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.
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.
Desertification is an alarming sign of land degradation in Henshan county of northwest china. Due to the considerable
costs of detailed ground surveys of this phenomenon, remote sensing is an appropriate alternative for analyzing and
evaluating the risks of the expansion of land degradation. Degradation features can be detected directly or indirectly by
using image data. In this paper, based on the Hyperion images of Hengshan desertification region of northwest china, a
new algorithm aimed at land degradation mapping, called Land Degradation Index (LDI), was put forward. This new
algorithm is based on the classified process. We applied the linear spectral unmixing algorithm with the training samples
derived from the formerly classified process so as to find out new endmembers in the RMS error imagine. After that,
using neutral net mapping with new training samples, the classified result was gained. In addition, after applying mask
processing, the soils were grouped to 3 types (Kappa =0.90): highly degraded soils, moderately degraded soils and
slightly degraded soils. By analyzing 3 mapping methods: mixture-classification, the spectral angle mapper and mixturetuned
matched filtering, the results suggest that the mixture-classification has the higher accuracy (Kappa=0.7075) than
the spectral angle mapper (Kappa=0.5418) and the mixture-tuned matched filter (Kappa=0.6039). As a result, the
mixture-classification is selected to carry out Land Degradation Index analysis.
Map objects' displacement is an important operator in the map generalization and spatial information visualization. In this
paper, the constraints of map objects displacement and their satisfaction method with the finite element method are discussed,
in which the constraints of map objects displacement and strategies of parameters of finite element method in the
displacement process play important roles for the displacement result, and compromised with the spatial reasoning method, a
progressive node displacement method is discussed in this paper, which is examined with a road widening case. And the
result shows that the force and the force propagation can be considered comprehensively in this method, through which
unnecessary map object displacement can be avoided.
This paper analyzed land use change in Bashang area of Hebei province, with 1992 and 2002 TM (ETM). Based on GIS and statistical methods, the intensity, state index of land use change and transfer matrix were used to study spatio-temporal land use change in the region. The results showed that the area of arable land decreased greatly, also the area of wetland decreased. The other way round, the area of grassland, forest land and building land increased. As a whole, the intensity of forest land change was higher, but others were lower. From the transfer matrix, most of the arable land changed into grassland and forest land, some to building land. The grassland and forest land was mainly transferred from unused land, except for arable land. The building land mostly came from arable land. The wetland was used for grass and forest area. It was showed that the eco-environment degraded, and the land use change was an important driving force of eco-environment change in the study area. Unfeasible land use pattern and land reclamation by human beings resulted in soil loss and sandy land increase.
Land degradation processes, which imply a reduction of the potential productivity of the land (e.g., soil degradation and accelerated erosion, reduction of the quantity and diversity of natural vegetation), result from a long history of human pressure upon land resources as well as from interactions between varying climatic characteristics and ecologically unbalanced human intervention. The north-west region outside of Beijing, is one of the most important regions where many departments invest most and pay most attention. The land degradation and other environmental problems in this region affect not only Beijing but also the surrounding area. This paper analyzed characteristics of land degradation actuality situation in the NW region of Beijing, based on TM (ETM) in 2002. The wind-eroded land was mainly distributed in north of Yin Shan Mountain. Due to degradation of grassland, the sandy land increased from 1991-2002, mostly distributed in the monitoring zone of Hunshandake sandy land. The water-eroded land was mainly distributed in monitoring zone of the south of Yin Shan Mountain and south of monitoring zone of Horqin sandy land. The salination-land was mainly distributed in lake surrounded area and the drainage basin of Sanggan River. And To better understand the drive forces of land degradation processes in study area, a multivariate spatial model associated with land degradation is found by the explanatory variables of Logistic multivariate regression model(LMR). The explanatory variables include wind speed, soil humidity, soil organic matter, NDVI, average precipitation, soil slope, et al. The value of the parameter estimated by model with their corresponding standard error, chi-square statistics, and significance probability are analyzed to find the driver of land degradation in studied area. And the high or low probability of land degradation is predicted. Finally, suggestions to the eco-environment construction of the studied region have been put forward.
Using remote sensing data of TM and ETM+ in 1992 and 2002, land degradation based on land use changes, especially sand changes were analyzed and land degradation status in 2002 was evaluated in the Huan Beijing Area. The area of sand in 2002 is 6669.6 km<sup>2</sup>, increased 716.2 km<sup>2</sup> compared to that in 1991, and most of the newly-produced sand came from grassland. Land degradation status in 2002 was evaluated by the combination of vegetation, soil and topography information and the region was divided by 1km ×1km cell as the evaluation unit by the application of the GIS. The indicators of land degradation evaluation included soil organic, soil depth, vegetation cover (NDVI) and slope. Land degradation index (DI) was computed, considering the contribution of different indicators to land degradation. The land degradation status was divided into four types according to DI, no-degradation (DI > = 55), slight degradation (50 = < DI < 55), moderate degradation (40 = < DI < 50) and severe degradation (DI < 40). The results showed that the area of degraded land is 132900 km<sup>2</sup>, which occupied the percent 58.2 of the whole Huan Beijing Area and the proportion of slightly-degraded land to degraded land is about 0.47. The political county taken as an evaluation unit, the partition of land degradation in this area was also analyzed based on land degradation area proportion and degree. Six types of land degradation partition were got.
In this article, the information development techniques and methods of characteristic spatial and spectral dimensions applied to distinguish separability of the similar spectrums and the relationship with soil physical and chemical characteristic in the conjoind field of the desert foreland and the Loess Plateau of China. The soillines parameters were primarily proved up the relations with soil orginal matter and soil water content,and had better availability to the markedly different soils; Canonical Discriminant can efficaciously distinguished the soils which were similar each other in the spectral and chemic-physical characteristic.The two principal components were extracted from PCA. Thereinto the soil water and whole Fe<sup>3+</sup>/Fe<sup>2+</sup> contributed to Fac1 and accounted for 72%;Fac2 for 27%. Further,a unitary quadratic equation was modelestablished with the Fac1 as causal variable and soil water content as independent variable based on the above-mentioned results.
A land use/cover classification system is important for land resource management and it is one of the key research issues in Land Use /Cover Change (LUCC) and land change science research. Lots of work has been done in this area, however, a universally-accepted classification system has not been available yet. This paper proposed several guidelines for building a land use/ cover classification system, which encompasses the basic concept of land use and land cover. Then a preliminary framework for Chinese land use/cover classification system at different scale based on remote sensing data was detailed. The framework is made up of four scales, including national scale, regional scale, county scale and country scale. The classes of first level at national scale in the system are agriculture land, woodland, natural grassland, built-up land, water, wetland and barren land. The regional scale includes 27 classes of land use/cover and the county scale includes 43 types. The general diagnostic criterion of the first level of classification system is the situation of existing vegetation, soil and water, artificial and natural surface. Monitoring on land resource in Beijing-around area as an example, this paper introduce the respects need to be paid attention of this classification system. Because of the complexity and difficulty of this question itself, this system was based on synthesis of relevant research achievement; its actual feasibility still remains to be verified.
This paper analyzed major characteristics of land use changes in the Beijing-around region, based on TM(ETM) in 1991 and 2002. On the basis of that, we studied the differences in districts of land use change on county area scale, using intensity, state and trend parameters of land use change. In addition, we investigated the effects of land use change on eco-environment in this region. We found that the area of arable land decreased greatly, with a gradually increasing trend from southeast to northwest from 1991 to 2002. On the other hand, the area of forested land and grassland increased, especially in the northwestern area. The total area of sandy land increased, with a gradually decreasing trend from east to west. Land use change was characterized by low intensity, and the area of net change in each kind type of land use was much more for the most counties in the studied region. From south and north to middle and east to west, the intensity of land use change increased gradually. The degree of single-direction interchange between different land use types decreased gradually from west to east. In recent 10 yr, quality and productivity of land was decreased increasingly in this region. But with the construction of various forest zones by reversing arable land to grassland and forest land, the descent dust amount per yr in Beijing suburban decreased with increase of the area of grassland and forestry land in the Beijing-around region.
The aim of this work is to apply hyperspectral remote sensing technique to land quality monitoring to explore its application potential in this field. According to the characteristics of hyperspectral remote sensing technique combining with spectral features of land quality indicators, we use multivariate statistics methods and approaches based on spectral position variables, explore the spectral indicators sensitive to land quality, set up the retrieval models of land quality indicator, study the potential of applying hyperspectral remote sensing technique in land quality monitoring. Attempt to ameliorate land quality monitoring techniques, expand monitoring extents, decrease the self cost of survey, shorten the survey period, and make the results more scientific, objective and stable by this technique.