Multi-criteria technique for mapping of debris-covered and clean-ice glaciers in the Shaksgam valley using Landsat TM and ASTER GDEM

Glaciers in the Shaksgam valley provide important fresh water resources to neighbourhood livelihood. Repeated creation of the glacier inventories is important to assess glacier–climate interactions and to predict future runoff from glacierized catchments. For this study, we applied a multi-criteria technique to map the glaciers of the Shaksgam valley of China, using Landsat Thematic Mapper(Landsat TM)(2009) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model version two(ASTER GDEM V2) data. The geomorphometric parameters slope, plan, and profile curvature were generated from ASTER GDEM. Then they were organized in similar surface groups using cluster analysis. For accurate mapping of supraglacial debris area, clustering results were combined with a thermal mask generated from the Landsat TM thermal band. The debris-free glaciers were identified using the band ratio(TM band 4/TM band 5) technique. Final vector maps of the glaciers were created using overlay tools in a geographic information system(GIS).Accuracy of the generated glacier outlines was assessed through comparison with glacier outlines based on the Second Chinese Glacier Inventory(SCGI) data and glacier outlines created from high-resolution Google Earth? images of 2009. Glacier areas derived using the proposed approach were 3% less than in the reference datasets. Furthermore, final glacier maps show satisfactory mapping results, but identification of the debris-cover glacier terminus(covered by thick debris layer) is still problematic. Therefore, manual editing was necessary to improve the final glacier maps.

Using GIS-Based Weighted Linear Combination Analysis and Remote Sensing Techniques to Select Optimum Solid Waste Disposal Sites within Mafraq City, Jordan

Landfill siting was determined within Mafraq City, Jordan, through the integration of geographic information system (GIS), weighted linear combination (WLC) analysis, and remote sensing techniques. Several parameters were collected from various sources in vector and raster GIS formats, and then, used within the GIS-based WLC analysis to select optimum solid waste disposal sites. Namely, urban areas, agricultural lands, access roads, surface aquifers, groundwater table, fault system, water wells, streams, and land slope were considered in this research. Also, the trend of urban expansion within the study area was monitored using the Landsat data of 1989, 1999, and 2009 to support the selection process of disposal sites. It is found that about 84% of the study area was within “most suitable” to “moderately suitable” classes for landfill sites, while the rest of the study area was within “poorly suitable” and “unsuitable” classes. Based on the analysis of Landsat satellite data the urban area was expanded of more than 240% during the last three decades, mainly toward south, and southwest, except the villages near the existing disposal site, where the trend was toward east and northeast. Finally, three sites were suggested as alternatives to the existing disposal site taking into the consideration the environmental, biophysical, and economical variables applied in the GIS-based WLC analysis.

A Study on Tropical Land Cover Classification Using ALOS PALSAR 50 m Ortho-Rectified Mosaic Data

The main objective of this study is to find better classifier of mapping tropical land covers using Synthetic Aperture Radar (SAR) imagery. The data used are Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50 m ortho-rectified mosaic data. Training data for forest, herbaceous, agriculture, urban and water body in the test area located in Kalimantan were collected. To achieve more accurate classification, a modified slope correction formula was created to calibrate the intensity distortions of SAR data. The accuracy of two classifiers called Sequential Minimal Optimization (SMO) and Random Forest (RF) were applied and compared in this study. We focused on object-based approach due to its capability of providing both spatial and spectral information. Optimal combination of features was selected from 32 sets of features based on layer value, texture and geometry. The overall accuracy of land cover classification using RF classifier and SMO classifier was 46.8% and 55.6% respectively, and that of forest and non-forest classification was 74.4% and 79.4% respectively. This indicates that RF classifier has better performance than SMO classifier.

Potential Hazard Map for Snow Disaster Prevention Using GIS-Based Weighted Linear Combination Analysis and Remote Sensing Techniques: A Case Study in Northern Xinjiang, China

Snow disaster is one of the top ten natural disasters worldwide. Almost every year, there will be snow disasters in north Xinjiang, northwestern China. Since the accumulated heavy snow in winter season will seriously threaten people’s lives, the main object of this study is to produce a potential hazard map for snow avalanche prevention. Taking three snow seasons from November to March of year 2008 to 2010, potential hazard areas were estimated, based on snow volume products and terrain features. Remote sensing (RS) techniques and geographical information system (GIS) based weighted linear combination (WLC) approach were applied, taking into consideration multiple criteria. Snow avalanche risks were analyzed using physical exposure and vulnerability indexes. The analysis indicates that: the areas at high-risk of avalanches are located in the north and south part of the counties of Altay, Bortala and Ili prefectures;the areas at medium-risk of avalanches are found in the certain part of Altay prefecture and Urumqi, Changji, Tacheng prefectures;the avalanche risk is generally low throughout the large area to the certain part of the study area and the region on the border of the eastern north Xinjiang. Overall, the risks of snow avalanche in Altay and Ili prefectures are higher than that other regions;those areas should be allocated correspondingly more salvage materials.

Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM

A regional map of mangrove forests was produced for six islands located in the southern part of Japan by integrating the spectral analyses of Landsat Enhanced Thematic Mapper plus (ETM+) images with a digital elevation model (DEM). Several attempts were applied to propose a reliable method, which can be used to map the distribution of mangrove forests at a regional scale. The methodology used in this study comprised of obtaining the difference between Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), band ratio 5/4, and band 5, from Landsat ETM+, and integrating them with the topographic information. The integration of spectral analyses with topographic data has clearly separated the mangrove forests from other vegetation. An accuracy assessment was carried out in order to check the accuracy of the results. High overall accuracy ranging from 89.3% to 93.6% was achieved, which increased the opportunity to use this methodology in other countries rich in mangrove forests.

Production of global land cover data-GLCNMO

Global land cover is one of the fundamental contents of Digital Earth.The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset
Global Land Cover by National Mapping Organizations.It has 20 land cover classes defined using the Land Cover Classification System.Of them,14 classes were derived using supervised classification.The remaining six were classified independently:urban,tree open,mangrove,wetland,snow/ice,andwater.Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003.Training data for supervised classification were collected using Landsat images,MODIS NDVI seasonal change patterns,Google Earth,Virtual Earth,existing regional maps,and expert’s comments.The overall accuracy is 76.5%and the overall accuracy with the weight of the mapped area coverage is 81.2%.The data are available from the Global Mapping project website(http://www.iscgm.org/).TheMODISdata used,land cover training data,and a list of existing regional maps are also available from the CEReS website.This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.

Analysis of the land suitability for paddy fields in Tanzania using a GIS-based analytical hierarchy process

The importance of irrigation development is considered a key factor for food security and poverty reduction because it improves crop productivity,and ensures stable expansion of agricultural production.However,irrigation development requires understanding of the avail-able resources including the suitability of the land for agriculture.In this study,the land suitability for paddy fields was evaluated within the United Republic of Tanzania mainland by integrating the geographic information system(GIS)and analytical hierarchy process(AHP).In this study,11 criteria based on various sources(soil type,soil drainage,soil organic carbon,soil pH,soil depth,elevation,slope,land use,topographic wetness index,temperature,and precipitation)were used.These criteria were used within the GIS-based AHP to identify the most suitable land for sustainable paddy field cultivation considering the preservation of the natural environment of forests and protected areas by examining two scenarios:rainfed condition and irrigation priority.The former ten criteria were assumed to be constant in both scenarios and were assigned the same scores,while the latter criterion(precipitation)was assigned different scores for varying amounts to plan new irrigation projects.Unsuitable land represents 72.8%of the study area,reducing the potential agriculture land(PAL)appropriate for cultivation to 27.2%.In the rainfed condition scenario,the very high and high suitability classes represent 17.6%of the total land of the study area and 64.7%of the PAL.In the irrigation priority scenario,the same classes represent 21.4%of the total land of the study area and 78.6%of the PAL.Finally,the distribution of the land suitability for both scenarios was analyzed within eight administrative irrigation zones to determine the irrigation zone with the greatest potential for paddy field cultivation.

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales.However,the rapid growth of urban areas exerts effects on climate,biodiversity,hydrology,and natural ecosystems worldwide.Therefore,regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local,regional,and potentially global scales.This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data,including Moderate Resolution Imaging Spectroradiometer(MODIS)data of 2013,population density of 2012,the Defense Meteorological Satellite Program’s Operational Linescan System(DMSP-OLS)nighttime lights of 2012,and constructed Impervious Surface Area(ISA)data of 2010.The Eurasian urban map was created using the threshold method for these data,combined with references of fine resolution Landsat and Google Earth imagery.The resultant map was compared with nine global urban maps and was validated using random sampling method.Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%.This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.