南方丘陵山区典型地物景观特征尺度研究

景观的特征尺度反映了人与自然交互作用的空间过程,合理识别景观空间结构及其特征尺度有助于遥感影像景观空间异质性分析。论文以地处南方丘陵山区的福建省福州市为研究区,针对城市、农田、森林与水域4种地物景观,基于SPOT 10 m影像,分别利用半方差分析、小波分析与平均局部方差方法,开展景观特征尺度研究。结果表明:①不同景观类型的空间异质性差异较大,其中森林景观空间异质性最大,其次为城市、农田景观,水域的空间异质性最小;②小波方差分析和半方差分析分别检测到两个不同的特征尺度,而局部方差仅仅检测到较小的空间结构;③森林景观特征尺度比通常偏小,与南方丘陵山区破碎地形有关,城市景观更多体现为人类活动的影响,南方丘陵山区城市景观至少具有两种不同的空间结构,其特征尺度均较小,农田景观特征尺度最大。基于小波分析与半方差各自的特点,总结提炼出综合两种方法合理识别景观特征尺度的基本流程,即:首先开展小波分析,然后在此基础上利用半方差分析多种理论模型组合从而获得更详细的特征尺度信息,模型组合个数与参数初始值依据小波分析的结果而定。

Identifying the Characteristic Scale of Typical Landscapes in Mountainous Area of South China

The characteristic scales in real landscapes reflect the spatial patterns and scales of human interactions with the environment. Identifying spatial structure and its characteristic scale is very important as well as necessary for exploring the spatial variability of different landscapes within remotes sensing images. Landscapes in mountainous area of South China is characterized as with strong variability and controlled by topographic conditions to a certain degree. Further researches are needed to quantitatively identify the characteristic scale of typical landscapes in those areas and its relationship with natural and anthropogenic processes. The prime objective of this study was to explore the characteristic scale of main landscapes at mountainous area in South China with semivariogram, wavelet transform and local variance using SPOT 10 m image. The first principal component of SPOT image is used for analysis. Results of variograms, wavelet variance, local variance for forest, city, agricultural and water landscapes all demonstrate that forest landscape possesses great spatial variability as quantified by the variogram or wavelet, local variance, and it partially originated from topographic complexity. City landscape also exhibits strong spatial variability and it's principally influenced by anthropogenic processes. Agricultural landscape is more heterogeneous than forest and city landscapes, but water landscape is most heterogeneous and no further variogram modeling conducted. Two different spatial structures were detected from wavelet analysis and semivariogram modeling in forest, city and agricultural landscapes individually. Results from variogram modeling are more precise and show that the range of the first spatial structure of city, agricultural and forest landscape is 16 m, 79 m and 95 m, and the second one is 133 m, 1031 m and 483 m respectively. Only one smaller characteristic scale is spotted from local variance. The mean characteristic scale quantified by mean length scale varies from 111 m to 569 m over city, forest and agricultural landscapes. The characteristic scale of forest in mountainous areas is relatively smaller than usual with the introduction of complicated landform. The largest spatial structure is detected in agricultural landscape which is the mosaic of agricultural crop fields surrounded by rivers and roads. In conclusion, a combined process for identifying characteristic scale with wavelet and semivariogram analysis is proposed. Firstly, wavelet method could be applied to distinguish the different spatial structures and the rough value of characteristic scale; then semivariogram modeling might be utilized to gain the exact value, with the number of combined models and parameters derived from results of wavelet analysis.