CAJ | 학술논문

为掌握大理河流域植被格局时间和空间分布特征，构建了基于分形布朗运动理论的流域植被格局量化模型。该文利用自1990年至2006年共计5期TM/ETM影像为基础信息源，在ARCGIS平台下计算得到大理河流域各时相的像元归一化植被指数（normalized difference vegetation index，NDVI），建立了流域像元尺度的栅格结构数字植被模型（digital vegetation model，DVM），并逐个计算得到各子流域植被格局分形维数。结果显示分形维数都大于2.5，介于2.7311～2.8499之间，各下一级子流域植被覆盖分形维数的大小都没有超过其所在的更大流域的分形维数。植被格局分形维数（fractional brownian motion，FBM）从下游到上游随着离出口点距离的增大而逐渐变小，植被格局自下游到上游逐渐趋于破碎复杂。各子流域从1990年至2006年植被格局分形维数基本随着时间的变化呈现了先减小后增大的趋势。该文建立的流域植被格局分形量化模型在综合量化流域植被格局破碎程度等方面具有一定的优越性。
위장악대리하류역식피격국시간화공간분포특정，구건료기우분형포랑운동이론적류역식피격국양화모형。해문이용자1990년지2006년공계5기TM/ETM영상위기출신식원，재ARCGIS평태하계산득도대리하류역각시상적상원귀일화식피지수（normalized difference vegetation index，NDVI），건립료류역상원척도적책격결구수자식피모형（digital vegetation model，DVM），병축개계산득도각자류역식피격국분형유수。결과현시분형유수도대우2.5，개우2.7311～2.8499지간，각하일급자류역식피복개분형유수적대소도몰유초과기소재적경대류역적분형유수。식피격국분형유수（fractional brownian motion，FBM）종하유도상유수착리출구점거리적증대이축점변소，식피격국자하유도상유축점추우파쇄복잡。각자류역종1990년지2006년식피격국분형유수기본수착시간적변화정현료선감소후증대적추세。해문건립적류역식피격국분형양화모형재종합양화류역식피격국파쇄정도등방면구유일정적우월성。
Based on the spatial distribution model of NDVI (normalized difference vegetation index) value which is extracted from remote sensing image, spatial distribution features of the watershed surface NDVI value can be understood to be composed of a large number of equal units, and the side length of the units is equal to the size of the remote sensing image pixels. NDVI value is stored in each unit of attribute sheet "VALUE". In this paper, we realize the measurement of NDVI increment at every point on the watershed by the development of GIS algorithm and the establishment of the moving window in the research process. The moving window statistical method divides the whole watershed into a number of equal DVM (digital vegetation model) cells (r×r, r is an odd multiple of the pixel size, r>1). Each cell is called a "window" and the cells don't have overlap for each other. We computerize the difference of NDVI values between each pixel point and the center pixel point in each cell. We calculate NDVI increment value of the watershed points at a certain spatial scale by the moving window statistical method, then computerize mathematics expectations of measure collection, which is composed of all NDVI incremental values. To study the characteristics of fractal dimension of the vegetation cover and its variation at different scales, it is divided into 4 scales according to the area of the basin, with 5 phases per level. The first level is the entire Dali River basin with an area of 3 906 km2, approximately 5 million times of pixel size (30 m × 30 m). At the second stage, Dali River basin will be divided into 3 parts i.e. upstream, midstream and downstream, with an average area of approximately 1 000 km2, approximately 1 million times of pixel size (30 m × 30 m). At the third stage, it will be divided into 14 small basins of Dali River, with an area of 179.4-392.5 km2, about 200 thousand times of pixel size (30 m × 30 m). At the fourth stage, it will be divided into 53 small basins of Dali River (No.1 to 53), with an area of 21.9-108.9 km2, about 40 thousand times of pixel size (30 m × 30 m). Firstly, on the basis of information source from 5 issues of TM/ETM images from 1990 to 2006, the image data are processed, the vegetation information at basin stage is extracted by the platform of geographic information system (GIS), and then watershed DVM is established. FBM (fractional brownian motion) fractal dimension for watershed vegetation cover is between 2.7311 and 2.8499. By calculation and analysis, vegetation cover FBM fractal dimension is increasing from upstream to downstream, so vegetation distribution is more uniform from upstream to downstream. Vegetation cover fractal dimension is increasing with the watershed area. Through analyzing spatial and temporal variation of each sub-watershed at all levels, it presents the curvilinear trend of decreasing firstly and then increasing as the change of time from 1990 to 2006; at the same time, the vegetation coverage fractal dimension of each sub-watershed at all levels is no more than its bigger sub-watershed.