Evaluation of the Surface Water Distribution in North-Central Namibia Based on MODIS and AMSR Series

Mizuochi, Hiroki; Hiyama, Tetsuya; Ohta, Takeshi; Nasahara, Kenlo Nishida

doi:10.3390/rs6087660

Cited by 23 publications

(18 citation statements)

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“…The creation of a LUT, therefore, requires an interpolation of the gaps. In the original DBUX [33,34], a bin-average approach was used to fill the gaps. That is, the match-ups were allocated to the bins with a regular interval of pixel values of the temporal images, and then the averages were obtained for each bin, which resulted in a step-like function, with a step at each bin.…”

Section: Training Of Random Forest Database Unmixing (Rfdbux)mentioning

confidence: 99%

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Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

et al. 2018

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In this study, a novel data fusion approach was used to monitor the water-body extent in a tropical wetland (Lake Sentarum, Indonesia). Monitoring is required in the region to support the conservation of water resources and biodiversity. The developed approach, random forest database unmixing (RFDBUX), makes use of pixel-based random forest regression to overcome the limitations of the existing lookup-table-based approach (DBUX). The RFDBUX approach with passive microwave data (AMSR2) and active microwave data (PALSAR-2) was used from 2012 to 2017 in order to obtain PALSAR-2-like images with a 100 m spatial resolution and three-day temporal resolution. In addition, a thresholding approach for the obtained PALSAR-2-like backscatter coefficient images provided water body extent maps. The validation revealed that the spatial patterns of the images predicted by RFDBUX are consistent with the original PALSAR-2 backscatter coefficient images (r = 0.94, RMSE = 1.04 in average), and that the temporal pattern of the predicted water body extent can track the wetland dynamics. The PALSAR-2-like images should be a useful basis for further investigation of the hydrological/climatological features of the site, and the proposed approach appears to have the potential for application in other tropical regions worldwide. and growth of the neighboring countries of Indonesia. This development policy has resulted in rapid population growth and large changes in land use and land cover in the watershed, which may impact the water quality and dynamics of it [4].Geographical information is needed regarding the lake area to support the conservation of water resources and biodiversity. This is especially important for identifying the water-body dynamics, because during the dry season, the volume of water is very small and found only in the main river, soil basins, and oxbow lakes. During the rainy season, the water overflows inundated areas, lakes, puddles, and river courses. This annual inundation increases the variety of habitats available to the aquatic organisms [5]. In addition to local factors, the amount of lake water is strongly affected by global climate variability and events. For example, the lake becomes extremely dry in the El Niño years [6]. This means that the water-extent variability is controlled by both seasonal changes and the far less predictable annual changes. Even though long-term, frequent, and detailed mapping of the lake water extent is desirable and needed, few studies have attempted it.Satellite remote sensing is suitable for such a purpose. In fact, many techniques for monitoring wetlands have been proposed and developed [7]. In particular, microwave remote sensing is frequently used because it has two advantages, a high sensitivity to surface water instead of liquid water on the ground and observational capability, even under cloud cover or at night.One of the main microwave remote sensing approaches involves the use of synthetic aperture radar (SAR). The backscatter coefficient images derived from SAR can dis...

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Section: Training Of Random Forest Database Unmixing (Rfdbux)mentioning

confidence: 99%

“…Mizuochi et al [33,34] developed another type of STF called database unmixing (DBUX). One of the drawbacks of other STFs is their inability to predict sudden changes in the land surface and the necessity of having ancillary data, such as land-cover maps.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

et al. 2018

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“…At present, remote sensing has become a routine approach for land surface water bodies' monitoring, because the acquired data can provide macroscopic, real-time, dynamic and cost-effective information, which is substantially different from conventional in situ measurements [4][5][6]. Various methods, including single band density slicing [7], unsupervised and supervised classification [8][9][10][11] and spectral water indexes [12][13][14][15][16][17][18][19], were developed in order to extract water bodies from different remote sensing images. Among all existing water body mapping methods, the spectral water index-based method is a type of reliable method, because it is user friendly, efficient and has low computational cost [20].…”

Section: Introductionmentioning

confidence: 99%

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

et al. 2016

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Abstract:Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies' mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and À Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

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“…Recently, Mizuochi et al (2014) detected the distribution of surface water with high spatial and temporal resolution by using two types of complementary satellite data-MODerate-resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)-as well as AMSR2 after AMSR-E became unavailable. They then aggregated the extracted water presence maps into probability of water presence (PWP) maps for the rainy season and the whole year, and estimated the area potentially suitable for rice cultivation.…”

Section: Introductionmentioning

confidence: 99%

Analysing the origin of rain- and subsurface water in seasonal wetlands of north-central Namibia

Hiyama

Kanamori

Kambatuku

et al. 2017

Environ. Res. Lett.

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We investigated the origins of rain-and subsurface waters of north-central Namibia's seasonal wetlands, which are critical to the region's water and food security. The region includes the southern part of the Cuvelai system seasonal wetlands (CSSWs) of the Cuvelai Basin, a transboundary river basin covering southern Angola and northern Namibia. We analysed stable water isotopes (SWIs) of hydrogen (HDO) and oxygen (H 2 18 O) in rainwater, surface water and shallow groundwater. Rainwater samples were collected during every rainfall event of the rainy season from October 2013 to April 2014. The isotopic ratios of HDO (dD) and oxygen H 2 18 O (d 18 O) were analysed in each rainwater sample and then used to derive the annual mean value of (dD, d 18 O) in precipitation weighted by each rainfall volume. Using delta diagrams (plotting dD vs. d 18 O), we showed that the annual mean value was a good indicator for determining the origins of subsurface waters in the CSSWs. To confirm the origins of rainwater and to explain the variations in isotopic ratios, we conducted atmospheric water budget analysis using Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) data and ERA-Interim atmospheric reanalysis data. The results showed that around three-fourths of rainwater was derived from recycled water at local-regional scales. Satellite-observed outgoing longwave radiation (OLR) and complementary satellite data from MODerate-resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer (AMSR) series implied that the isotopic ratios in rainwater were affected by evaporation of raindrops falling from convective clouds. Consequently, integrated SWI analysis of rain-, surface and subsurface waters, together with the atmospheric water budget analysis, revealed that shallow groundwater of small wetlands in this region was very likely to be recharged from surface waters originating from local rainfall, which was temporarily pooled in small wetlands. This was also supported by tritium ( 3 H) counting of the current rain-and subsurface waters in the region. We highly recommend that shallow groundwater not be pumped intensively to conserve surface and subsurface waters, both of which are important water resources in the region.

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Evaluation of the Surface Water Distribution in North-Central Namibia Based on MODIS and AMSR Series

Cited by 23 publications

References 20 publications

Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Analysing the origin of rain- and subsurface water in seasonal wetlands of north-central Namibia

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