Determining the Type and Starting Time of Land Cover and Land Use Change in Southern Ghana Based on Discrete Analysis of Dense Landsat Image Time Series


Rural to urban migration and relatively high fertility rates have influenced speedy land cover and land use change (LCLUC) in southern Ghana, which warrants additional frequent monitoring. We tend to develop and test approaches for semiautomatically and a lot of frequently identifying the kind and date of LCLUC from time series of Landsat ETM+ imagery from 2000 to 2014. Clouds, cloud shadows, and scan line corrector-off produce missing data in $textETM+$ images. Forty-one dates of $textETM+$ images that partially contain missing information were utilised. The final approach is to conduct a per-pixel supervised classification on each image of a Landsat time series after masking missing data. Spatial, temporal, and logical filters are applied to correct for misclassification and missing information. Every image is classified into 3 general classes: one) Designed; two) Natural Vegetation; 3) and Agriculture, with growth of Designed being our main focus. Reference knowledge for Modification-to-Built were independently selected from all obtainable high-spatial resolution satellite pictures (e.g., Quickbird, GeoEye, Worldview, and Google Earth imagery), and the kind and beginning time of LCLUC was recorded. Results show that the temporal-filtered product identified each the placement and the beginning of Change-to-Engineered a lot of exactly and accurately than the nonfiltered and other filtered products. Based mostly on reference knowledge, fortyp.c of the Change-to-Designed samples were properly identified without filtering; whereas, when a temporal filter was applied, eightyp.c were properly identified with low amounts of false positive Amendment-to-Designed pixels. The temporal-filtered product has the highest temporal precision and accuracy ($textmean time difference = a pair textyears$) in identifying the start of Ch- nge-to-Designed.

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