ABSTRACT

We present systematic analyses of the temporal dynamics of the growth of Kumasi, the fastest growing city in Ghana using 20-year Landsat time-series data from 2000 to 2020 (with 1986 Landsat image as a baseline). Two classification algorithms – random forest (RF) and support vector machines (SVM) – were used to produce binary (built-up / non-built up) maps for all years within the temporal span. We further implemented an anomaly detection and temporal consistency algorithm followed by a changing logic to correct the classification anomalies due to image contamination from the cloud and other sources. The mean overall accuracies obtained for RF and SVM were 94.9% (kappa = 0.90) and 95.5% (kappa = 0.91), respectively. Our results reveal that the mean built-up area percentages of the metropolis are approximately 74, 65, 47, and 23 for the years 2020, 2010, 2000, and 1986, respectively, representing a mean annual change of 3.5% over the 34 years. With the present lack of labeled data in Ghana for in-depth analyses of the evolution of land use, we believe that this study serves as an initial attempt to a better understanding of the effects of increasing anthropogenic activities due to urbanization, on human and environment health.

我们使用从2000年到2020年的20年Landsat时间序列数据（以1986年Landsat图像为基准），对加纳增长最快的城市库马西的增长的时间动态进行了系统分析。使用两种分类算法-随机森林（RF）和支持向量机（SVM）-来生成时间跨度内所有年份的二进制（组合/非组合）地图。我们进一步实现了异常检测和时间一致性算法，然后执行更改逻辑以纠正由于云和其他来源的图像污染而导致的分类异常。RF和SVM的平均总体准确度分别为94.9％（kappa = 0.90）和95.5％（kappa = 0.91）。我们的结果表明，到2020年，大都市的平均建筑面积百分比约为74、65、47和23，2010年，2000年和1986年分别代表这34年的年均变化3.5％。由于加纳目前缺乏用于深入分析土地利用演变的标签数据，因此我们认为，这项研究是更好地了解由于城市化带来的人类活动增加对人类和环境的影响的初步尝试。健康。