1. Introduction
The extensive and steadily expanding use of plastic films in agriculture, and particularly 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]. Therefore, an accurate remote sensing mapping of plastic covered greenhouses (PCG), walk-in tunnels, low tunnels and plastic-mulched farmland (PMF) has important practical significance for analyzing land-use evolutions and guiding regional agricultural production [
3,
4].
The recent work published by [
5] provides an overview of the general research dynamics regarding the topic of remote sensing-based mapping of agricultural greenhouses and plastic-mulched crops throughout the 21st century. 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 (unsupervised or supervised, parametric or non-parametric and machine learning or deep learning-based approaches); (v) single or multitemporal remotely sensed data; vi) and only mapping plastic covering structures or going a step further to classify and map plastic covered crops.
A groundbreaking very high resolution (VHR) satellite named WorldView-3 (WV3) was successfully launched on 13 August 2014. WV3 opened up new opportunities for analyzing and mapping plastic materials thanks to its global super-spectral observation capabilities (eight VNIR bands and eight SWIR bands) [
6].
Throughout the last decade, studies on plastic detection via remote sensing have been focused on anthropogenic marine debris mapping in the oceans [
7,
8,
9]. Acuña-Ruz et al. [
7] employed eight VNIR bands and the panchromatic band of WV3 data, along with different classification methods, to map plastic debris on beaches. Biermann et al. [
8] used remote sensing techniques with Sentinel-2 for mapping patches of floating macroplastics on the ocean surface. Themistocleous et al. [
9] detected plastic targets on the sea surface through the proposed Plastic Index (PI) based on Sentinel-2 data. Moreover, several works have been carried out for mapping plastic films in agriculture, such as PCG [
10,
11] or PMF [
12,
13]. Novelli et al. [
10] carried out the first work based on Sentinel-2 Multispectral Instrument (MSI) and Landsat 8 OLI images to map PCG by adopting an OBIA approach and an RF classifier. Yang et al. [
11] developed a specific index for the detection of PCG (Plastic Greenhouse Index; PGI) from Landsat TM and Landsat 8 OLI images. Lu et al. [
12] developed a simple but robust decision tree (DT) classifier to detect PMF from only two Landsat 5 TM images. Hasituya et al. [
13] used Landsat 8 OLI imagery and GoogleEarth data (training phase) to map PMF using spectral and textural characteristics. Research by [
14] addressed the classification of plastic materials in urban areas using the short-wave infrared (SWIR) bands from a WV3 image, obtaining promising results. Asadzadeh and de Souza Filho [
15] tested the WV3’s SWIR bands for the detection of hydrocarbons, which are spectrally similar to plastics. In fact, plastic is a type of synthetic polymer or hydrocarbon commonly made by linking numerous hydrocarbon monomers together into long chains of molecules [
16].
Several indices have been proposed in the aforementioned studies for mapping plastic materials. Among them, we highlight the following: (i) The Plastic Greenhouse Index (PGI), derived from the visible and near-infrared (VNIR) bands of Landsat 7 Enhanced Thematic Mapper plus (ETM+) imagery [
11]; (ii) the Plastic Index (PI), from the VNIR bands of Sentinel-2 [
9]; (iii) the Plastic–Mulched Landcover Index (PMLI), from the SWIR bands of Landsat 5 Thematic Mapper (TM) images [
12]; (iv) the Relative-absorption Band Depth index (RBD), derived from WV3 SWIR bands [
15]; and (v) the Normalized Difference Plastic Index (NDPI), from WV3 SWIR bands [
14].
Regarding the spectral properties (350–2500 nm) of human-made plastic materials used in covered agriculture, Levin et al. [
17] analyzed, in the laboratory, 15 samples of polyethylene (PE) sheets (transparent, black, opaque, and yellow opaque), and various nets (white and black) used in Israel, utilizing a field spectroradiometer Analytical Spectral Devices FieldSpec Pro (ASD 2001). They also considered field data taken from a hyperspectral AISA-ES image with a spatial resolution of 1 m. They pointed out three major absorption features around 1218 nm, 1732 nm and 2313 nm for plastic detection. Moreover, these valley values were not affected by settling dust, whitewashing or by the underlying surface. They marked the value of 1732 nm as the best spectral feature for plastic mapping.
More recently, Guo and Li [
14] analyzed the spectra corresponding to urban plastic materials. They found that the reflectance of different plastics was highly variable in the visible spectral range depending on, for instance, the colors of the plastic samples. However, in the NIR to SWIR spectral range, the shape of spectral curve was related to the specific plastic composition. Plastics are organic compounds with numerous C-H bonds. Existing studies have indicated that these bonds have diagnostic absorption features at the wavelength regions of 1100–1250 nm (second overtone of C-H stretching mode), 1300–1450 nm (C-H combination band), 1600–1800 nm (first overtone of C-H stretching mode), and 2150–2500 nm (combination band) [
18]. Guo and Li [
14] tried to transfer this knowledge to the spectral information included in the eight WV3 SWIR bands, detecting two declining trends: (i) between band 10 (SWIR10) and band 12 (SWIR12) (i.e., 1570 nm to 1730 nm), and (ii) from band 13 (SWIR13) to band 16 (SWIR16) (i.e., 2165 nm to 2330 nm). These findings led to the proposal of the NDPI index [
14].
The innovative goal addressed in this work relies on testing the predictive power attributed to features derived from the WV3 SWIR bands, in contrast to those obtained from the WV3 VNIR bands for mapping PCG within the context of an OBIA approach. Thus far, and to the best knowledge of the authors, this is the first research work that deals with this topic.
6. Discussion
The automatic MRS segmentation carried out in this work achieved a modified ED2 value of 0.313 with a resonable visual quality (
Figure 3a). In others works, when dealing with VNIR WorldView-2 (WV2) (2 m GSD) or WV3 (1.2 m GSD) atmospherically corrected orthoimages, ED2 values ranged from 0.11 to 0.29. In fact, Aguilar et al. [
21], using all eight equally weighted bands and the original ED2 metric proposed by [
25], reported the best original ED2 value (0.11) for a VNIR WV2 orthoimage taken in September 2013, and the worst one (0.29) from a VNIR WV2 orthoimage collected in July 2015. Novelli et al. [
10] improved the last ED2 value to 0.199, working with the same WV2 orthoimage (July 2015) but using a blue-green-NIR2 equally weighted band combination and the command line tool AssesSeg. Finally, Aguilar et al. [
24] reported a modified ED2 valued of 0.141 from a VNIR WV3 orthoimage taken on July 5, 2016; in this case, using the band combination recommended by [
10] and AssesSeg. All these data related to the segmentation of PCG reflect that this is an important and not yet concluded research line. In this sense, it is important to note a significant increase in recent years in the number of remote sensing image segmentation-related scientific publications, together with the available image segmentation methods (e.g., semantic segmentation) [
32]. Perhaps the introduction of SWIR bands in the MRS should also be studied in the future.
Regarding the classification accuracy assessments, both object-based (
Table 3) and pixel-based (
Table 4) tests reached similar results for all the three strategies, though the final pixel-based classifications were always slightly better. In previous works, the accuracies dealing with a sample of meaningful objects were always better than the pixel-based results achieved in the whole study area using a raster ground truth [
21,
22]. In the current work, and unlike the works cited above, the sample of objects used in the object-based accuracy assessments (i.e., 2350) covered most of the existing objects in the segmentation of the study area (about 3188). Moreover, as the segmentation of PCG was based on the ground truth, many of the misclassification problems due to errors in the segmentation were avoided.
The OA values significantly improved from 90.85% (using VNIR strategy) to 97.38% (with All Features strategy). The last OA value achieved by using the information contained in the 16 WV3 bands (VNIR plus SWIR) turns out to be the best that has been published in recent years for PCG or PMF [
22,
33,
34,
35,
36]. It is important to note here that an even better OA value of 98.08% was reached using a knowledge-based DT for dealing with the whitewashed issue related to the disturbance of the spectral reflectance characteristics of the plastic films (
Figure 7). With this strategy, only two PCG were wrongly classified as Non-GH. One of these greenhouses was made of glass, as already mentioned, while the other was a PCG abnormally whitewashed with a massive amount of lime.
It is important to note that Levin et al. [
17] stated that the three main absorption wavelengths for agricultural plastic films were located at 1218 nm, 1732 nm and 2313 nm, and they were not affected by settling dust, whitewashing or by the underlying surface. Looking at
Figure 7, the valley values reported by [
17] could be appreciated only in the greenhouses without whitewashing.
The WV3 SWIR bands information, particularly in the form of NDPI and PMLI indices, provided valuable information to improve the separability between GH and Non-GH classes. However, the mean and SD object values of SWIR bands had a low importance in the PCG classification (
Table 4). Furthermore, RBD index was not between the most important features for mapping PCG. In fact, any relationship between SWIR11 and SWIR13 bands can be found in
Figure 7.
The result achieved in the study area was good thanks to the eight WV3 SWIR bands and the plastic discrimination power of NDPI. Unfortunately, WV3 is a commercial VHR satellite which is not available for free; this is a particular problem when applied to very large areas. In that sense, it would be necessary to study the capability of other optical sensors containing SWIR bands, such as Sentinel-2 or Landsat 8, or even the hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor developed by the Italian Space Agency [
37] for mapping PCG while taking into account the special characteristics of plastic materials.
Having verified the importance of the WV3 SWIR bands in the detection of PCG, we propose to go a step further, trying to distinguish between different types of plastic films used in the different PCG. According to [
1], the main plastic films used in Spain are polyethylene (PE), polyvinylchloride (PVC) and polypropylene (PP), in that order. Moreover, up to 10% of greenhouse films are made of ethylene-vinyl acetate (EVA), sometimes being co-extruded with three layers made of EVA and low-density polyethylene (LDPE).
7. Conclusions
This study aimed to test the capability of the eight SWIR bands included in WV3 imagery (3.2 m GSD) to improve classification accuracy for mapping PCG attained from the VNIR eight bands (1.2 m GSD). For that, three strategies (VNIR, SWIR and All Features) were carried out by applying an OBIA approach.
The accuracy results clearly showed that the information available from WV3 SWIR bands (mainly NDPI and, secondly, PMLI indices) significantly improved the OA and kappa measures. Values of 90.85% and 0.812 for OA and kappa, respectively, were achieved using VNIR strategy, while improvements of around 6.7% for OA and 14% for kappa were reached working with the All Features strategy.
The NDPI index showed its potential for detecting plastic materials, resulting in the most important feature for mapping PCG. However, the fact that some PCGs within the study area were whitewashed in June 2020 decreased the NDPI values by masking the presence of plastic films. The last issue was addressed in this work by proposing a new index named NDPI_B that combines the NDPI index and Brightness for improving the detection of whitewashed PCG. Impressive accuracy values of 98.08% and 0.959 for OA and kappa, respectively, were obtained from using only the NDPI_B index.
Although the results achieved here are promising, we have only scratched the surface of the possibilities that WV3 offers for mapping PCG. Further works should be carried out in this research area.