In addition, vegetation aggregation increases with increasing soil loss. During this period of study, plant diversity can have different relationships with soil erosion according to the vegetation pattern. When vegetation distribution is relatively homogeneous, plant cover decreases with increasing diversity, and the soil loss increases. When vegetation pattern distributes between homogeneous and heterogeneous, the relationship between vegetation diversity and soil erosion is not obvious. When vegetation distribution is in a heterogeneous pattern, cover increases with increasing diversity, and soil loss decreases.
Remote Sensing, 2018
Northern China is one of the most sensitive and vulnerable regions in the country. To combat environmental degradation in northern China, a series of vegetation protection programs, such as the Three-North Shelter Forest Program (TNFSP), have been implemented. Whether the implementation of these programs in northern China has improved the vegetation conditions has merited global attention. Therefore, quantifying vegetation changes in northern China is essential for meteorological, hydrological, ecological, and societal implications. Fractional vegetation cover (FVC) is a crucial biophysical parameter which describes land surface vegetation conditions. In this study, four FVC data sets derived from remote sensing data over northern China are employed for a spatio-temporal analysis to determine the uncertainty of fractional vegetation cover change from 2001 to 2012. Trend analysis of these data sets (including an annually varying estimate of error) reveals that FVC has increased at the rate of 0.26 ± 0.13%, 0.30 ± 0.25%, 0.12 ± 0.03%, 0.49 ± 0.21% per year in northern China, Northeast China, Northwest China, and North China during the period 2001-2012, respectively. In all of northern China, only 33.03% of pixels showed a significant increase in vegetation cover whereas approximately 16.81% of pixels showed a significant decrease and 50.16% remained relatively stable.
2010
This study builds on results from our analysis of a time series of the global MODIS Fraction of Absorbed Photosynthetically Active radiation that a plant canopy absorbs, and a relationship between the product to Landsat TM and ETM+ green and non-green fractions of ground cover and vegetation structural categories in the dry tropical savannas in Queensland, Australia. In a multiple regression analysis (including interaction terms) 75% of the variability in dry season MODIS FPAR was explained by the Landsat datasets. The vegetation structural categories were determined through classes of Landsat woody foliage projective cover. Based on those findings, we developed three schemes to derive high temporal, remotely sensed cover factor estimates integrating the MODIS FPAR with the Landsat products, and ICESat canopy height estimates for potential use in future erosion modelling. Those cover factor schemes are the first of their kind, and a future study will enable their validation.
Soil erosion is a serious environmental problem in Northern Thailand causing a threat to sustainable agriculture. Faulty agricultural practices, high annual rainfall and the undulating topography of Northern Thailand are the key factors contributing to a high annual rate of soil erosion. For finding the status and extent of the erosion problem in the Mae Ao watershed of Northern Thailand, the annual rate of soil erosion was estimated using remote sensing data. Since the vegetative cover is a major factor of soil erosion, therefore, a Normalized Difference Vegetation Index (NDVI) derived from Remote Sensing data (Landsat-TM) was used in this study to assess the vegetative cover in the watershed. A soil erosion model was developed to integrate NDVI and land slope for estimating the annual soil erosion rate. Results indicated that the changes in agricultural pattern from traditional crops to orchard plantation and adoption of soil conservation measures in the Mae Ao watershed decreased...
Remote Sensing, 2015
Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor