利用元胞自动机模型与卫星影像识别用于土地退化定性评估的植被格局
Identifying vegetation patterns for a qualitative assessment of land degradation using a cellular automata model and satellite imagery
AI总结：
本研究通过对比随机元胞自动机模型与卫星影像的NDVI、LAI数据，识别植被空间格局与生长动态，发现接近渗流转变时的周期破碎恢复现象，可用于定性评估土地退化与环境压力。
摘要：
我们旨在识别植被的空间分布及其生长动态，以对与植被状态、生产力、健康状况及土地退化相关的植被特征进行定性评估。为此，我们将植被生长统计模型与从地表影像提取的植被指数进行对比。具体而言，我们分析了一个随机元胞自动机模型，以及从卫星图像获取的数据，即使用归一化差异植被指数（NDVI）和叶面积指数（LAI）。在实验数据中，我们寻找植被破碎为小斑块的区域，并将其与元胞自动机模型中出现的渗流态、破碎态和退化态进行定性比较。我们对季节的周期性影响进行建模，发现当模型参数足够接近模型的渗流转变点时，存在周期性破碎与恢复现象的数值证据。我们在真实世界的植被影像中定性识别出这些效应，并将其视为环境压力和脆弱性升高的信号。最后，我们通过同时考虑植被密度及其聚集性，给出了陆地影像中环境压力的一种估算方法。
英文摘要：
We aim to identify the spatial distribution of vegetation and its growth dynamics with the purpose of obtaining a qualitative assessment of vegetation characteristics tied to its condition, productivity and health, and to land degradation. To do so, we compare a statistical model of vegetation growth and land surface imagery derived vegetation indices. Specifically, we analyze a stochastic cellular automata model and data obtained from satellite images, namely using the Normalized Difference Vegetation Index (NDVI) and the Leaf Area Index (LAI). In the experimental data, we look for areas where vegetation is broken into small patches and qualitatively compare it to the percolating, fragmented, and degraded states that appear in the cellular automata model. We model the periodic effect of seasons, finding numerical evidence of a periodic fragmentation and recovery phenomenology if the model parameters are sufficiently close to the model's percolation transition. We qualitatively recognize these effects in real-world vegetation images and consider them a signal of increased environmental stress and vulnerability. Finally, we show an estimation of the environmental stress in land images by considering both the vegetation density and its clusterization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
