Roy Anjan, Inamdar Arun B
Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai-400076, Maharashtra, India.
Heliyon. 2019 Apr 19;5(4):e01478. doi: 10.1016/j.heliyon.2019.e01478. eCollection 2019 Apr.
Multi-temporal and multi-sensor satellite data calibration is an inherent problem in remote sensing-based applications. If multiple satellite scenes cover the study area, it is difficult to compare and process the images for change detection and long-term trend analysis of the same day and/or seasons from different satellites or sensors. Moreover, the validation of all the past images is a challenge due to unavailability of past ground truth datasets. The proposed calibration paradigm in this study is based on radiance normalization in the spatial and spectral domain to ease the alignment of multiple images into an identical radiometric foundation. In this study, an intuitive radiometric correction technique (at a daily, monthly and yearly scale) was proposed, aimed at all Landsat sensors' datasets for long-term Land Use Land Cover (LULC) trend analysis for a dry semi-arid river basin in western India, facing drought conditions. The post-calibration mosaiced images were smooth, and meaningful LULC classification results could be obtained easily for all the years. The LULC change dynamics were analyzed and compared for the years 1972, 1980, 1991, 2001, 2011 and 2016 in Shivna River Basin. During these study periods, wasteland was found to be the most altered class, followed by agricultural land and forest. The spatial extent of agricultural land was found to decrease linearly, while forest cover showed an exponential decrease; a linear increase was observed in wasteland. Though during the 44 years study period (1972-2016), 241.48 km area was converted to agricultural land from wasteland, but more than double that land was converted to wasteland from agricultural land; alarmingly, 5.18% (8.12% in 1972 and 2.94% in 2016) forest cover decreased. The existing forest cover in 2016 is approximately one-third compared to 1972. The present work provides a generic framework for the calibration of multi-temporal and multi-sensor satellite images for long-term LULC trend analysis, which can be adopted for other satellite datasets.
多时间和多传感器卫星数据校准是基于遥感的应用中的一个固有问题。如果多个卫星场景覆盖研究区域，那么就难以对来自不同卫星或传感器的同一天和/或季节的图像进行比较和处理，以进行变化检测和长期趋势分析。此外，由于过去的地面真值数据集不可用，对所有过去的图像进行验证是一项挑战。本研究中提出的校准范式基于空间和光谱域中的辐射归一化，以便于将多幅图像对齐到相同的辐射测量基础上。在本研究中，提出了一种直观的辐射校正技术（在日、月和年尺度上），目标是针对印度西部面临干旱条件的干旱半干旱流域的所有陆地卫星传感器数据集进行长期土地利用土地覆盖（LULC）趋势分析。校准后的镶嵌图像很平滑，并且可以轻松获得所有年份有意义的LULC分类结果。对希夫纳河流域1972年、1980年、1991年、2001年、2011年和2016年的LULC变化动态进行了分析和比较。在这些研究期间，发现荒地是变化最大的类别，其次是农业用地和森林。发现农业用地的空间范围呈线性下降，而森林覆盖呈指数下降；荒地则呈线性增加。尽管在44年的研究期（1972 - 2016年）内，有面积241.48平方公里的荒地转变为农业用地，但从农业用地转变为荒地的面积是前者的两倍多；令人担忧的是，森林覆盖减少了5.18%（1972年为8.12%，2016年为2.94%）。2016年的现有森林覆盖面积约为1972年的三分之一。本研究为多时间和多传感器卫星图像校准以进行长期LULC趋势分析提供了一个通用框架，该框架可应用于其他卫星数据集。
