Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: a case study in southern Xinjiang, China

Abstract

Abstract. Plastic-mulched landcover (PML) is the land surface covered by thin plastic films. PML has been expanding rapidly worldwide and has formed a significant agriculture landscape in the last two decades. Large-scale PML may impact the regional and global climate, ecosystem, and environment because it changes the energy balance and water cycles of the land surfaces, reduces the biodiversity, deteriorates the soil structure, etc. To study its impact, the spatial and temporal distributions and dynamics of PML have to be obtained. This paper presents a threshold model (TM) for PML detection and mapping with moderate-resolution imaging spectroradiometer (MODIS) time series data. Based on the temporal-spectral features of PML in the early stage of a growing season after planting, a TM was designed with the number of days (d) when the normalized difference vegetation index (NDVI) value is larger than a threshold value (x) as the discriminator. The model has been successfully applied to map PML in southern Xinjiang, China, from the interpolated MODIS NDVI time series (from 90th to 125th day of each year). Results indicate that when TM parameter x is set to 0.2 and d to 8, the overall accuracy and kappa coefficient (κ) are >0.84 and 0.65, respectively. We believe this classification accuracy can meet the PML mapping for large geographic areas. Furthermore, visual comparison between the PML maps from TM classification of MODIS time series and that from the maximum likelihood classification of Landsat ETM+ and OLI images shows they are consistent both in the pattern and location of PML. Therefore, detection and mapping of PML by using MODIS time series with the TM method is feasible. The PML mapping in this study used a cropland mask derived from Landsat images using a maximum likelihood classifier to mask out non-cropland when applying the TM algorithm. The accuracy of such a mask is subject to further study. Because of frequent global coverages of MODIS data, the method presented in this paper could potentially be used for PML detecting and mapping at continental and global scales.

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

“…In fact, these accuracy values resulted much more stable over the time than those reported by Lu et al [17,18] working with time series from Landsat 5 TM and MODIS. The main source of error of the proposed TM was located at elongated objects situated between greenhouses where Non-GH objects were classified as GH (see objects in brown in Figure 6e,f).…”

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

“…The normalized difference vegetation index (NDVI) [32] and normalized difference built-up index (NDBI) [58] were taken into account for PMF mapping in this study. NDVI is a pixel-based measure of the vegetation, while NDBI is a measure of built-up area within a Landsat pixel.…”

A New Spatial Attraction Model for Improving Subpixel Land Cover Classification

“…Remote sensing is the only feasible way of obtaining LULC information for large geographic areas. Many algorithms have been developed to classify various remote sensing data to obtain LULC maps [1][2][3][4]. However, remote sensing images often contain mixed pixels, since the sensor's instantaneous field of view (IFOV) includes more than one land cover class [5,6].…”

“…The existence of mixed pixels leads to three main problems which need be solved: (1) What classes of land cover does a mixed pixel contain? (2) What are the proportions of land cover classes in a pixel? (3) What is the subpixel spatial distribution of land cover classes [7]?…”