Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Abstract

Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R 2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

“…Lately, attempts have been made to achieve higher accuracy of rice mapping using data from various image sensors. Optical images, such as AVHRR, MODIS, Landsat, and Sentinel-2A, as well as high spatial resolution images (IKONOS, Quick-Bird) and hyperspectral images [19][20][21][22][23][24][25][26]94] were used for rice mapping. However, most rice-growing regions have serious cloud contamination, which is a challenge for obtaining enough clean optical images.…”

“…Moderate resolution imaging spectroradiometer (MODIS) data have been used to map rice worldwide due to their high temporal and moderate spatial resolutions [19,20]. Landsat (30 m) data have a higher spatial resolution than MODIS, so they can be used to develop more accurate rice maps for less extensive areas [21,22]. Recently, Sentinel-2A MSI (Multispectral Instrument), with higher spatial and spectral resolutions than Landsat data, has been used to map paddy rice and other land-cover types [23,24].…”

Time Series MODIS and in Situ Data Analysis for Mongolia Drought

“…For instance, VIs from MODIS sensors have great improvements in spatial, spectral and radiometric measurements of surface vegetation conditions [18], and NDVI could be used to detect the changes of surface vegetation conditions [19][20][21][22], to monitor the maize green leaf area index [23], to estimate evapotranspiration [24,25], to conduct flood mapping and disaster loss assessment [26,27] and to perform environmental vulnerability assessment [28]. In addition, many studies showed that NDVI is a useful index for studying vegetation and ecosystems in semi-arid environments where vegetation cover is less than 30% [29,30].…”

Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm

“…Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. Flooding mapping needs to use high temporal remote sensing images [4][5][6], but these images usually have relatively low spatial resolutions. The mixed pixel issue, in which one pixel covers multiple types of land surfaces, commonly occurs in such images.…”