Remote Sensing

Research

18472 KiB

Open AccessArticle

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

by Sheng Chang, Bingfang Wu, Nana Yan, Bulgan Davdai and Elbegjargal Nasanbat

Remote Sens. 2017, 9(7), 650; https://doi.org/10.3390/rs9070650 - 26 Jun 2017

Cited by 39 | Viewed by 7378

In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices [...] Read more.

In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices for accurate and real-time expression of grassland drought in Mongolia. Firstly, an adaptability analysis was performed by comparing nine remote sensing-derived drought indices with reference indicators obtained from field observations using several methods (correlation, consistency percentage (CP), and time-space analysis). The reference information included environmental data, vegetation growth status, and region drought-affected (RDA) information at diverse scales (pixel, county, and region) for three types of land cover (forest steppe, steppe, and desert steppe). Second, a meteorological index (PED), a normalized biomass (NorBio) reference indicator, and the RDA-based drought CP method were adopted for describing Mongolian drought. Our results show that in forest steppe regions the normalized difference water index (NDWI) is most sensitive to NorBio (maximum correlation coefficient (MAX_R): up to 0.92) and RDA (maximum CP is 87%), and is most consistent with RDA spatial distribution. The vegetation health index (VHI) and temperature condition index (TCI) are most correlated with the PED index (MAX_R: 0.75) and soil moisture (MAX_R: 0.58), respectively. In steppe regions, the NDWI is most closely related to soil moisture (MAX_R: 0.69) and the VHI is most related to the PED (MAX_R: 0.76), NorBio (MCC: 0.95), and RDA data (maximum CP is 89%), exhibiting the most consistency with RDA spatial distribution. In desert steppe areas, the vegetation condition index (VCI) correlates best with NorBio (MAX_R: 0.92), soil moisture (MAX_R: 0.61), and RDA spatial distribution, while TCI correlates best with the PED (MAX_R: 0.75) and the RDA data (maximum CP is 79%). The VHI is a combination of constructed VCI and TCI, and can be used instead of them. Finally, the mode method was adopted to identify appropriate drought indices. The best two indices (VHI and NDWI) can be utilized to develop a combination drought model for accurately monitoring and quantifying drought in the future. Additionally, the new framework can be adopted to investigate and analyze the suitability of satellite-derived drought indices and determine the most appropriate index/indices for other countries or areas. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

10822 KiB

Open AccessArticle

Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean

by Kuo-Wei Lan, Teruhisa Shimada, Ming-An Lee, Nan-Jay Su and Yi Chang

Remote Sens. 2017, 9(5), 444; https://doi.org/10.3390/rs9050444 - 5 May 2017

Cited by 63 | Viewed by 8124

Abstract

Changes in marine environments affect fishery resources at different spatial and temporal scales in marine ecosystems. Predictions from species distribution models are available to parameterize the environmental characteristics that influence the biology, range, and habitats of the species of interest. This study used [...] Read more.

Changes in marine environments affect fishery resources at different spatial and temporal scales in marine ecosystems. Predictions from species distribution models are available to parameterize the environmental characteristics that influence the biology, range, and habitats of the species of interest. This study used generalized additive models (GAMs) fitted to two spatiotemporal fishery data sources, namely 1° spatial grid and observer record longline fishery data from 2006 to 2010, to investigate the relationship between catch rates of yellowfin tuna and oceanographic conditions by using multispectral satellite images and to develop a habitat preference model. The results revealed that the cumulative deviances obtained using the selected GAMs were 33.6% and 16.5% in the 1° spatial grid and observer record data, respectively. The environmental factors in the study were significant in the selected GAMs, and sea surface temperature explained the highest deviance. The results suggest that areas with a higher sea surface temperature, a sea surface height anomaly of approximately −10.0 to 20 cm, and a chlorophyll-a concentration of approximately 0.05–0.25 mg/m³ yield higher catch rates of yellowfin tuna. The 1° spatial grid data had higher cumulative deviances, and the predicted relative catch rates also exhibited a high correlation with observed catch rates. However, the maps of observer record data showed the high-quality spatial resolutions of the predicted relative catch rates in the close-view maps. Thus, these results suggest that models of catch rates of the 1° spatial grid data that incorporate relevant environmental variables can be used to infer possible responses in the distribution of highly migratory species, and the observer record data can be used to detect subtle changes in the target fishing grounds. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Figure 1

2972 KiB

Open AccessArticle

Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions

by No-Wook Park, Phaedon C. Kyriakidis and Sungwook Hong

Remote Sens. 2017, 9(3), 255; https://doi.org/10.3390/rs9030255 - 9 Mar 2017

Cited by 43 | Viewed by 8040

Abstract

This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can [...] Read more.

This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

8021 KiB

Open AccessArticle

Decline of Geladandong Glacier Elevation in Yangtze River’s Source Region: Detection by ICESat and Assessment by Hydroclimatic Data

by Nengfang Chao, Zhengtao Wang, Cheinway Hwang, Taoyong Jin and Yung-Sheng Cheng

Remote Sens. 2017, 9(1), 75; https://doi.org/10.3390/rs9010075 - 14 Jan 2017

Cited by 22 | Viewed by 7425

Abstract

Several studies have indicated that glaciers in the Qinghai-Tibet plateau are thinning, resulting in reduced water supplies to major rivers such as the Yangtze, Yellow, Lancang, Indus, Ganges, Brahmaputra in China, and south Asia. Three rivers in the upstream of Yangtze River originate [...] Read more.

Several studies have indicated that glaciers in the Qinghai-Tibet plateau are thinning, resulting in reduced water supplies to major rivers such as the Yangtze, Yellow, Lancang, Indus, Ganges, Brahmaputra in China, and south Asia. Three rivers in the upstream of Yangtze River originate from glaciers around the Geladandong snow mountain group in central Tibet. Here we used elevation observations from Ice, Cloud, and land Elevation Satellite (ICESat) and reference elevations from a 3-arc-second digital elevation model (DEM) of Shuttle Radar Terrestrial Mission (SRTM), assisted with Landsat-7 images, to detect glacier elevation changes in the western (A), central (B), and eastern (C) regions of Geladandong. Robust fitting was used to determine rates of glacier elevation changes in regions with dense ICESat data, whereas a new method called rate averaging was employed to find rates in regions of low data density. The rate of elevation change was −0.158 ± 0.066 m·a⁻¹ over 2003–2009 in the entire Geladandong and it was −0.176 ± 0.102 m·a⁻¹ over 2003–2008 in Region C (by robust fitting). The rates in Regions A, B, and C were −0.418 ± 0.322 m·a⁻¹ (2000–2009), −0.432 ± 0.020 m·a⁻¹ (2000–2003), and −0.321 ± 0.139 m·a⁻¹ (2000–2008) (by rate averaging). We used in situ hydroclimatic dataset to assess these negative rates: the glacier thinning was caused by temperature rises around Geladandong, based on the temperature records over 1979–2009, 1957–2013, and 1966–2013 at stations Tuotuohe, Wudaoliang, and Anduo. The thinning Geladandong glaciers led to increased discharges recorded at the river gauge stations Tuotuohe and Chumda over 1956–2012. An unabated Geladandong glacier melting will reduce its long-term water supply to the Yangtze River Basin, causing irreversible socioeconomic consequences and seriously degrading the ecological system of the Yangtze River Basin. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

17819 KiB

Open AccessArticle

Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution

by Zhiyong Lv, Wenzhong Shi, Jón Atli Benediktsson and Xiaojuan Ning

Remote Sens. 2016, 8(12), 1023; https://doi.org/10.3390/rs8121023 - 15 Dec 2016

Cited by 14 | Viewed by 6926

Abstract

Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover [...] Read more.

Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%–18% improvement in overall accuracy. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

9725 KiB

Open AccessArticle

Analysis of the Effects of Drought on Vegetation Cover in a Mediterranean Region through the Use of SPOT-VGT and TERRA-MODIS Long Time Series

by Mehrez Zribi, Ghofrane Dridi, Rim Amri and Zohra Lili-Chabaane

Remote Sens. 2016, 8(12), 992; https://doi.org/10.3390/rs8120992 - 2 Dec 2016

Cited by 30 | Viewed by 5560

Abstract

The analysis of vegetation dynamics and agricultural production is essential in semi-arid regions, in particular as a consequence of the frequent occurrence of periods of drought. In this paper, a multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION [...] Read more.

The analysis of vegetation dynamics and agricultural production is essential in semi-arid regions, in particular as a consequence of the frequent occurrence of periods of drought. In this paper, a multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION (between September 1998 and August 2013) and TERRA-MODIS satellite data (between September 2000 and August 2013), was used to analyze the vegetation dynamics over the central region of Tunisia in North Africa, which is characterized by a semi-arid climate. Products derived from these two satellite sensors are generally found to be coherent. Our analysis of land use and NDVI anomalies, based on the Vegetation Anomaly Index (VAI), reveals a strong level of agreement between estimations made with the two satellites, but also some discrepancies related to the spatial resolution of these two products. The vegetation’s behavior is also analyzed during years affected by drought through the use of the Windowed Fourier Transform (WFT). Discussions of the dynamics of annual agricultural areas show that there is a combined effect between climate and farmers’ behavior, leading to an increase in the prevalence of bare soils during dry years. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

7412 KiB

Open AccessArticle

Spatial Correlation of Satellite-Derived PM_2.5 with Hospital Admissions for Respiratory Diseases

by Ching-Ju Liu, Chian-Yi Liu, Ngoc Thi Mong and Charles C. K. Chou

Remote Sens. 2016, 8(11), 914; https://doi.org/10.3390/rs8110914 - 3 Nov 2016

Cited by 18 | Viewed by 6581

Abstract

Respiratory diseases, particularly allergic rhinitis, are spatially and temporally correlated with the ground PM_2.5 level. A study of the correlation between the two factors should therefore account for spatiotemporal variations. Satellite observation has the advantage of wide spatial coverage over pin-point style [...] Read more.

Respiratory diseases, particularly allergic rhinitis, are spatially and temporally correlated with the ground PM_2.5 level. A study of the correlation between the two factors should therefore account for spatiotemporal variations. Satellite observation has the advantage of wide spatial coverage over pin-point style ground-based in situ monitoring stations. Therefore, the current study used both ground measurement and satellite data sets to investigate the spatial and temporal correlation of satellite-derived PM_2.5 with respiratory diseases. This study used 4-year satellite data and PM_2.5 levels of the period at eight stations in Taiwan to obtain the spatial and temporal relationship between aerosol optical depth (AOD) and PM_2.5. The AOD-PM_2.5 model was further examined using the cross-validation (CV) technique and was found to have high reliability compared with similar models. The model was used to obtain satellite-derived PM_2.5 levels and to analyze the hospital admissions for allergic rhinitis in 2008. The results suggest that adults (18–65 years) and children (3–18 years) are the most vulnerable groups to the effect of PM_2.5 compared with infants and elderly people. This result may be because the two affected age groups spend longer time outdoors. This result may also be attributed to the long-range PM_2.5 transport from upper stream locations and the atmospheric circulation patterns, which are significant in spring and fall. The results of the current study suggest that additional environmental factors that might be associated with respiratory diseases should be considered in future studies. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

4159 KiB

Open AccessArticle

Guidance Index for Shallow Landslide Hazard Analysis

by Cheila Avalon Cullen, Rafea Al-Suhili and Reza Khanbilvardi

Remote Sens. 2016, 8(10), 866; https://doi.org/10.3390/rs8100866 - 20 Oct 2016

Cited by 24 | Viewed by 7964

Abstract

Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the [...] Read more.

Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

32996 KiB

Open AccessArticle

Spectral Discrimination of Vegetation Classes in Ice-Free Areas of Antarctica

by María Calviño-Cancela and Julio Martín-Herrero

Remote Sens. 2016, 8(10), 856; https://doi.org/10.3390/rs8100856 - 18 Oct 2016

Cited by 38 | Viewed by 7400

Abstract

Detailed monitoring of vegetation changes in ice-free areas of Antarctica is crucial to determine the effects of climate warming and increasing human presence in this vulnerable ecosystem. Remote sensing techniques are especially suitable in this distant and rough environment, with high spectral and [...] Read more.

Detailed monitoring of vegetation changes in ice-free areas of Antarctica is crucial to determine the effects of climate warming and increasing human presence in this vulnerable ecosystem. Remote sensing techniques are especially suitable in this distant and rough environment, with high spectral and spatial resolutions needed owing to the patchiness and similarity between vegetation elements. We analyze the reflectance spectra of the most representative vegetation elements in ice-free areas of Antarctica to assess the potential for discrimination. This research is aimed as a basis for future aircraft/satellite research for long-term vegetation monitoring. The study was conducted in the Barton Peninsula, King George Island. The reflectance of ground patches of different types of vegetation or bare ground (c. 0.25

m^{2}

,

n = 30

patches per class) was recorded with a spectrophotometer measuring between 340

nm

to 1025

nm

at a resolution of 0.38

n m

. We used Linear Discriminant Analysis (LDA) to classify the cover classes according to reflectance spectra, after reduction of the number of bands using Principal Component Analysis (PCA). The first five principal components explained an accumulated 99.4% of the total variance and were added to the discriminant function. The LDA classification resulted in c. 92% of cases correctly classified (a hit ratio 11.9 times greater than chance). The most important region for discrimination was the visible and near ultraviolet (UV), with the relative importance of spectral bands steeply decreasing in the Near Infra-Red (NIR) region. Our study shows the feasibility of discriminating among representative taxa of Antarctic vegetation using their spectral patterns in the near UV, visible and NIR. The results are encouraging for hyperspectral vegetation mapping in Antarctica, which could greatly facilitate monitoring vegetation changes in response to a changing environment, reducing the costs and environmental impacts of field surveys. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

35058 KiB

Open AccessArticle

A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

by ZhiYong Lv, Haiqing He, Jón Atli Benediktsson and Hong Huang

Remote Sens. 2016, 8(10), 814; https://doi.org/10.3390/rs8100814 - 30 Sep 2016

Cited by 12 | Viewed by 6613

Abstract

Very high resolution (VHR) remote sensing images are widely used for land cover classification. However, to the best of our knowledge, few approaches have been shown to improve classification accuracies through image scene decomposition. In this paper, a simple yet powerful observational scene [...] Read more.

Very high resolution (VHR) remote sensing images are widely used for land cover classification. However, to the best of our knowledge, few approaches have been shown to improve classification accuracies through image scene decomposition. In this paper, a simple yet powerful observational scene scale decomposition (OSSD)-based system is proposed for the classification of VHR images. Different from the traditional methods, the OSSD-based system aims to improve the classification performance by decomposing the complexity of an image’s content. First, an image scene is divided into sub-image blocks through segmentation to decompose the image content. Subsequently, each sub-image block is classified respectively, or each block is processed firstly through an image filter or spectral–spatial feature extraction method, and then each processed segment is taken as the feature input of a classifier. Finally, classified sub-maps are fused together for accuracy evaluation. The effectiveness of our proposed approach was investigated through experiments performed on different images with different supervised classifiers, namely, support vector machine, k-nearest neighbor, naive Bayes classifier, and maximum likelihood classifier. Compared with the accuracy achieved without OSSD processing, the accuracy of each classifier improved significantly, and our proposed approach shows outstanding performance in terms of classification accuracy. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

13314 KiB

Open AccessArticle

Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model

by Xiaobo Luo and Yidong Peng

Remote Sens. 2016, 8(9), 760; https://doi.org/10.3390/rs8090760 - 15 Sep 2016

Cited by 40 | Viewed by 7760

Abstract

Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this [...] Read more.

Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR) approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m). The impact factors include the Soil-adjusted Vegetation Index (SAVI), the Index-based Built-up Index (IBI), and the Soil Brightness Index (NDSI), which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS) model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R²), Corrected Akaike Information Criterion (AICc), and F-test at small spatial resolution (< 240 m); however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

6996 KiB

Open AccessArticle

Thermal Imagery-Derived Surface Inundation Modeling to Assess Flood Risk in a Flood-Pulsed Savannah Watershed in Botswana and Namibia

by Jeri J. Burke, Narcisa G. Pricope and James Blum

Remote Sens. 2016, 8(8), 676; https://doi.org/10.3390/rs8080676 - 20 Aug 2016

Cited by 15 | Viewed by 8433

Abstract

The Chobe River Basin (CRB), a sub-basin of the Upper Zambezi Basin shared by Namibia and Botswana, is a complex hydrologic system that lies at the center of the world’s largest transfrontier conservation area. Despite its regional importance for livelihoods and biodiversity, its [...] Read more.

The Chobe River Basin (CRB), a sub-basin of the Upper Zambezi Basin shared by Namibia and Botswana, is a complex hydrologic system that lies at the center of the world’s largest transfrontier conservation area. Despite its regional importance for livelihoods and biodiversity, its hydrology, controlled by the timing and relative contributions of water from two regional rivers, remains poorly understood. An increase in the magnitude of flooding in this region since 2009 has resulted in significant displacements of rural communities. We use an innovative approach that employs time-series of thermal imagery and station discharge data to model seasonal flooding patterns, identify the driving forces that control the magnitude of flooding and the high population density areas that are most at risk of high magnitude floods throughout the watershed. Spatio-temporal changes in surface inundation determined using NASA Moderate-resolution Imaging Spectroradiometer (MODIS) thermal imagery (2000–2015) revealed that flooding extent in the CRB is extremely variable, ranging from 401 km² to 5779 km² over the last 15 years. A multiple regression model of lagged discharge of surface contributor basins and flooding extent in the CRB indicated that the best predictor of flooding in this region is the discharge of the Zambezi River 64 days prior to flooding. The seasonal floods have increased drastically in magnitude since 2008 causing large populations to be displaced. Over 46,000 people (53% of Zambezi Region population) are living in high magnitude flood risk areas, making the need for resettlement planning and mitigation strategies increasingly important. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

4883 KiB

Open AccessArticle

Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment

by Sanggoo Kang and Kiwon Lee

Remote Sens. 2016, 8(8), 662; https://doi.org/10.3390/rs8080662 - 16 Aug 2016

Cited by 18 | Viewed by 6768

Abstract

Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes [...] Read more.

Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes automatic scalability, i.e., the scale-out and scale-in processing of virtual servers in a cloud computing environment. This study investigates the applicability of auto-scaling to geo-based image processing algorithms by comparing the performance of a single virtual server and multiple auto-scaled virtual servers under identical experimental conditions. In this study, the cloud computing environment is built with OpenStack, and four algorithms from the Orfeo toolbox are used for practical geo-based image processing experiments. The auto-scaling results from all experimental performance tests demonstrate applicable significance with respect to cloud utilization concerning response time. Auto-scaling contributes to the development of web-based satellite image application services using cloud-based technologies. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

4407 KiB

Open AccessArticle

Time Series MODIS and in Situ Data Analysis for Mongolia Drought

by Munkhzul Dorjsuren, Yuei-An Liou and Chi-Han Cheng

Remote Sens. 2016, 8(6), 509; https://doi.org/10.3390/rs8060509 - 16 Jun 2016

Cited by 44 | Viewed by 9560

Abstract

Drought is a period of abnormally dry weather with a serious shortage of water supply. Drought indices can be an advantageous indicator to assess drought for taking further response actions. However, drought indices based on ground meteorological measurements could not completely reveal the [...] Read more.

Drought is a period of abnormally dry weather with a serious shortage of water supply. Drought indices can be an advantageous indicator to assess drought for taking further response actions. However, drought indices based on ground meteorological measurements could not completely reveal the land use effects over a regional scale. On the other hand, the satellite-derived products provide consistent, spatial and temporal comparisons of global signatures for the regional-scale drought events. This research is to investigate the drought signatures over Mongolia by using satellite remote sensing imagery. The evapotranspiration (ET), potential evapotranspiration (PET) and two-band Enhanced Vegetation Index (EVI2) were extracted from MODIS data. Based on the standardized ratio of ET to PET (ET/PET) and EVI2, the Modified Drought Severity Index (MDSI) anomaly during the growing season from May–August for the years 2000–2013 was acquired. Fourteen-year summer monthly data for air temperature, precipitation and soil moisture content of in situ measurements from sixteen meteorological stations for four various land use areas were analyzed. We also calculated the percentage deviation of climatological variables at the sixteen stations to compare to the MDSI anomaly. Both comparisons of satellite-derived and observed anomalies and variations were analyzed by using the existing common statistical methods. The results demonstrated that the air temperature anomaly (T anomaly) and the precipitation anomaly (P anomaly) were negatively (correlation coefficient r = −0.66) and positively (r = 0.81) correlated with the MDSI anomaly, respectively. The MDSI anomaly distributions revealed that the wettest area occupied 57% of the study area in 2003, while the driest (drought) area occurred over 54% of the total area in 2007. The results also showed very similar variations between the MDSI and T anomalies. The highest (wettest) MDSI anomaly indicated the lowest T anomaly, such as in the year 2003, while the lowest (driest) MDSI anomaly had the highest T anomaly in 2007. By comparing the MDSI anomaly and soil moisture content at a 10-cm depth during the study period, it is found that their correlation coefficient is 0.74. Full article

(This article belongs to the Special Issue Earth Observations for a Better Future Earth)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Earth Observations for a Better Future Earth

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (14 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI