A GIS for hydrological modelling in the semi-arid, HAPEX-Sahel experiment area of Niger, Africa

During the HAPEX-Sahel experiment (1991–94), water redistribution processes were studied at the meso-scale (10 000 km²) near Niamey, Niger. A project now under way at ORSTOM aims at modelling the regional water balance through a spatial approach combining GIS data organization and distributed hydrological modelling. The main objective is to extend the surface water balance, by now available only on a few, small (around 1 km²) unconnected endoreic catchments, to a more significant part of the HAPEX-Sahel square degree, a 1500 km² region called SSZ that includes most of the environmental and hydrology measurement sites. GIS architecture and model design consistently consider data and processes at the local, catchment scale, and at the regional scale. The GIS includes spatial and temporal hydrological data (rainfall, surface runoff, ground water), thematic maps (topography, soil, geomorphology, vegetation) and multi-temporal remote sensing data (SPOT, aerial pictures). The GIS supports the simulation of the composite effect at the regional scale of highly variable and discontinuous component hydrologic processes operating at the catchment scale, in order to simulate inter-annual aquifer recharge and response to climatic scenarios at the regional scale.     

Study on estimating the evapotranspiration cover coefficient for stream flow simulation through remote sensing techniques

This study focuses on using remote sensing techniques to estimate the evapotranspiration cover coefficient (CV) which is an important parameter for stream flow. The objective is to derive more accurate stream flow from the estimated CV. The study area is located in the Dan-Shuei watershed in northern Taiwan. The processes include the land-use classification using hybrid classification and four Landsat-5 TM images; the CV estimations based on remote sensing and traditional approaches; comparison of stream flow simulation according to the above two CV values. The result indicated that the study area was classified into seven land-use types with 88.3% classification accuracy. The simulated stream flow using remote sensing approach could represent more accurate hydrological characteristics than a traditional approach. Obviously integrating remote sensing technique and the SEBAL model is a useful approach to estimate the CV. The CV parameter estimated by remote sensing technique did improve the accuracy of the stream flow simulation. Therefore, the results can be extended to further studies such as forest water management.     

Semi-automatic mapping of anthropogenic impervious surfaces in an urban/suburban area using Landsat 8 satellite data

Impervious surfaces have a significant impact on urban runoff, groundwater, base flow, water quality, and climate. Increase in Anthropogenic Impervious Surfaces (AIS) for a region is a true representation of urban expansion. Monitoring of AIS in an urban region is helpful for better urban planning and resource management. Cost effective and efficient maps of AIS can be obtained for larger areas using remote sensing techniques. In the present study, extraction of AIS has been carried out using Double window Flexible Pace Search (DFPS) from a new index named as Normalized Difference Impervious Surface Index (NDAISI). NDAISI is developed by enhancing Biophysical Composition Index (BCI) in two stages using a new Modified Normalized Difference Soil Index (MNDSI). MNDSI has been developed from Band 7 and Band 8 (PAN) of Landsat 8 data. In comparison to existing impervious surface extraction methods, the new NDAISI approach is able to improve Spectral Discrimination Index (SDI) for bare soil and AIS significantly. Overall accuracy of mapping of AIS, using NDAISI approach has been found to be increased by nearly 23% when compared with existing impervious surface extraction methods.     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

Application of satellite — based rainfall products and SRTM DEM in hydrological modelling of Brahmaputra basin

Hydrological modelling of large river catchments is a challenging task for water resources engineers due to its complexity in collecting and handling of both spatial and non-spatial data such as rainfall, gauge discharges, and topographic parameters. In this paper an attempt has been made to use satellite-based rainfall products such as Climatic Prediction Centre (CPC)-National Oceanic and Atmospheric Administration (NOAA) data for hydrological modelling of larger catchment where the limited field rainfall data is available. Digital Elevation Models (DEM) such as Global DEM (1 km resolution) and Shuttle Radar Topography Mission (SRTM) 3-arc second (90 m resolution) DEM have been used to extract topographic parameters of the basin for hydrological modelling of the study area. Various popular distributed models have been used in this study for computing excess rainfall, direct runoff from each sub-basin, and flow routing to the main outlet. The Brahmaputra basin, which is very complex both hydraulically and hydrologically due to its shape, size, and geographical location, has been examined as study area in this study. A landuse map derived from the satellite remote sensing data in conjunction with DEM and soil textural maps have been used to derive various basin and channel characteristics such as each sub-basin and channel slope, roughness coefficients, lag-time. Percentage of residual flows computed between observed flows and simulated flows using Global and SRTM DEMs are discussed. It is found that the topographic parameters computed using SRTM DEM could improve the model accuracy in computing flood hydrograph. Need of using better resolution satellite data products and the use of high-density field discharge observations is discussed.     

Urban land use mapping: A case study of ahmedabad city and its environs

Proper urban planning and effective implementation requires reliable urban land use statistics. In this context, satellite remote sensing data has been studied using both visual and digital techniques. A portable eight-band radiometer has been used to collect spectral signatures of surface features present in Ahmedabad city and its environs. Using these signatures a suitable approach employing visual and digital techniques has been developed for urban land use/sprawl mapping. Urban land-use maps of Ahmedabad city and its environs were prepared on 1:25,000 scale and for Ahmedabad Urban Development Authority Area on 1:50,000 scale using this methodology. It has been found that edge-enhancement techniques are useful to enhance the contrast among different urban land uses. Classification techniques such as MXL and Bayes classifiers are not successful in discriminating urban land uses. Tonal characteristics alongwith other elements of interpretation are required to classify urban land uses such as residential, industrial etc. Spatial distribution of various urban and uses and the space devoted to each urban land use has been brought out.     

Impact of Climate Change on Runoff of the Major River Basins of India Using Global Circulation Model (HadCM3) Projected Data

The effects of climate change on hydrological regimes have become a priority area for water and catchment management strategies. The terrestrial hydrology driven by monsoon rainfall plays a crucial role in shaping the agriculture, surface and ground water scenario in India. Thus, it is imperative to assess the impact of the changing climatic scenario projected under various climate change scenario towards the hydrological aspects for India. Runoff is one of the key parameters used as an indicator of hydrological process. A study was taken up to analyse the climate change impact on the runoff of river basins of India. The global circulation model output of Hadley centre (HADCM3) projected climate change data was used. Scenario for 2080 (A2 scenario indicating more industrial growth) was selected. The runoff was modeled using the curve number method in spatial domain using satellite derived current landuse/cover map. The derived runoff was compared with the runoff using normal climatic data (1951–1980). The results showed that there is a decline in the future climatic runoff in most of the river basins of India compared to normal climatic runoff. However, significant reduction was observed for the river basins in the eastern region viz: lower part of Ganga, Bahamani-Baitrani, Subarnrekha and upper parts of the Mahanadi. The mean projected runoff reduction during monsoon season (June–September) were 18 Billion Cubic Meter (BCM), 3.2 BCM, 3.5 BCM and 5.9 BCM for Brahmaputra-Barak Subarnrekha, Subarnarekha and Brahmini-Baitrani basin, respectively in comparison to normal climatic runoff. Overall reduction in seasonal runoff was high for Subarnrekha basin (54.1%). Rainfall to runoff conversion was high for Brahmaputra-Barak basin (72%), whereas coefficient of variation for runoff was more for Mahanadi basin (1.88) considering the monsoon season. Study indicates that eastern India agriculture may be affected due to shortage of surface water availability.     

土地利用类型变化对环境影响的遥感信息分析

利用多时相、多分辨率的Ouickbird和SPOT图像进行土地利用变化的监测,对遥感图像上的信息进行分析,利用遥感图像进行土地利用变化信息的自动发现,结合野外调查,分析了土地利用变化对周围环境的积极效应和负面影响。实践表明,对遥感图像的信息分析结合野外调查对于发现土地利用变化对环境的影响是一种有效的方法。     

An artificial neural network based approach for urban growth zonation in Dehradun city,India

For regulating urban growth, it is imperative to produce urban growth zonation maps, in which future urbanizable areas along with their urban growth potential are delineated. As, these maps provide a rational and scientific basis for taking future decisions regarding the growth of the city. The conventional approach for generating urban growth zonation maps is subjective in nature. To reduce this subjectivity, an artificial neural network (ANN) approach has been proposed for generating urban growth zonation maps. The database required for ANN-based urban growth zonation has been compiled from remote sensing data and other existing maps. GIS is used for handling of this spatial data. A comparison of the ANN- and conventional approach-derived zonation maps was also done. The study demonstrated the potential of ANN for urban growth zonation of an area, which may provide a valuable input to the urban planning authorities for regulating urban growth     

无锡市城镇用地变化及其环境效应研究

利用地形图和遥感影像提取无锡市城镇用地变化的空间信息,分析无锡市城镇用地变化的特征及其动力机制,探讨了无锡市城镇用地变化对生态环境带来的各种正负效应。     

Monitoring land-use change by combining participatory land-use maps with standard remote sensing techniques: Showcase from a remote forest catchment on Mindanao,Philippines

This paper combines participatory activities (PA) with remote sensing analysis into an integrated methodology to describe and explain land-cover changes. A remote watershed on Mindanao (Philippines) is used to showcase the approach, which hypothesizes that the accuracy of expert knowledge gained from remote sensing techniques can be further enhanced by inputs from vernacular knowledge when attempting to understand complex land mosaics and past land-use changes. Six participatory sessions based on focus-group discussions were conducted. These were enhanced by community-based land-use mapping, resulting in a final total of 21 participatory land-use maps (PLUMs) co-produced by a sample of stakeholders with different sociocultural and ecological perspectives. In parallel, seven satellite images (Landsat MSS, Landsat TM, Landsat ETM+, and SPOT4) were classified following standard techniques and provided snapshots for the years 1976, 1996, and 2010. Local knowledge and collective memory contributed to define and qualify relevant land-use classes. This also provided information about what had caused the land-use changes in the past. Results show that combining PA with remote-sensing analysis provides a unique understanding of land-cover change because the two methods complement and validate one another. Substantive qualitative information regarding the chronology of land-cover change was obtained in a short amount of time across an area poorly covered by scientific literature. The remote sensing techniques contributed to test and to quantify verbal reports of land-use and land-cover change by stakeholders. We conclude that the method is particularly relevant to data-poor areas or conflict zones where rapid reconnaissance work is the only available option. It provides a preliminary but accurate baseline for capturing land changes and for reporting their causes and consequences. A discussion of the main challenges encountered (i.e. how to combine different systems of knowledge), and options for further methodological improvements, are also provided.     

LAND-USE MAPPING FROM REMOTE SENSING DATA: A WORLD SUMMARY

A brief history of land-use mapping sets the stage for a survey of contemporary land-use mapping across the world, and more specifically, the applications of remote sensing methods in such mapping. Other issues addressed include the need to refine the definition of land use to encompass the totality of geographic space, to formally recognize the difference between large-scale maps of agricultural land-use and smaller-scale general land-use maps, and to investigate more closely the wisdom and desirability of a standardized world land-use classification. Four types of land-use maps are identified: diagnostic/informational, historical, dynamic, and predictive. Translated from: Vestnik Leningradskogo Universiteta, 1983, No. 24, pp. 52-61.     

Water Balance Study for a Basin Integrating Remote Sensing Data and GIS

Water balance of a basin involves estimation of input precipitation, runoff, infiltration and evapotranspiration (ET). Although ET may have large variations over a big basin, it is commonly estimated using a few point measurements and this makes the estimation error prone. Satellite based remote sensing data provides few parameters for estimation of energy fluxes, at the land surface and atmosphere interaction in a distributed manner using the meteorological parameters. These parameters through surface energy balance equation have been used for the estimation of ET in this study. Various spatially distributed variables required for ET estimation; viz. NDVI, surface albedo, surface temperature etc. have been derived using remote sensing and ancillary data for Tapi basin located in western India. Beside this field data such as rainfall, air temperature, relative humidity, sunshine hours etc. have been used. For computation of runoff, Soil Conservation Services (SCS) approach has been considered. Tapi basin up to Ukai dam has been selected as the study area. Satellite data from National Oceanic and Atmospheric Administration (NOAA), Polar Orbiting Environmental Satellite, which carries the Advanced Very High Resolution Radiometer (AVHRR), have been used for preparation of various maps required for runoff and ET analysis. The results of runoff and ET have been compared with observed data for 2 years, 2002–2003 and the results have been found in good agreement with observed data.     

基于光谱混合分解的流域不透水面提取及其动态分析——以于桥水库为例

不透水面不仅是城市非点源污染的主要来源,还是流域生态环境变化的主要因素之一。不透水面的数量、位置、几何形状、分布格局以及透水率与不透水率的比值,均影响着流域的水文环境,因此成为研究热点。本文以天津于桥水库流域为例,综合遥感(RS)与地理信息系统(GIS)技术,从流域尺度上研究1984~2013年间不透水面覆盖度的变化。在ENVI 5.1软件支持下,利用遥感影像获取1984,1994,2004和2013年4个时相的不透水面信息。采用修正后的归一化水体指数剔除水体信息,排除水体对不透水面提取精度的影响。运用线性光谱混合分析法(Linear Spectral Mixture Analysis,LSMA),提取流域不透水面覆盖度。结果表明:流域内不透水面覆盖度大多集中在1~5级,植被覆盖程度较高。近30年间不透水面比例逐年增加,2013年比1984年增加了2.802%,呈线性增长。中等分辨率的遥感影像适合流域尺度的不透水面提取的结果可作为流域水文及规划管理的重要基础性数据。     

利用Quick Bird全色遥感影像更新城市大比例尺地形图

利用现势的高分辨率遥感影像,经过精确的多项式变换正射几何纠正后,得到正射影像(DOM)与原有的数字线划图(DLG)叠加,经判读识别地物的变化,实现对原有地形图的快速更新.通过对地面控制点和检查点纠正的精度分析说明,利用Quick Bird遥感影像数据在地势较为平坦的城区更新1:2000比例尺的地形图是可行的.     

利用雷达干涉数据进行城市不透水层百分比估算

人工不透水层是城市地区的重要特征.作为城市生态环境的关键指数,不透水层百分比(Impervious Surfaces Percentage, ISP)常用于城市水文过程模拟、水质面源污染及城市专题制图等研究中.本文利用ERS-1/2 重复轨道雷达干涉数据,采用分类与回归树(CART)算法探究了雷达遥感在城市ISP估算中的可行性和潜力,并与SPOT5 HRG光学遥感图像的估算结果进行了分析比较.香港九龙港岛实验区的初步研究结果表明,雷达干涉数据在城市不透水层研究中具有一定的应用潜力,特别是裸土和稀疏植被的ISP估算结果要好于光学遥感,这主要得益于雷达干涉数据(特别是长时间相干图像)在人工建筑物和裸土或稀疏植被之间具有很强的区分能力,另外,雷达干涉数据和光学遥感数据间的融合能够提高ISP估算精度.     

遥感影像时—空融合的“点”—“线”—“面”质量评价

遥感影像时—空融合可集成多源数据高空间分辨率和高时间分辨率互补优势,生成时间连续的高空间分辨率影像,在遥感影像的动态监测与时序分析等方面具有重要应用价值.然而,现有多数研究往往基于单一数据产品对时—空融合算法进行评价,而在实际生产应用中,需要验证算法在多种遥感产品数据的融合表现;此外,目前研究大多基于“单点时刻”进行评...     

联合像元-深度-对象特征的遥感图像城市变化检测

特征空间的构建和优化对遥感图像识别能力的提高具有重要作用。针对面向对象方法对波段光谱信息利用不足,以及像元识别法无法充分利用图像空间几何等信息的问题,本文建立了新颖的联合像素级和对象级特征的航摄遥感图像城市变化检测方法。首先,充分利用像素级和对象级特征的优势,建立考虑光谱、指数、纹理、几何、表面高度及神经网络深度特征的特征空间;然后,引入LightGBM(light gradient boosting machine)算法对大量特征进行选择研究;最后,采用随机森林识别器对宜兴市2012年和2015年两期遥感图像进行识别,利用变化矩阵进行城市的变化检测。结果表明:联合像元、深度、对象特征和LightGBM特征选择算法的识别效果最好,平均的总体识别精度达到了88.50%,Kappa系数达到0.86,比基于像元、深度或对象特征的识别方法分别提高了10.50%、15.00%和4.00%;城市变化检测精度达到了87.50%。因此,本文方法是利用甚高分辨率航摄遥感图像进行城市变化的检测的有效方法。     

利用改进的CASA模型估算城市尺度NPP——以徐州城区为例

针对城市地物的特点,本文基于两种不同空间分辨率的遥感数据,利用原始与改进后的CASA估算了徐州城区的NPP,探讨了CASA模型的改进和遥感影像的空间分辨率对城市尺度NPP估算结果的影响.研究结果表明:①城市建筑用地对城市NPP的估算结果有较大的影响.改进的CASA模型将建筑用地的光合有效辐射(FPAR)归零,其估算值降...