Mapping and monitoring of the complex process of desertification based on ground data in broad arid and semiarid areas faces basic limitations. Therefore, the purpose of the present study was to propose a new method for mapping this phenomenon in central Iran using biological and nonbiological (BNB) indices of remote sensing products from 2003 to 2016. For this purpose, BNB indices including normalized difference vegetation index, land surface temperature, temperature vegetation dryness index, precipitation, evapotranspiration, net primary production, rain use efficiency (RUE), aridity index, and slope were extracted using MOD13A2, MOD11A2, PERSIANN-CDR, and SRTM products. After calibration and normalization of indices, they were combined using fuzzy logic and gamma operator and the combined 2003 map was validated by MEDALUS model map prepared based on ground data in 2003 using Pearson correlation and error matrix. Results showed more than 70% correlation (p < .001) as well as overall accuracy and kappa coefficient of more than 70% and 0.5 between remote sensing-based and MEDALUS-based desertification maps. According to the 2003 and 2016 maps, desertification classes including low, moderate, severe, and very severe changed from 11.9, 49.8, 34, and 4.1% to 11.11, 43.21, 40.43, and 5.24%, respectively, which indicate increasing trend of desertification in the region. The findings demonstrate the high capability of proposed method to map and monitor desertification classes. Therefore, it can be used to update existing desertification models and to report desertification condition and its positive and negative trends at local, national, and international levels.

中文翻译：

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基于遥感的生物和非生物指标，用于评估伊朗的荒漠化：图像与现场指数

根据广阔的干旱和半干旱地区的地面数据对荒漠化的复杂过程进行制图和监测面临着基本的局限性。因此，本研究的目的是提出一种使用2003年至2016年遥感产品的生物和非生物（BNB）指数绘制伊朗中部这一现象的新方法。为此，BNB指数包括归一化差异植被指数，使用MOD13A2，MOD11A2，PERSIANN-CDR和SRTM产品提取了地表温度，温度植被干燥指数，降水，蒸散量，净初级生产力，雨水利用效率（RUE），干旱指数和坡度。经过指标的校准和归一化后，利用模糊逻辑和伽玛算子对它们进行了组合，并通过MEDALUS模型图对2003年的组合图进行了验证，该模型图是基于2003年基于皮尔森相关性和误差矩阵的地面数据而准备的。结果显示超过70％的相关性（p  <.001），以及基于遥感和基于MEDALUS的荒漠化地图之间的整体准确性和卡伯系数均超过70％和0.5。根据2003年和2016年的地图，荒漠化等级包括低度，中度，严重度和非常严重度，分别从11.9％，49.8％，34％和4.1％变为11.11％，43.21％，40.43％和5.24％，这表明荒漠化趋势呈上升趋势在该区域。研究结果表明，该方法具有较高的测绘和监测荒漠化等级的能力。因此，它可用于更新现有的荒漠化模型并报告荒漠化状况及其在地方，国家和国际各级的积极和消极趋势。