Mapping plastic-covered greenhouse farming areas using high-resolution PlanetScope and RapidEye imagery: studies from Loukkos perimeter (Morocco) and Dalat City (Vietnam).

Abstract

The proliferation of plastic-covered greenhouse (PCG) farming has resulted in high horticulture crop yields worldwide during the last few decades. A proper and cost-effective PCG monitoring method is necessary for maintaining sustainable horticulture and high-quality agricultural production with less plastic pollution. Remote sensing applications for mapping PCG have received great attention from the scientific community in recent years. In this paper, a comparative study was carried out in two plastic-covered greenhouse areas in Loukkos perimeter in Morocco and Dalat City in Vietnam to test PCG mapping accuracy of high spatial resolution RapidEye and PlanetScope satellite data and to understand the differences in PCG mapping quality due to topographic effects. Medium-resolution Landsat-8 OLI and Sentinel-2 MSI imagery were also applied. Moreover, two classification algorithms-retrogressive plastic greenhouse index (RPGI) and a supervised classification algorithm using random forest (RF)-were used for mapping PCG. The findings reveal that RF outperforms RPGI. Overall, the mapping accuracy achieved exceeded 90% in both study areas, except for the RPGI method using Landsat-8 data (PCG mapping accuracy using Landsat data varied between 87.4 and 89%). Furthermore, PCGs were better detected by PlanetScope data than by RapidEye imagery due to the differences in the spectral range. Better performance in Loukkos perimeter can be explained by the study area's topography; Dalat City and surrounding areas are situated in mountainous terrain. The results obtained from this study indicate that spectral indices can be used as a cost-effective tool for mapping PCG under cloud-free conditions. PCG mapping using RF classifiers resulted in accurate PCG mapping without topographic factors' influence.