The study investigated wetland change in Wanning. For this purpose, three high resolution SPOT images recorded in 2002, 2007 and 2013, respectively, were classified. The results indicated that there were little change in wetland types during 2002 and 2013. The coastal waters, culture pond and river were the main wetland types. The natural wetland trended to decline. The ditch had the largest net increase and the reservoir shrank the most. There was a dramatic increase of culture pond plaques, which making the landscape more fragmentized. The coastal waters and the land had a lot change with other wetland types. The area change in Wanning was mainly composed of the transition between the land and culture pond.
The high spatial heterogeneity forms a major uncertainty in accurately monitoring of vegetation
coverage. In this study, an optimal zoning approach with dividing the whole heterogeneous
image into relatively homogeneously segments was proposed to reduce the effects of high
heterogeneity on vegetation coverage estimation. With the combination of the spectral
similarity of the adjacent pixels and spatial autocorrelation of the segments, the optimal zoning
approach accounted for the intrasegment uniformity and intersegment disparity of improved
image segmentation. In comparison, vegetation coverage in the highly heterogeneous karst
environments tended to be underestimated by the normalized difference vegetation index
(NDVI) and overestimated by the normalized difference vegetation index-spectral mixture
analysis (NDVI-SMA) model. Hence, when applying remote sensing for highly heterogeneous
environments, the influence of high heterogeneity should not be ignored. Our study indicates
that the proposed model, using NDVI-SMA model with improved segmentation, is found to
ameliorate the effects of the highly heterogeneous environments on the extraction of vegetation
coverage from hyperspectral imagery. The proposed approach is useful for obtaining accurate
estimations of vegetation coverage in not only karst environments but also other environments
with high heterogeneity.
In china, especially in the North and Northwest, many cities suffer sand-dust or sand-storm attacks in winter and spring. There are two sand sources forming the bad weather, local sand source and other source out of local area. The second kind source needs state level activities to control sand movement and recover local ecological environment. For the local government, it should pay much attention to decreasing its inner sand & dust source, because the sand-stormy or sand-dusty weather causing by the local sand source usually comes abruptly and brings much damage frequently, and it is also hard to forecast it. However, people always emphasize all-year bare land controlling and pay less attention to seasonal bare land (especially in winter and spring seasons) which caused by unreasonable agricultural pattern. In this paper, taking Beijing as the test area, using MODIS vegetation index time-series data, all-year and seasonal bare land had been classified. The data set used was 16-day composited EVI time-series with a 250m spatial resolution. After filtered and reconstructed, this paper applied the parallelepiped classification algorithm to the data set, and emphasized the all-year bare land with lower EVI value and seasonal bare land with lower EVI value just in spring and winter. Taking accord of local terrain, infield was the main part of seasonal bare land. The experiment result showed that all-year bare land mainly distributes in northwest Beijing, the joint area of Beijing, Shanxi and Inner Mongolia, especially in Inner Mongolia. Seasonal bare land mainly distributes in northwest and west Beijing, gathering in northwest Hebei, east Shanxi; there are also some ones in Daxing, fangshan, changpin, yanqing, miyun, shunyi, and tongzhou areas of Beijing. These two kind bare lands were all possible contributor to sand storm or sand dust weather in Beijing. Considering wind direction and terrain information of Beijing area, some possible sand source could be found. This paper emphasized the research of seasonal bare land, which is easily neglected in common work for sand and dust prevention. The paper considered that much attention should be paid to change the original cultivation pattern through the reasonable guidance, to realize the balance between the economic efficiency and environment benefit, and finally, reduce the local sand dust harm gradually by such development strategy. For the governors in north China, it is also essential to take accord of this factor when designing city development plan.
Vegetation fraction (VF) is the indispensable factor involved in the assessment of land degradation in the inclement climate condition and harsh natural environment. Based on the analysis of an in situ spectral dataset of alpine grasslands on the Tibetan plateau, we assessed the performance of 28 widely used vegetation indices (VIs) and a spectral mixture analysis (SMA) model applied on the analytical spectral device and simulated enhanced thematic mapper (ETM)+ and Huan Jing (HJ)-1 data to select a method for retrieving VF there. The results show that simple VIs are competent for extracting VF information, and VIs with an extra blue band involved will produce a better performance. However, involvement of too many more bands does not yield much higher accuracy, indicated by the fact that hyperspectral VIs are not superior to multispectral ones in our case. The SMA model provides an acceptable accuracy as well but lower than that of VI regression. In addition, the normalized difference vegetation index (NDVI) values of vegetation and soil, generally, as the key parameter in the widely used NDVI-SMA model is obtained, and this would benefit the application of this model to derive VF of alpine grasslands on the Tibetan plateau with minimal or no need for field work support.
Trends in vegetation change and their relationships with terrain conditions are significant to understand and evaluate the efficiency of ecological engineering implemented in karst regions, Southwest China. This study aimed to identify vegetation change trends in Hechi, Guangxi, China using time-series of SPOT-VGT NDVI data (1999-2010) and DEM. Linear trend analysis was applied to examine NDVI change trends. The results indicated that most of NDVI values had increased during this time period. There were spatial variations in NDVI change trends, which could be partiallly explained by different karst terrain conditions. The areas of most obviously positive trends in NDVI change were found at the elevation of 500-1000m and the relief amplitude between 200 and 500 m. Negative trends in NDVI change were appeared on slopes of south (sunlit) and west (semi-sunlit) aspect and at the elevation of 200 - 500 m, where were mainly due to human activities.
Karst rocky desertification is a special kind of land desertification developed under violent human impacts on the
vulnerable eco-geo-environment of karst ecosystem. The process of karst rocky desertification results in simultaneous
and complex variations of many interrelated soil, rock and vegetation biogeophysical parameters, rendering it difficult to
develop simple and robust remote sensing mapping and monitoring approaches. In this study, we aimed to use Earth
Observing 1 (EO-1) Hyperion hyperspectral data to extract the karst rocky desertification information. A spectral
unmixing model based on Monte Carlo approach, was employed to quantify the fractional cover of photosynthetic
vegetation (PV), non-photosynthetic vegetation (NPV) and bare substrates. The results showed that SWIR (1.9-2.35μm)
portions of the spectrum were significantly different in PV, NPV and bare rock spectral properties. It has limitations in
using full optical range or only SWIR (1.9-2.35μm) region of Hyperion to decompose image into PV, NPV and bare
substrates covers. However, when use the tied-SWIR, the sub-pixel fractional covers of PV, NPV and bare substrates
were accurately estimated. Our study indicates that the "tied-spectrum" method effectively accentuate the spectral
characteristics of materials, while the spectral unmixing model based on Monte Carlo approach is a useful tool to
automatically extract mixed ground objects in karst ecosystem. Karst rocky desertification information can be accurately
extracted with EO-1 Hyperion. Imaging spectroscopy can provide a powerful methodology toward understanding the
extent and spatial pattern of land degradation in karst ecosystem.