Ecological land provides goods and services that have direct or indirect benefic to eco-environment and human welfare. In recent years, researches on ecological land have become important in the field of land changes and ecosystem management. In the study, a multi-scale classification scheme of ecological land was developed for land management based on combination of the land-use classification and the ecological function zoning in China, including eco-zone, eco-region, eco-district, land ecosystem, and ecological land-use type. The geographical spatial unit leads toward greater homogeneity from macro to micro scale. The term “ecological land-use type” is the smallest one, being important to maintain the key ecological processes in land ecosystem. Ecological land-use type was categorized into main-functional and multi-functional ecological land-use type according to its ecological function attributes and production function attributes. Main-functional type was defined as one kind of land-use type mainly providing ecological goods and function attributes, such as river, lake, swampland, shoaly land, glacier and snow, while multi-functional type not only providing ecological goods and function attributes but also productive goods and function attributes, such as arable land, forestry land, and grassland. Furthermore, a six-level grid encoding mode was proposed for modern management of ecological land and data update under cadastral encoding. The six-level irregular grid encoding from macro to micro scale included eco-zone, eco-region, eco-district, cadastral area, land ecosystem, land ownership type, ecological land-use type, and parcel. Besides, the methodologies on ecosystem management were discussed for integrated management of natural resources in China.
Impervious surface is an important part of urban underlying surface, as well as an important monitoring index for city ecological system and environment changes. However, accurate impervious surface extraction is still a challenge. This paper uses the color, shape and overall heterogeneity features from the high spatial resolution remote sensing image to extract the impervious surface. An edge-based image segmentation algorithm is put forward to fuse heterogeneous objects which integrates edge features and multi-scale segmentation algorithm and uses the edge information to guide image objects generation. Results showed that this method can greatly improve the accuracy of image segmentation. Accuracy assessment indicated that the overall impervious surface classification accuracy and a Kappa coefficient yield 87% and 0.84, respectively.
The Donghe basin is one of the typical regions with serious water and soil erosion in Kaixian county of Chongqing city.
In this study the spatial distribution variation of water and soil erosion in Donghe basin is analyzed based on a
comprehensive method that integrates SWAT model with a Geographic Information System and remote sensing
technology. SWAT is a physical based model that requires specific information about weather, soil properties,
topography, vegetation, and land management practices occurring within the watershed. Firstly, with the Donghe basin
as a study area, using the spatially distributed and mechanism-based SWAT model, the distributed hydrological and
sediment model are developed to simulate the runoff and sediment production of Donghe basin. Then the model is
calibrated and validated against observed runoff and sediment data from 2003 to 2004, the validated result shows a
deterministic coefficient of 0.93. Finally the spatial distribution of the water and soil erosion of Donghe is analyzed. The
results show that the mean sediment production of Donghe basin is 30.7 t/ha•a, the maximum sediment production of its
sub-watersheds is 212.7 t/ha•a, and the minimum is 0.3 t/ha•a. There is an obviously clustering feature of sub-watersheds distribution with different sediment production level. The area of the high erosion, strong erosion and violent erosion account for 30% of the whole basin area, the other soil erosion area occupy 70%.
For the extraction of land degradation information we should use not only information on climate, soil, vegetation,
physiognomy, land use and its productivities, but also the knowledge and methodologies of geosciences. It is of
importance to study some conceptual issues about geographic image cognition (GEOIC) on studying land degradation.
The study is to discuss some conceptual issues and the theoretical background of the approach of geographic image
cognition (GEOIC) on studying land degradation for building its methodological framework. Some issues concerning the
approach of GEOIC on studying land degradation, especially the factors of impacting human's visual cognition, were
discussed. The results indicated that the GEOIC is the objectification cognition on remote sensing images and multi-source
information using geo-knowledge. As an integrated approach, it is the extension of the methodology of OBIA.
The key objective of the GEOIC on studying land degradation is to simulate the function and process of the visual interpretation by experts, and extract spatial features, spatial object and spatial pattern of land degradation under the cognition mode of feature-object-pattern from remote sensing images and multi-source information. The methodology of the GEOIC is realized through the segmentation of geo-objects or meaningful image objects using remote sensing information, geographic information, vegetation, soil, and other ancillary information with geosciences knowledge and intelligence.
China's land resources are extremely scarce. There is a pressing need for building the technical system of land survey
and monitoring for detail knowledge about the current situation of land use, land value and land property right of each
piece of land in the whole country for land use and management. Many works of land survey and monitoring in China
have been finished for providing references directly for the macro decision-making and making the national economic
and social development planning. However, there were limits in integrity, systemization, and standardization, and some
of works about survey and monitoring were carried out in the early period and their results were not updated in time. The
purpose of the paper is to establish the framework of systemized technical system of land survey and monitoring for
guidance of future national work of land survey and monitoring. The study was through comparing and analyzing the
past and ongoing projects of land survey and monitoring. Results indicated that the technical system is constituted by 5
sub-systems. The system will integrate land survey and monitoring, land evaluation, and information sharing service into
a whole. The regional arrangement of land survey and monitoring was proposed. Seven implementation regions of land survey and monitoring were divided, including the northeastern region, eastern coastal region, central region, southwestern region, northwestern region, Xinjiang Urgur region, and Qinghai-Tibetan region. The survey and monitoring objectives and contents in each region are different. The zoning is for guidance of the future project arrangement about national land survey and monitoring in China based on land resources background and economic development demands.
Desertification is one of the most serious ecological and environmental problems in China, especially in the arid and semi-arid area of China. Based on investigation of current research and previous efforts on desertification, in this paper we propose a desertification index system suitable for large-scale desertification monitoring using remote sensing techniques. According to the desertification index design principle, we selected five desertification indexes (MSAVI, FVC, Albedo, LST and TVDI) suitable for large-scale desertification monitoring using remote sensing technique. After applying different index and index combinations on desertification monitoring and its precision evaluation in test area, the result shows that the precision of index combination of MSAVI, FVC, Albedo, LST and TVDI is superior than others. Based on analysis and comparison of current retrieval algorithms, we utilized a suitable algorithm on large scale to retrieve five desertification indexes with ten-day NOAA AVHRR data set in 1995 and 16-day MODIS data set in 2001. In term of the desertification climate types, the potential extent of desertification in China was respectively divided into four categories: dry sub-humid area, semi-arid area, arid area, high and cold area. Different desertification index system was built for each area. By assessing the classification accuracies of three types of classifiers (unsupervised classifier, maximum likelihood classifier and decision tree classifier), we select decision tree classifier for desertification monitoring. Supported by desertification index system and the database of desertification indexes, the desertification status in 1995 and 2001 was classified by decision tree classifier, and analysis of desertification changes from 1995 to 2001 was also completed in study area. Statistical result according to individual country shows that the speed of desertification developing is faster than that of rehabilitating, there is a trend of development as a whole and improving locally in desertificated areas in China.
The primary purpose of this study was to estimate the boundary between vegetated and non-vegetated areas and to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study are a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator to desertification assessment. Vegetation is very sparse in desert and therefore onset of green-up in the desert was undetectable with AVHRR NDVI data. The occurrence of onset of green-up, as determined with time series NDVI data was used to identify desert and non-desert areas. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) is used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of samples data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over 20-year period for which data was available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification.
The commonly presented speckle in Synthetic Aperture Radar (SAR) imagery makes it very difficult to be visually interpreted or directly used for quantitative application. Therefore, speckle reduction is a prerequisite for many SAR image processing and application projects. In this paper, we discuss a non-linear adaptive algorithm in detail. The key point of the algorithm is to sort the pixel values within a local window, to compute the sequences of standard deviations and means, and then to take the normalized differences between two successive standard deviation/mean ratios. Speckle detection is achieved by thresholding these differences. Speckle noise suppression is achieved by replacing the pixel’s digital value with rank-ordered local statistics. Experimental results shows that the proposed method demonstrates promising performance in comparison to some conventional SAR speckle filters in terms of the edge preservation, mean preservation, variance reduction and the visual effect.