Evaluation of Object-Based Greenhouse Mapping Using WorldView-3 VNIR and SWIR Data: A Case Study from Almería (Spain)

Manuel A Aguilar,Rafael Jiménez-Lao,Fernando J Aguilar

doi:10.3390/rs13112133

Manuel A Aguilar, Rafael Jiménez-Lao + Show 1 more

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https://doi.org/10.3390/rs13112133

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Journal: Remote Sensing	Publication Date: May 28, 2021
Citations: 13	License type: CC BY 4.0

Affiliation: University of Almería

Abstract

Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and eight additional bands in the short-wave infrared (SWIR) region. A few studies have already established the importance of indices based on some of these SWIR bands to detect urban plastic materials and hydrocarbons which are also related to plastics. This paper aims to investigate the capability of WV3 (VNIR and SWIR) for direct PCG detection following an object-based image analysis (OBIA) approach. Three strategies were carried out: (i) using object features only derived from VNIR bands (VNIR); (ii) object features only derived from SWIR bands (SWIR), and (iii) object features derived from both VNIR and SWIR bands (All Features). The results showed that the majority of predictive power was attributed to SWIR indices, especially to the Normalized Difference Plastic Index (NDPI). Overall, accuracy values of 90.85%, 96.79% and 97.38% were attained for VNIR, SWIR and All Features strategies, respectively. The main PCG misclassification problem was related to the agricultural practice of greenhouse whitewash (greenhouse shading) that temporally masked the spectral signature of the plastic film.

Highlights

Accepted: 26 May 2021The extensive and steadily expanding use of plastic films in agriculture, and in protected horticulture, is reported worldwide since the middle of the twentieth century [1]
In previous works dealing with mapping Plastic covered greenhouse (PCG), this segmentation was performed on atmospherically corrected visible and near-infrared (VNIR) very high resolution (VHR) satellite imagery [21,22] using the widely known multi-resolution (MRS) image segmentation algorithm implemented in Trimble eCognition Developer v
This study aimed to test the capability of the eight short-wave infrared (SWIR) bands included in WV3 imagery (3.2 m ground sample distance (GSD)) to improve classification accuracy for mapping PCG attained from the VNIR eight bands (1.2 m GSD)

Summary

Introduction

The extensive and steadily expanding use of plastic films in agriculture, and in protected horticulture, is reported worldwide since the middle of the twentieth century [1]. Due to their synoptic acquisitions and high revisit frequency, the data obtained by remote sensing can offer a significant contribution to provide periodic and accurate pictures of the agricultural sector [2]. Several research lines have emerged throughout the 21st century They are based on (i) input satellite data sources, including optical (e.g., Landsat or Sentinel-2) and SAR (Radarsat-2, Sentinel-1)imagery; (ii) image processing approaches (i.e., pixel-based or object-based image analysis (OBIA)); (iii) input vector features for image classification (multispectral or hyperspectral, geometrical, texture, 3D information, radar, lidar and data fusion); (iv) image classification methods

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