Changes in the glaciers of Chandra–Bhaga basin,Himachal Himalaya,India, between 1980 and 2010 measured using remote sensing

This study reports the glacier changes of Chandra–Bhaga basin, northwest Himalaya, India, from 1980 to 2010. Satellite remote-sensing data from the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM), the Linear Imaging Self Scanning Sensor (LISS) and Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing (IRS) series, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) were used to study the changes in glacier parameters such as glacier area, length, snout elevation, and the impact of glacier topographical parameters (glacier slope, aspect, and altitude range) on the glacier changes. It was found that the total glaciated area had shrunk to 368.2 km² in 2010 from 377.6 km² in 1980, a loss of 2.5%. The average position of glacier terminuses retreated by 465.5 ± 169.1 m from 1980 to 2010 with an average rate of 15.5 ± 5.6 m year^?1. The decadal scale analysis showed that the average rate of retreat had increased the most in the recent decade. A moraine-dammed lake located in the study region was found to have expanded in area from (0.65 ± 0.01) km² in 1980 to (1.26 ± 0.03) km² in 2010. Glaciers with steep slope and less altitude range have lost more area than the glaciers having gentle slope and greater altitude range.     

利用长时间序列Landsat分析博斯腾湖面积变化

目的 近年来博斯腾湖面积波动较大,影响当地经济发展,掌握博斯腾湖面积变化及其与气候变化的相关规律,以指导湖泊保护和可持续利用管理策略。方法 利用Landsat影像计算1988—2014年博斯腾湖面积,监测并分析湖水面积年际变化及空间变化趋势,探讨博斯腾湖流域年降水量、年均气温变化和人类活动对湖水面积的影响,并将监测结果与MODIS数据计算的2000—2014年湖水面积以及1987—2011年实测水位数据进行对比验证。结果 结果表明,以2002年为分界线,博斯腾湖面积变化分2个阶段:1)1988—2002年,湖水面积呈增加趋势,增加288.88 km²,增长了31.62%;2)2002—2014年呈减小趋势,减小281.56 km²,减少了23.42%。根据气候条件分布差异,将博斯腾湖流域分为山区和平原区,分析发现:1988—2002年,山区年降水量和气温上升,与湖水面积呈显著正相关;2002年后,山区年降水量相对下降,平原区气温升高,人类活动用水量增加。结论 湖水面积变化受流域气候与人类活动共同作用,1988—2002年主要受山区气候影响,2002年后湖水面积缩小可能是气温升高和人类活动用水量增加导致。     

Reconciled freshwater flux into the Godthåbsfjord system from satellite and airborne remote sensing

As the rapid reduction in ice volume of the Greenland ice sheet (GrIS) continues, increased melt water flux from the GrIS enters the deep Greenlandic fjords. This increased freshwater flux may change the salinity and eventually the ecology of the fjords. Here, we present a case study in which we, from various remote-sensing data sets, estimate the freshwater flux from the GrIS into a specific fjord system, the Godthåbsfjord, in southwest Greenland. The area of the GrIS draining into Godthåbsfjord covers approximately 36,700 km². The large areal extent and the multiple outlets from the GrIS hamper in situ observations. Here, we evaluate available data from remote sensing and find a drainage basin in rapid change. An analysis of data from the Gravity Recovery and Climate Experiment (GRACE) satellites shows a mean seasonal freshwater flux into Godthåbsfjord of 18.2 ± 1.2 Gt, in addition to an imbalance in the mass balance of the drainage basin from 2003 to 2013 of 14.4 ± 0.2 Gt year^?1. Altimetry data from air and spaceborne missions also suggest rapid changes in the outlet glacier dynamics. We find that only applying data from the Ice, Cloud, and land Elevation Satellite (ICESat) mission the mass change of the Godthåbsfjord drainage basin is significantly underestimated. When including additional laser-altimetry surveys, to account for changes in the outlet glaciers elevation, not captured by ICESat, the altimetry data were able to reconcile the basin mass balance with the gravimetric estimate and provide a higher spatial resolution of the mass changes.     

Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security,determined using MODIS 250 m time-series data and spectral matching techniques,in the Krishna River basin (India)

The objective of this study was to investigate the changes in cropland areas as a result of water availability using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series data and spectral matching techniques (SMTs). The study was conducted in the Krishna River basin in India, a very large river basin with an area of 265 752 km² (26 575 200 ha), comparing a water-surplus year (2000–2001) and a water-deficit year (2002–2003). The MODIS 250 m time-series data and SMTs were found ideal for agricultural cropland change detection over large areas and provided fuzzy classification accuracies of 61–100% for various land‐use classes and 61–81% for the rain-fed and irrigated classes. The most mixing change occurred between rain-fed cropland areas and informally irrigated (e.g. groundwater and small reservoir) areas. Hence separation of these two classes was the most difficult. The MODIS 250 m-derived irrigated cropland areas for the districts were highly correlated with the Indian Bureau of Statistics data, with R ²-values between 0.82 and 0.86.

The change in the net area irrigated was modest, with an irrigated area of 8 669 881 ha during the water-surplus year, as compared with 7 718 900 ha during the water-deficit year. However, this is quite misleading as most of the major changes occurred in cropping intensity, such as changing from higher intensity to lower intensity (e.g. from double crop to single crop). The changes in cropping intensity of the agricultural cropland areas that took place in the water-deficit year (2002–2003) when compared with the water-surplus year (2000–2001) in the Krishna basin were: (a) 1 078 564 ha changed from double crop to single crop, (b) 1 461 177 ha changed from continuous crop to single crop, (c) 704 172 ha changed from irrigated single crop to fallow and (d) 1 314 522 ha changed from minor irrigation (e.g. tanks, small reservoirs) to rain-fed. These are highly significant changes that will have strong impact on food security. Such changes may be expected all over the world in a changing climate.     

Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery

This study focuses on the statistical characterization of ice conditions (extent, sea ice occurrence probability (SIOP), and length of ice season) in the Gulf of Riga, Baltic Sea, using remote-sensing data. The optical remote-sensing data with 250 m resolution acquired by a Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2011 were used for statistical characterization of sea ice. A method based on bimodal histogram analysis of remote-sensing reflectance data was developed to discriminate ice from water. In general, ice extent information obtained from MODIS data agrees with the official ice chart data (synthetic aperture radar (SAR) and in situ measurements) and multi-sensor product containing data from microwave and infrared instruments (R² >0.83). However, in case of severe winters and extremely mild winters there are differences in the dates when maximum ice extent is registered. MODIS data can be used for detailed analysis of ice extent in specific basins of Baltic Sea. Depending on the year, the ice season length in the Gulf of Riga ranged from 68 to 146 days, and the maximum ice extent varied greatly from 329 to 15,350 km². SIOP and number of ice days increased significantly in areas where the depth is less than 15 m. Based on negative-degree days and ice cover characteristics (SIOP and ice season length), three winter scenarios were defined: severe (2003, 2006, 2010, and 2011), medium (2004 and 2005), and mild (2007, 2008, and 2009).     

Temporal and spatial variations of grassland desertification monitoring in Tibet of China

Tibet, the largest region of the Qinghai–Tibet Plateau, is undergoing extensive grassland deterioration and desertification due to both human and natural factors. Alpine meadow and grassland restoration is difficult after degradation; consequently, the desertification of the Tibetan grassland has attracted substantial social attention. This article considered Amdo, Baingoin, Coqên, and Zhongba counties in Tibet as the study areas, employed remote-sensing data, and developed Tibetan grassland desertification classification indices based on field surveys. Moreover, this study used spectral mixture analysis (SMA) methods to interpret remote-sensing image data from the study areas during three periods (1990, 2000, and 2009) and considered the bare sand (gravel) area proportion as the main basis for the evaluation of grassland desertification. The results of this study demonstrate that the slightly, moderately, and severely desertified grasslands of the monitoring zone covered a total area of 114,113.16 km² in 1990, accounting for 82.12% of the study area. The area exhibited no change in 2000 and decreased by 4472.31 km² in 2009. The severely desertified grassland area declined from 1990 to 2009. The degree of grassland desertification in these four Tibetan counties diminished from 1990 to 2009, and the grassland desertification area exhibited a gradual reduction during the same period. Regarding other soil coverage types, the ice and snow area markedly changed and declined to approximately one-third of its original extent during these 20 years, and most of the ice and snow area was converted to bare land and various types of desertified grassland.     

Parameterizing the aerodynamic roughness length on a regional scale based on multi-source remote-sensing data

Aerodynamic roughness length (z₀) is one of those important biophysical parameters that influence energy exchange at the land–atmosphere interface, so it is significant to quantify the z₀ accurately. In this article, a scheme parameterizing land-surface z₀ at regional scale has been approached based on multi-resource remote-sensing data, including lidar and optical remote sensing. First, we retrieved the regional vegetation height from lidar data of Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud, and land Elevation Satellite (ICESat), and then the z₀ values of vegetated land surface were calculated using height data and canopy area index retrieved from remote-sensing data. Finally, the wall-to-wall map of z₀ in January and July 2008 were developed. The conclusions are as follows. (1) The vertical and horizontal structures of vegetation can be retrieved combining spaceborne lidar data and other optical remote-sensing data, so the vegetation characteristics and their intra-annual diversification of different land surfaces can be presented dynamically. The variation of z₀ with vegetation phenology can be quantified by modelling with vegetation height and multi-temporal leaf area index from multi-resource remote-sensing data. (2) The z₀ values of vegetated surface change significantly during leaf-on or leaf-off period in the year, but there are different features in the sparsely or densely vegetated surface. In the sparse vegetation areas, due to the relatively low leaf density in leaf-off season, the value of z₀ is also low. With the increase of leaf density in leaf-on season, the z₀ values will also increase. However, the relationship is complicated in the dense vegetation areas in leaf-on season; the z₀ values may or may not increase, but the zero-plane displacement heights will keep increasing continuously. This operational scheme to parameterize z₀ based on the vegetation height and canopy area index retrieved from multi-source remote-sensing data can be applied to quantify time serial z₀ at regional scale. Besides, it can also improve z₀ parameterization in land models or atmospheric models.     

Change in area of Ebinur Lake during the 1998–2005 period

Ebinur Lake is located in a typical arid region in the north‐west of China. It is an area with the lowest elevation in the Junggar Basin in the Province of Xinjiang. Recent monitoring indicates that the lake surface area has increased. To obtain a continuous record of the change in lake area, a radiometric analysis of SPOT/VEGETATION (VGT) imagery was carried out based on methodology developed for regional lake area mapping. Two indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were selected to identify the water body of Ebinur Lake. The indices are calculated based on the spectral reflectances in the red and near infrared bands of VGT sensor. If the NDVI is less than a critical value (0) and if the NDWI is larger than a critical value (0), the pixel is flagged as a water body. Validation indicates that the methodology to identify water bodies based on multi‐spectral VGT data is applicable in our study area achieving an overall accuracy of 91.4%. Independent monitoring results elicit that the lake surface area was at its lowest in 1998. The yearly average surface area is about 503 km². The lake area increased to 603 km² during 1999. In the period 1999–2001 the area changes are marginal. A large area increase occurred from 2001 to 2002 till the lake area reached a surface area of 791 km². The lake area peaks to 903 km² in 2003 and subsequently decreased to areas of 847 km² in 2004 and 746 km² in 2005. Similar area change dynamics are observed when applying the remote sensing based technique. Seasonally, the typical dynamics elicit a larger surface area in spring and winter and a smaller one during summer.     

The application of a binary division procedure to the classification of forest subcategories using MODIS time-series data during 2000–2010 in China

Forests account for more than 23% of China’s total area. As the most important terrestrial ecosystem, forests have tremendous ecological value. However, it remains difficult to classify forest subcategories at the national scale. In this study, a newly developed binary division procedure was used to categorize forest areas, including their spatiotemporal dynamics, during the period 2000–2010. Time-series images acquired using the Moderate Resolution Imaging Spectroradiometer (MODIS), together with auxiliary data on land use, climate zoning, and topography, were utilized. Hierarchical classification and zoning were combined with remote-sensing auto-classification. Based on the forest extent mask, the state-level forest system was divided into four classes and 18 subcategories. The method achieved an acceptable overall accuracy of 73.1%, based on a comparison to the sample points of China’s fourth forest general survey data set. In 2010, the total forest area was 1.755 × 10⁶ km², and the total area of and shrubs was 4.885 × 10⁵ km². The total area of woodland increased by 2536.25 km² during the decade 2000–2010. The shrub subcategories exhibited almost no change during this time period; however, significant changes in forest area occurred in the mountainous region of Northeast China as well as in the hilly regions of Southern China. The main transformations took place in cold-temperate and temperate mountainous deciduous coniferous forest, subtropical deciduous coniferous forest, subtropical evergreen coniferous forest, and temperate and subtropical deciduous broadleaved mixed forests. The binary division procedure proposed herein can be used not only to rapidly classify more forest subcategories and monitor their dynamic changes, but also to improve the classification accuracy compared with global and national land-cover maps.     

Assessment of forest damage caused by an ice storm using multi-temporal remote-sensing images: a case study from Guangdong Province

In early 2008, forest ecosystems in southern China suffered damage due to a severe ice storm disaster. The area and degree of forest damage caused by the ice storm was assessed using Satellite Pour l’Observation de la Terre (SPOT)-Vegetation images for Guangdong Province acquired between 1999 and 2008. By using the maximum value composition method and image thresholding techniques, the forest vegetation loss, expressed as the change in net primary productivity (NPP) and two indicators (I₁, I₂), was estimated. The damage threshold was determined by comparing the standard deviation of pixels of the undamaged areas in 2008 and other years without any disaster, which was 10%. The area of damaged forest vegetation was 47,670 km², with the northern Guangdong Province most seriously affected. The total loss of NPP for forest vegetation was 50,578,055 t (DW) year^?1, with 52 counties (43.7%) suffering forest vegetation damage. Evergreen coniferous forest was most widely affected, but evergreen broad-leaved forest was the most severely damaged vegetation type. Terrain topography influenced the damage to forest vegetation, which was found to increase with increasing elevation and slope gradient. The range and degree of damaged forest determined by remote-sensing data is consistent with the extent of the ice storm, indicating that this study provides a new approach for rapid assessment of forest disasters at a regional scale.     

Modelling and testing spatially distributed sediment budgets to relate erosion processes to sediment yields

Identifying the erosion processes contributing to increased basin fine sediment yield is important for reducing downstream impacts on aquatic ecosystems. However, erosion rates are spatially variable, and much eroded sediment is stored within river basins and not delivered downstream. A spatially distributed sediment budget model is described that assesses the primary sources (hillslope soil erosion, gully and riverbank erosion) and sinks (floodplain and reservoir deposition) of fine sediment for each link in a river network. The model performance is evaluated in a 17,000-km² basin in south-east Australia using measured suspended sediment yields from eight catchments within the basin, each 100–700 km² in area. Spatial variations within the basin in yield and area-specific yield were reliably predicted. Observed yields and area-specific yields varied by 17-fold and 15-fold respectively between the catchments, while predictions were generally within a factor of 2 of observations. Model efficiency at predicting variations in area-specific yield was good outside forested areas (0.58), and performance was weakly sensitive to parameter values. Yields from forested areas were under-predicted, and reducing the predicted influence of riparian vegetation on bank erosion improved model performance in those areas. The model provided more accurate and higher resolution predictions than catchment area interpolation of measured yields from neighbouring river basins. The model is suitable for guiding the targeting of remediation measures within river basins to reduce downstream sediment yields.     

Remotely sensing the ecological influences of ditches in Zoige Peatland,eastern Tibetan Plateau

Zoige Peatland in the eastern Tibetan Plateau, the largest alpine peatland in China, was widely ditched in 1970s for pasture expansion. The ditching is believed to have caused peatland degradation, but there is still no widespread agreement on this due to the absence of essential regional and temporal information about ditch drainage. Therefore, this study used both remote-sensing observations and field surveys to examine the ecological influences of ditching for this alpine peatland. In the study, ditch distribution was interpreted with remote-sensing imagery and the ecological responses were investigated with temporal observation by Moderate Resolution Imaging Spectroradiometer (MODIS) and field surveys. The results showed that there were ~1200 km ditches interpreted, mainly in three spatial patterns depending on hydro-geomorphologies. The MODIS enhanced vegetation index (EVI) was more sensitive to peatland surface water depth (R² = 0.678, P < 0.001) than the normalized difference water index (NDWI) (R² = 0.583, P < 0.001), because the latter would become saturated at a certain surface water depth (~50 cm in Zoige). The temporal MODIS imagery reflected the ecological responses of ditched peatland to drainage in terms of vegetation density and water conditions. This study indicated that ditching depressed the surface water depth of the Zoige Peatland in summer, but not to the extent of completely transforming peatland into steppe due to the recharging of local beneficial hydro-geomorphologies. The MODIS indices investigated in the study could be used to monitor the annual regional status of vegetation cover and surface water for Zoige peatland.     

Spatio-temporal distribution of burned areas by ecoregions in Mexico and Central America

Fire activity in Mexico and Central America, and its associated emissions, has impacts across multiple scales. On the local-to-regional scale, fire activity impacts land use, productivity, and biodiversity. On the regional-to-global scale, fire activity impacts hydrological, biogeochemical, and atmospheric processes. A consistent, reliable, large-scale characterization of the spatial and temporal distribution of fire burned area is required to assess its environmental impacts and to support natural resources’ management. The spatial and temporal distributions of fire burned areas in ecoregions of Mexico and Central America are evaluated in this study for the period 2001–2014, using the satellite Moderate Resolution Imaging Spectroradiometer (MODIS) MCD45 Burned Area data set. The methodology combines the 500 m burned area product with a MODIS land cover product and a map of North American land cover to calculate the spatiotemporal variability of fire activity as a function of land-use type.

The total burned area over Mexico and Central America over the period 2001–2014 was found to be 614,243.5 km², but with significant interannual variability over the 14 years included in the study. Indeed, the minimum burned area over the period was 9892.25 km² in 2014 and the maximum was 37,669.50 km² in 2011, a fourfold increase. Burned areas were found to be concentrated in northern Mexico and on the Pacific coast, mainly from October to June. Agricultural burned areas accounted for 37% and 43% of total detected burns in Mexico and Central America, respectively. The largest extent of burned surface occurs in May for most land-cover types. The maximum density of burned areas occurred in the tropical dry forests ecoregion during the dry season. Both in Mexico and Central America, burned area anomalies have significant anti-correlation with precipitation anomalies.     

Quantitative detection of change in grass cover from multi-temporal TM satellite data

In this paper, change in grassland cover near Lake Qinghai, west China was quantitatively detected from satellite remote-sensing data. Two Thematic Mapper images recorded in 1987 and 2000 were radiometrically corrected and used to derive the Normalized Difference Vegetation Index (NDVI). The NDVI image in 2000, after standardization via in situ measured spectra, was converted to a map of grass cover with the aid of in situ grass-cover samples. Another map was produced from the 1987 image after it was radiometrically benchmarked to the 2000 image using the calibration to like-values method. Comparison of these two maps revealed that a total of 36.28 km² of grassland had a higher cover, versus 44.72 km² that experienced grassland degradation in the study area. The absolute cover changed by a net value of??1.27%. The magnitude of change is related inversely to the value of the cover. The large majority of the area (82.6%), however, had a small change that was within ±20%. With this proposed method, it is possible to quantify changes in grassland cover from multi-temporal satellite data if one set of ground samples are concurrently collected with one of the satellite images.     

Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 10¹² g) over a total area of 66.01 × 10⁴ km² between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R² = 0.05, p > 0.05). (2) The mean AGB density was 28.9 g C m^?2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R² = 0.41, p < 0.001), while temperature seemed to be a minor factor (R² = 0.14, p < 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R² = 0.45, p < 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia.     

Frontal changes in the Manimahesh and Tal Glaciers in the Ravi basin,Himachal Pradesh,northwestern Himalaya (India), between 1971 and 2013

The Manimahesh and Tal Glaciers are located in the Budhil fifth-order sub-basin of the Ravi, Himachal Himalaya, Northwestern Himalaya (India). These glaciers were analysed using high- (Corona KH-4A) to medium- (Landsat TM/ETM+/OLI, ASTER) spatial resolution satellite data between 1971 and 2013, along with extensive field measurements (2011–2014) of frontal changes. The results show that the Manimahesh and Tal Glaciers retreated by 157 ± 34 m (4 ± 1 m year^–1) and 45 ± 34 m (1 ± 1 m year^–1), respectively, whereas, the total area lost is estimated at 0.21 ± 0.01 km² (0.005 km² year^–1) and 0.010 ± 0.003 km² (0.0002 km² year^–1), respectively, between 1971 and 2013. The rate of retreat is significantly lower than that previously reported. Our field measurements (2011–2014) also suggest a retreating trend and validate the measured glacier changes using remotely sensed temporal data.     

Landscape changes of the Ejin Delta in the Heihe River Basin in Northwest China from 1930 to 2010

The Heihe River Basin is located in the arid and semi-arid region of Northwest China; during the past 80 years, this basin has experienced water resource competition between irrigation agriculture and ecological demand in its middle and lower reaches, respectively. The land cover of the Ejin Delta in the lower reaches of the Heihe River Basin was interpreted and analysed for four different periods using a map created by Dr Sven Hedin in the 1930s, Corona satellite images taken in 1961, and Landsat Thematic Mapper (TM) images taken in 2000 and 2010. Overall, the results show that (1) the coarse resolution of the 1930s map increased the uncertainty of analysis in the study area and (2) the river area in the Ejin Delta decreased by 91.0% from the 1930s to 2000. In addition, two major terminal lakes, Gaxun Nuur Lake and Sogo Nuur Lake, dried up in 1961 and 1992, respectively, and the area of Populus euphratica decreased by 76.1% from the 1930s to 2000. Most reeds were overtaken by shrubs between the 1930s and 1961, which caused the area of reeds to decrease from 3481 to 1332 km² and the area of shrubs to increase from 805 to 2795 km². From the 1930s to 2000, the desert and alkaline land areas increased by 42.2% and 52.4%, respectively. (3) After the water transfer project was implemented in 2000, the area of Sogo Nuur Lake recovered to 40.58 km² by 2010. The areas of Populus euphratica, shrubland, and reedland showed a recovering trend, with increases of 4.5%, 6.5%, and 43.5%, respectively, by 2010. The desert and alkaline land areas decreased by 4.2% and 15.2%, respectively, by 2010. The area of cultivated land increased from 25 km² in 1961 to 85 km² in 2000 and rapidly approached 160 km² in 2010. These changes over time indicated that the ecological habitat in the Ejin Delta deteriorated between the 1930s and 2000. However, the water transfer project effectively changed the degradation trend.     

Remote-sensing and GIS-based landslide-susceptibility zonation using the landslide index method in Igo River Basin,Eastern Himalaya,India

A remote-sensing and geographical information sysytem (GIS)-based quantitative methodology for landslide-susceptibility zonation is described in a stepwise manner with its application in the Igo River Basin in the West Siang District of Arunachal Pradesh, Eastern Himalaya, India. Parameters such as geology, physiography, slope angle, slope length, slope aspect, slope type, generic landforms, lineament distance, road distance, drainage distance, altitudinal zones and land cover are used for landslide-susceptibility zonation. The quantitative relation between landslides and the selected parameters is established through the landslide index method of the International Institute for Geo-Information Science and Earth Observation (ITC), The Netherlands, by assigning weights. A weight value for a certain parameter class is defined as the natural logarithm of the landslide density in the class divided by the landslide density in the entire map. The final layer containing the composite index is divided into seven landslide-susceptibility categories. The maximum portion of the study area experiences moderately low to moderate landslide susceptibility, and each portion occupies an area of 91 km², representing 30% of the total area. High concentrations of very high and extremely high-susceptibility landslide areas are noticed in the steep slope areas, especially in the Sub-Himalayas. The settlements are found in the safe areas of very low, low and moderately low landslide-susceptibility categories. About 9% and 1.99% of the roads are exposed to high and very high landslide-susceptibility areas, respectively. About 15% of the slash-and-burn cultivation (jhum) is found along the high-susceptibility areas, 3.89% is found in very high-susceptibility areas and 0.19% is prone to extremely high susceptibility. The high-susceptibility zones are also found under dense and moderately dense forests.     

Modelling yearly flooding extent of the Waza-Logone floodplain in northern Cameroon based on MODIS and rainfall data

The Sahelian floodplains are of high ecological and economical importance, providing water and fresh pasture in the dry season. A spatial model is presented to predict the yearly flooding extent of the Waza-Logone floodplain based on cumulative runoff in the catchment area and estimations of the soil moisture prior to the flooding. Observations of flooding extent were based on thresholding 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) shortwave infrared (SWIR) images. The Soil Conservation Service Curve Number (SCS-CN) method was used to calculate cumulative runoff within the Logone catchment area based on rainfall estimates (RFEs) for Africa. MODIS SWIR images acquired prior to the flooding were used as indicators for soil moisture. The mean observed flooding extent of the Waza-Logone floodplain during the period 2000–2005 was 6747 km² with a standard deviation of 1838 km². Multiple regression analysis was performed to create a predictive model forecasting flooding extent 1.5 months in advance with a coefficient of determination (R ²) equal to 0.957. Multiple regression modelling was also performed for three subregions separately. The 1.5-month forecast model for the Waza subregion resulted in the highest accuracy (R ²?=?0.950). A floodwater distribution map was created for this subregion model, allowing determination where the flooding occurs for an estimated flood size. The average additional error caused by the mapping procedure was 138 km², which is relatively small compared to an average flooded area of 3211 km² for the Waza subregion. As the flooding extent in the Waza-Logone floodplain is highly correlated to the amount of natural resources available in the dry season, the model may be a valuable tool for sustainable management of these resources.     

Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods

Lake area derived from remote-sensing data is a primary data source, because changes in lake number and area are sensitive indicators of climate change. These indicators are especially useful when the climate change is not convoluted with a signal from direct anthropogenic activities. The data used for lake-area mapping is important, to avoid introducing unnecessary uncertainty into long-term trends of lake-area estimates. The methods for identifying waterbodies from satellite data are closely linked to the quality and efficiency of surface-water differentiation. However, few studies have comprehensively considered the factors affecting the selection of data and methods for mapping lake area in the Tibetan Plateau (TP), nor of evaluating their consequences. This study tests the dominant data sets (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data) and the methods for automated waterbody mapping on 14 large lakes (>500 km²) distributed across different climate zones of the TP. Seasonal changes in lake area and data availability from Landsat imagery are evaluated. Data obtained in October is optimal because in this month the lake area is relatively stable. The data window can be extended to September and November if insufficient data is available in October. Grouping data into three-year bins decreases the effects of year-to-year seasonal variability and provides a long-term trend that is suitable for time series analysis. The Landsat data (Multispectral Scanner, MSS; Thematic Mapper, TM; Enhanced Thematic Mapper Plus, ETM+; and Operational Land Imager, OLI) and MODIS data (MOD09A1) showed good performance for lake-area mapping. The Otsu method is used to determine the optimal threshold for distinguishing water from non-water features. Several water extraction indices, namely NDWI_McFeeters, NDWI_Xu, and AWEI_non-shadow, yielded high overall classification accuracy (92%), kappa coefficient (0.83), and user’s accuracy (~90%) for lake-water classification using Landsat data. The MODIS data using NDWI_McFeeters and NDWI_Xu showed consistent lake area (r² = 0.99) compared with Landsat data on the corresponding date with root mean square error (RMSE) values of 86.87 and 103.33 km² and mean absolute error (MAE) values of 25.7 and 29.04 km², respectively. The MODIS data is suitable for great lake mapping, which is the case for the large lakes in the TP. Although automated water extraction indices exhibited high accuracy in separating water from non-water, visual examination and manual editing are still necessary. Combined with recent Chinese high-resolution satellites, these remotely sensed imageries will provide a wealth of data for studies of lake dynamics and long-term lake evolution in the TP.