Study on spatial pattern of land-use change in China during 1995–2000

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It is more and more acknowledged that land-use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. Supported by the Landsat TM digital images, spatial patterns and temporal variation of

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   Vol. 46 No. 4   SCIENCE IN CHINA  (Series D )   April 2003   Study on spatial pattern of land-use change in China during 1995 ¡ ª  2000 LIU Jiyuan (Áõ¼ÍÔ¶) 1 , LIU Mingliang (ÁõÃ÷ÁÁ) 1 , ZHUANG Dafang (ׯ´ó·½) 1 , ZHANG Zengxiang (ÕÅÔöÏé) 2  & DENG Xiangzheng (µËÏéÕ÷) 1   1. Institute of Geographic Sciences of Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 2. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China Correspondence should be addressed to Liu Jiyuan(email: liujy@igsnrr.ac.cn) Received November 27, 2002 Abstract  It is more and more acknowledged that land-use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. Supported by the Landsat TM digital images, spatial patterns and temporal variation of land-use change during 1995¡ª2000 are studied in the paper. According to the land-use dynamic degree model, supported by the 1km GRID data of land-use change and the comprehensive characters of physical, economic and social features, a dynamic regionalization of land-use change is designed to disclose the spa-tial pattern of land-use change processes. Generally speaking, in the traditional agricultural zones, e.g., Huang-Huai-Hai Plains, Yangtze River Delta and Sichuan Basin, the built-up and residential areas occupy a great proportion of arable land, and in the interlock area of farming and pasturing of northern China and the oases agricultural zones, the reclamation of arable land is conspicuously driven by changes of production conditions, economic benefits and climatic conditions. The im-plementation of “returning arable land into woodland or grassland” policies has won initial success in some areas, but it is too early to say that the trend of deforestation has been effectively reversed across China. In this paper, the division of dynamic regionalization of land-use change is designed, for the sake of revealing the temporal and spatial features of land-use change and laying the foundation for the study of regional scale land-use changes. Moreover, an integrated study, in-cluding studies of spatial pattern and temporal process of land-use change, is carried out in this paper, which is an interesting try on the comparative studies of spatial pattern on change process and the change process of spatial pattern of land-use change. Keywords: land-use change, China, spatial pattern, regionalization. Land-use/cover change has become an event being of paramount importance to the study of global environmental change [1,2] . Land-cover change is closely related to the terrestrial surface material cycles and life-support processes [3] , i.e., the interaction between biosphere and atmos- phere, biodiversity, biogeochemical cycle and sustainable exploitation of resources [4,5] . The series of scientific study programs, issued and promoted by IGBP and IHDP in 1995, make the study of land-use and land-cover change (LUCC) [6]  become one of the hot topics in the global environ-mental change study. Case study at regional scale, especially the comparison study on the process of land-use change, the pattern of land-use change and land-use dynamics, became the key components in   374 SCIENCE IN CHINA (Series D) Vol. 46   LUCC study [7]  for the first time, and a series of regional studies on LUCC have been conducted in China [8] . As a developing but booming country, China has formulated a series of policies exerting great influence on the land-use change. In addition, due to varied physical environments and vast land areas, its land-use changes not only influence the social and economic development but also impact the global change accordingly. In order to simulate the modern process of land-use change, and more accurately predict its trend, the Chinese Academy of Sciences (CAS) has planned to  build a temporal and spatial data platform supported by the National Resources and Environment Database (NRED), with remote sensing data as its main data sources [9–11]  while laying the empha-ses on the study of the land-use change in the 1990s. It is essentially fundamental to study the regional differentiation of land-use dynamics for the regional land-use monitoring, driving forces analyses and the prediction of land-use change. In the study, the 1km GRID data of land-use change, reflecting area proportion for each kind of land-use category and its net change were generated to eliminate the scale effect of different data sources during data fusion and guarantee the data accuracy [12] . 1 Data sources and handling There are two kinds of methods to extract land-use change information from remote sensing data [13] , (i) by comparison of classified data at two periods, i.e. classifying based on the radiation data at first and then comparing the classification maps to get the dynamic information, and (ii) information extracted directly by temporal variation characteristics of surface radiation. The for-mer needs much more accurate classification standards, higher data accuracy and more labour ef-forts, whereas the latter has the rigorous requirement for the selections of remote sensing data sources and accurate data handling. In the process of building the National Resources and Environments Database, an efficient classification system is drafted and an effective research team is organized to work on remote sensed data through human-machine interactive interpretation to guarantee classification consis-tency and accuracy. Land-use maps at scale of 1©U100000, classified into 6 first levels and 25 second levels of land-use categories in total, are drawn based on the Landsat TM (Thematic Map- per) data. The outline of land-use change is delimited by comparison of TM data in 1995 and 2000, with the references from land-use background in 1995. The work flow of this integration is dis- played in fig. 1. The main data sources are Landsat TM digital images(520 scenes in 1995/1996 and 508 scenes in 1999/2000).Apart from that,the CBERS-1( China-Brazil Earth Resources Satellite 1 )data were also used to acquire land-use information for 1999/2000.After image being geometrically corrected and geo-referenced, the average location errors are less than 50 m (about 2 pixels). The out-door survey and random sample check (covering line survey of 70000 km and 13300 patches) testified that the average interpretation accuracy for land-use/land-cover is 92.9% and 97.6%for land-use change interpretation.The maps for linear features and ecological and environmental   No. 4 SPATIAL PATTERN OF LAND-USE CHANGE IN CHINA 375 Fig. 1. Work flow of land-use change monitoring work. factors in our NRED are used during the driving forces analyses. As we all know, supported by the 1km GRID global database, IGBP, IHDP and other inter-national research organizations have done a series of researches including land-cover dynamics, mechanism and global and regional models [6] . We also think that the 1km GRID data are an effi-cient kind of data fusion methods, which can promote the regional land-use change monitoring,  prediction and driving forces studies 1) . On one hand, it is more effective and efficient to handle GRID data than vector data, which facilitates the data integration and fusion for multi-source data, on the other hand, its data accuracy partly determined by its data structure meets the need for studies on LUCC at regional or national scales while the well-known application of 0.5° of lati-tude ¡Á0.5° of longitude cells to global research is too coarse to extract the phenomena for na-tional and local domain. Generation of the 1 km GRID percentage data is processed in ESRI Arc/Info 8.02 software environments and could be described as follows: firstly, combining the land-use change patches (178 173 in total) with one coverage; secondly, intersecting the changing  patches map with 1km vector data; thirdly, under TABLE model, taking statistical process on each kind of land-use change area grouped by 1km vector cell ID (fig. 2); lastly, changing the vector data into grid format data with area percentage information of all land-use change types in which 1) Tang Xianming, Studies on geo-spatial data fusion and its applications, Ph. D Thesis, Institute of Remote Sensing Applications, CAS, Beijing, 2000.   376 SCIENCE IN CHINA (Series D) Vol. 46   we are interested. The 1km GRID data have been further aggregated to 10 km GRID data frame to realize the dynamic regionalization of land-use change. The design of working flow insists on “zero-loss” of area information. Without special notification, the statistical farm-land area according to the GRID data is survey area by satellite remote sensed data, which can be called “gross area”. Census data (1995, at county level) for dynamic regionalization of land-use change have been spatialized based on the distribution features of built-up areas 1) , while the data of railway and road (length) are derived from the line feature map at scale of 100000. 2 Characteristics and measures on land-use change The regional differentiation of land-use change rate can be represented by the dynamic de-gree model of land use [9] , i.e. ()(1)100%, nijiiij SSStW  −   = ∆ × × ×    ∑   n =1, 2, 3, ¡, (1) where S is the land-use change rate, S  i  represents the total areas of i (land-use category) at the former stage while W  i  is the weight of areas proportion of i , and ∆ S  i −  j  represents the net change of area from i  to  j (land-use category) at the time scale of t  . This model can also be used to measure the dynamic degree for a single land-usce category. The basic unit to employ the dynamic degree model is 1km GRID, and the statistical result serves as basis to draw the land-use change and land-use conversion maps classified by land-use categories. The code system of land-use change patches, theoretically, includes 600 kinds determined by land-use change types at the second level (25•~24). On the basis of the land-use change patches of 1995/1996 and 1999/2000 at the GRID scale of 10 km, together with the working flow of fig. 2, the distribution maps for expanding and shrinking types of land-use change are generated based on maximal change type in each cell according to area summary (Plate I). In order to lay the em- phases on the analyses of the main conversion direction, a main transform matrix for land use is 1) Zhuang Dafang, Research on spatial information of remote sensing and GIS on land-use/land-cover change, Ph. D The-sis, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, 2001 Fig. 2. Workflow of constructing 1km GRID land-use change map.   No. 4 SPATIAL PATTERN OF LAND-USE CHANGE IN CHINA 377 generalized, which includes 9 kinds (C1 to C9) (table 1). C1 (cropland-cropland) refers to the conversion between paddy field and dry land; C2 is the result from returning arable land into woodland or grassland; C3 represents water body expansion (including river, lake, reservoir, gla-cier, seabeach and bottomland.); C4 refers to the other land-use categories being converted into  built-up areas; C5 is the result from deforestation and reclamation; C6 discloses forests being de-stroyed into grassland; C7 is grassland or unused land being reclaimed; C8 mainly refers to the afforestation in grassland or unused land; and C9 represents the conversion from water body to other kinds of land use (except built-up area). The transition type is determined by the conversion direction with the maximal conversion rate for each GRID (at the GRID scale of 10 km), but to evaluate certain land-use conversion area for those GRIDS with “no change” under the conditions, their maximal conversion rate has to be less than 0.05%, then the land-use dynamic map was gen-erated (plate II). Table 1 Classification and coding system for land-use conversion a)  Backward Forward AL WL GL and UL WB BuA AL C1 C2 WL C5 − C6 GL and UL C7 C8 − C3 WB C9 − C4 a) AL, WL, GL, UL, WB and BuA represent arable land, woodland, grassland, unused land, water body and built-up areas respectively. According to the spatial pattern of land-use change, a dynamic regionalization of land-use change was designed, supported by the 10 km GRID data, so as to disclose the regional differen-tiation during 1995¡ª2000. Referencing to  physical regionalization [14]  and agricultural division [15]  and comprehensively considering the man-environments related factors, the main principle for dynamic regionalization of land-use change is designed: (i) taking the land-use change type within each cell as primary considered factor to determine the zone and guarantee the consistency of main land-use change type in each zone as a whole; (ii) differentiating the land-use change zones with the same change type at different regions by the name of geographical units when consider-ing the principle of zonation continuity in geographical location. This principle is to ensure the consistency of regional geomorphology and macro economic environments; and (iii) comprehen-sively considering the physical environments and land-use characteristics to try to make them consistent in each zone. The spatial generalization and experience always lay the foundation for carrying out the dynamic regionalization of land-use change. Using the land-use change map at scale of 10 km (Plates I and II), the geomorphological map, land-use background map for 1995/1996 and climatic resources map as references, the outline of land-use dynamic regionaliza-tion, free from the limitation of administrative boundaries, is drafted under the overall digital en-vironments. Then a land-use dynamic regionalization of land-use change, including 12 zones, is completed and their names, titles, main land-use features, land-use change characteristics and  population distribution are shown in table 2.
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