Land Cover Mapping in Cloud-Prone Tropical Areas Using Sentinel-2 Data: Integrating Spectral Features with Ndvi Temporal Dynamics

Chong Huang,He Li,Fenzhen Su,Qingsheng Liu,Arika Bridhikitti,Gaohuan Liu,Chenchen Zhang,Yun He

doi:10.3390/rs12071163

Abstract

Accurate remote sensing and mapping of land cover in the tropics remain difficult tasks since data gaps and a heterogenic landscape make it challenging to perform land cover classification. In this paper, we proposed a multi-feature classification method to integrate temporal statistical features with spectral and textural features. This method is designed to improve the accuracy of land cover classification in cloud-prone tropical regions. Sentinel-2 images were used to construct an NDVI stack for a time-series statistical analysis to characterize the temporal variance of land cover. Two statistical indices were calculated and used to represent the variation in annual vegetation. These indices included the mean (NDVI_mean) and coefficient of variation (NDVI_cv) for the NDVI time series. The temporal statistical features were then integrated with spectral and textural features extracted from high-quality Sentinel-2 imagery for Random Forest classification. The performance and contribution of different combinations were assessed based on their classification accuracies. Our results show that the time-series statistical analysis is an effective way to represent land cover category information contained in annual NDVI variance. The method uses clear pixels from dense low-quality images to obtain the NDVI statistical characteristics, thus, to reduce the influence of random factors such as weather conditions on single-date image. The addition of NDVI_mean and NDVI_cv can improve the separability among most types of land cover. The overall accuracy and the kappa coefficient reached values of 0.8913 and 0.8514 when NDVI_mean and NDVI_cv were integrated. Furthermore, the time-series statistical analysis has less stringent requirements regarding image quality and features a high computational efficiency, which shows its great potential to improve the overall accuracy of land cover classification at regional scales in cloud-prone tropical regions.

Highlights

Land cover patterns denote fundamental land surface characteristics and underlying biophysical processes
Land cover has been regarded as an important climatic variable and environmental factor for modelling Earth surface processes related to anthropogenic activities and the natural environment [1,2,3]
Many studies have shown that high cloud cover in the tropics is the biggest obstacle to time-series analysis [8,28] In cloud-prone regions, few high-quality images are available throughout the year (Figure 5) even for satellite platforms with a high revisit period

Summary

Introduction

Land cover patterns denote fundamental land surface characteristics and underlying biophysical processes. Land cover has been regarded as an important climatic variable and environmental factor for modelling Earth surface processes related to anthropogenic activities and the natural environment [1,2,3]. Land cover maps are traditionally produced using remote sensing image classification. There are various studies that have used supervised or unsupervised algorithms to perform land cover mapping based on the spectral discrimination of different bands from a single date image [5,6]. Single-date images only represent instantaneous spectral characteristics of the land surface at a single period in time. Land cover categories may show a similar spectral reflectance due to the limitations imposed by broad spectral bands, which can produce inadequate classification results [4]

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