Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

Abstract

Abstract:Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R 2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R 2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…Then, we extracted the median values of the characteristic variables using the GEE cloud computing platform [58]. The characteristic variables included the multispectral bands (B2, B3, B4, B5, B6, B7, B8, B8A, B10, B11, and B12) and the vegetation indices selected according to the research of Astola et al [4], Wittke et al [81], and Xia et al [82]. The vegetation indices were calculated as follows (Table 3).…”

Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

“…According to the spatiotemporal resolutions and the length of time series, three satellite sensors, namely, Moderate Resolution Imaging Spectroradiometer (MODIS), Système Pour I'Observation de la Terre (SPOT), and Advanced Very High Resolution Radiometer (AVHRR), have often been used to monitor vegetation phenology [16,34]. Vegetation indices (e.g., EVI, Enhanced Vegetation Index; NDVI, Normalized Difference Vegetation Index) based on the spectral reflectance of vegetation are well correlated with chlorophyll abundance, photosynthetically active biomass, and energy absorption [48][49][50]. The EVI, which could avoid the problem of NDVI saturation in high vegetation coverage areas and reduce the effects of atmospheric and soil background, has proven to be a robust indicator of vegetation growth [51].…”

Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine

“…Satellite-based methods have advantages compared to the traditional methods in surface water mapping due to the low cost, high frequency, and repeatable observations. In recent decades, regional, continental, and global-scales surface water areas have been investigated using the advanced very high resolution radiometer (AVHRR) [5,6], the moderate-resolution imaging spectro-radiometer (MODIS) [7], Landsat [8][9][10][11][12][13][14][15][16][17][18], Sentinel satellite images and so on. Meanwhile, many satellite-based approaches have been developed to detect surface water.…”

“…The surface water detection algorithms can be roughly divided into general feature classification methods and thematic water surface extraction algorithms [17,19]. General feature classification methods include spectral mixture models [20,21], maximum likelihood classification [22], artificial intelligence methods [15,23,24], etc. However, these methods are difficult to quickly map water body using multi-temporal images over a large basin, a big country, and at a global scale [19], because it needs human expertise and knowledge to select samples and training algorithms.…”