Object oriented image classification (OOIC) and neural network aided Markov Chain (MC) modeling tools were used to map and predict land use and land cover (LULC) changes. A case study in the Kiskatinaw River Watershed (KRW) of Canada was presented. With an overall classification accuracy of 90.45%, the multi-temporal Landsat satellite images of KRW were analyzed for 11 selected LULC types. It was found that KRW experienced a significant wetland depletion along with a change in forest cover types from 1984 to 2010. The vulnerability of LULC change in different parts of KRW was predicted through MC modeling based on the obtained transition probability, and the results indicated slight LULC changes from 2010 with a wetland depletion of 67.89 km2. In summary, the proposed methods generated valuable results for informed LULC management and hold the potential to be applied to other watersheds.

中文翻译：

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面向对象图像分类和马尔可夫链建模在土地利用和土地覆盖变化分析中的应用

面向对象的图像分类 (OOIC) 和神经网络辅助马尔可夫链 (MC) 建模工具用于绘制和预测土地利用和土地覆盖 (LULC) 变化。介绍了加拿大基斯卡蒂诺河流域 (KRW) 的案例研究。以 90.45% 的总体分类精度，对 KRW 的多时相 Landsat 卫星图像分析了 11 种选定的 LULC 类型。研究发现，1984-2010 年，随着森林覆盖类型的变化，KRW 经历了显着的湿地枯竭。基于获得的转换概率，通过 MC 模型预测了 KRW 不同地区 LULC 变化的脆弱性，结果表明LULC 与 2010 年相比发生了变化，湿地枯竭面积为 67.89 平方公里。总之，