Integrated remote sensing and GIS study for hydrogeomorphological mapping and delineation of groundwater potential zones in Khallikote block,Ganjam district,Orissa

The present study attempts to delineate different groundwater potential units using remote sensing and geographic information system (GIS) in Khallikote block of Ganjam disrict, Orissa. Thematic maps of geology, geomorphology, land use and land cover, drainage density, lineament density, slope and DEM (digital elevation model) were prepared using the Landsat Thematic Mapper data in 3 spectral bands, band 7 (mid-infrared light), band 4 (near-infrared light), Band 2 (visible green light). Relationship of each layer to the groundwater regime has been evaluated through detailed analysis of the individual hydrological parameters. The SMCE (Spatial Multi-Criteria Evaluation) module in ILWIS (Integrated Land and Water Information System) supports the decision-making process for evaluating the ground water potential zones in the area. The study shows that more than 70% of the block is covered by medium to excellent category having good ground water potential.     

利用遥感指数时间序列轨迹监测森林扰动

作为陆地生态系统的主体,森林的碳循环与碳蓄积对研究陆地生态系统起着重要作用,但目前森林扰动资料的缺乏在很大程度上影响着森林碳通量的估算精度。利用1986年-2011年共14期的Landsat TM/ ETM+影像,以江西武宁县为例,使用遥感指数时间序列轨迹分析方法,研究了适用于中国南方森林的扰动监测技术,该技术不仅可以识别森林的扰动变化,同时还可以监测植被的恢复信息。精度分析表明该方法得出的扰动产品的Kappa系数达到0.80,总体精度达到89.7%,表明该方法对武宁县森林扰动具有较好的监测能力。森林扰动特征分析表明武宁县森林在20世纪90年代受扰动最为剧烈,并且扰动主要发生在低海拔地区。     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.     

Large area forest inventory using Landsat ETM+: A geostatistical approach

Large area forest inventory is important for understanding and managing forest resources and ecosystems. Remote sensing, the Global Positioning System (GPS), and geographic information systems (GIS) provide new opportunities for forest inventory. This paper develops a new systematic geostatistical approach for predicting forest parameters, using integrated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, GPS, and GIS. Forest parameters, such as basal area, height, health conditions, biomass, or carbon, can be incorporated as a response variable, and the geostatistical approach can be used to predict parameter values for uninventoried points. Using basal area as the response and Landsat ETM+ images of pine stands in Georgia as auxiliary data, this approach includes univariate kriging (ordinary kriging and universal kriging) and multivariable kriging (co-kriging and regression kriging). The combination of bands 4, 3, and 2, as well as the combination of bands 5, 4, and 3, normalized difference vegetation index (NDVI), and principal components (PCs) were used in this study with co-kriging and regression kriging. Validation based on 200 randomly sampling points withheld field inventory was computed to evaluate the kriging performance and demonstrated that band combination 543 performed better than band combination 432, NDVI, and PCs. Regression kriging resulted in the smallest errors and the highest R-squared indicating the best geostatistical method for spatial predictions of pine basal area.     

基于影像的Landsat TM/ETM+数据正规化技术

阐述了基于影像的LandsatTM／ETM^＋的数据正规化技术及其发展。该技术通过将Landsat影像的亮度值转换成传感器处的辐射值和反射率采对影像进行辐射校正。实例表明，使用正规化技术处理后的影像可以明显削弱日照和大气的影响，去除它们产生的噪声；其所书的传感器处的反射率与地面实测反射率的RMS值非常小。     

Downscaling of Land Surface Temperature Maps in the Texas High Plains with the TsHARP Method

High-resolution evapotranspiration (ET) maps can assist demand-based irrigation management. Development of high-resolution daily ET maps requires high-resolution land surface temperature (LST) images. Earth-observing satellite sensors such as the Landsat 5 Thematic Mapper (TM) and MODerate resolution Imaging Spectroradiometer (MODIS) provide thermal images that are coarser than simultaneously acquired visible and near-infrared images. In this study, we evaluated the TsHARP downscaling technique for its capability to downscale coarser LST images using finer resolution normalized difference vegetation index (NDVI) data. The TsHARP technique was implemented to downscale seven coarser scale (240, 360, 480, 600, 720, 840, and 960 m) synthetic images to a 120 m LST image. The TsHARP was also evaluated for downscaling a coarser 960 m LST image to 240 m to mimic MODIS datasets. Comparison between observed 120 m LST images and 120 m LST images downscaled from coarser 240, 360, 480, 600, 720, 840, and 960 m images yielded root mean square errors of 1.0, 1.3, 1.5, 1.6, 1.7, 1.8, and 1.9°C, respectively. This indicates that the TsHARP method can be used for downscaling coarser (960 m) MODIS-based LST images using finer Landsat (120 m) or MODIS (240 m)-derived NDVI images. However, the TsSHARP method should be evaluated further with real datasets before using it for an operational ET remote sensing program for irrigation scheduling purposes.     

Assessing the performance of a large-scale irrigation system by estimations of actual evapotranspiration obtained by Landsat satellite images resampled with cubic convolution

Remote sensing techniques allow monitoring the Earth surface and acquiring worthwhile information that can be used efficiently in agro-hydrological systems. Satellite images associated to computational models represent reliable resources to estimate actual evapotranspiration fluxes, ET_a, based on surface energy balance. The knowledge of ET_a and its spatial distribution is crucial for a broad range of applications at different scales, from fields to large irrigation districts. In single plots and/or in irrigation districts, linking water volumes delivered to the plots with the estimations of remote sensed ET_a can have a great potential to develop new cost-effective indicators of irrigation performance, as well as to increase water use efficiency. With the aim to assess the irrigation system performance and the opportunities to save irrigation water resources at the “SAT Llano Verde” district in Albacete, Castilla-La Mancha (Spain), the Surface Energy Balance Algorithm for Land (SEBAL) was applied on cloud-free Landsat 5 Thematic Mapper (TM) images, processed by cubic convolution resampling method, for three irrigation seasons (May to September 2006, 2007 and 2008). The model allowed quantifying instantaneous, daily, monthly and seasonal ET_a over the irrigation district. The comparison between monthly irrigation volumes distributed by each hydrant and the corresponding spatially averaged ET_a, obtained by assuming an overall efficiency of irrigation network equal to 85%, allowed the assessment of the irrigation system performance for the area served by each hydrant, as well as for the whole irrigation district. It was observed that in all the investigated years, irrigation volumes applied monthly by farmers resulted generally higher than the corresponding evapotranspiration fluxes retrieved by SEBAL, with the exception of May, in which abundant rainfall occurred. When considering the entire irrigation seasons, it was demonstrated that a considerable amount of water could have been saved in the district, respectively equal to 26.2, 28.0 and 16.4% of the total water consumption evaluated in the three years.     

Review of Alternative Methods for Estimating Terrestrial Emittance and Geothermal Heat Flux for Yellowstone National Park Using Landsat Imagery

Yellowstone National Park (YNP) is legally mandated to monitor geothermal features for their future preservation, and remote sensing is a component of the current monitoring plan. Landsat imagery was explored as a tool for mapping terrestrial emittance and geothermal heat flux for this purpose. Several methods were compared to estimate terrestrial emittance and geothermal heat flux (GHF) using images from 2007 (Landsat Thematic Mapper) and 2002 (Landsat Thematic Mapper Plus). Accurate estimations were reasonable when compared to previously established values and known patterns but were likely limited due to inherent properties of Landsat data, the effects of solar radiation, and variation among geothermal areas. Landsat data can be valuable for calculation of GHF in YNP. The method suggested in this paper is not highly parameterized. Landsat data provide the means to calculate GHF for all of YNP and have the potential to enable scientists to identify locations for in-depth study.     

Landsat TM versus MSS data for forest type identification

Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) data were digitally analyzed for forest type identification in the Kisatchie Ranger District, Kisatchie National Forest, Louisiana. Ground‐verification maps were produced from field surveys and interpretation of 1.12,000 and 1: 58,000 color‐infrared (CIR) aerial photography of nine compartments. Stand boundary and soils maps were input to a digital Geographic Information System (GIS) with the Landsat and ground‐verification data.

‐ Unsupervised classifications of the Landsat data did not identify the above cover types well. Supervised classifications were tested by stand agreement to the ground verification. The highest four‐class agreement was obtained for the TM classification (76 percent). Three‐class (open, pine, and hardwoods) stand agreements (81 (MSS) and 85 (TM) percent) were not significantly different as tested by analysis of variance (alpha level 0.1).     

Applying Landsat Imagery for Groundwater Development in Egypt

Landsat Thematic Mapper data over the Nile Valley and Delta were analyzed to assist in various phases of groundwater development in Egypt. Land surface features were identified and located in combination with other data stored in a Geographic Information System for input to the final hydrogeological map of this area. Simple vegetation indices were used to delineate the extent of vegetation cover and related to groundwater recharge. Supervised classification techniques were used to separate features such as sabkhas which are areas of high evaporative losses. Detection of upward groundwater seepage at the surface in the winter season was used to calibrate regional groundwater flow models. Possible future applications include estimation of evapotranspiration, determination of irrigation water needs, and improvement of the existing network of groundwater observation wells for water quality purposes.     

Spatial Analysis of Urban Growth Impacts on Vegetative Greenness with Landsat TM Data

Abstract

Landsat Thematic Mapper (TM) data have been used to monitor land cover types and to estimate biophysical parameters. However, studies examining the spatial relationships between land cover change and biophysical parameters are generally lacking. With the integration of remote sensing and Geographic Information Systems (GIS), these relationships can be better explored. The research reported in this paper applies this integrated approach for detecting urban growth and assessing its impact on vegetative greenness in the Zhujiang Delta, China. Multi‐temporal Landsat TM data were utilized to map urban growth and to extract and identify changes in vegetative greenness. GIS analyses were conducted to examine the changing spatial patterns of urban growth and greenness change. Statistical analyses were then used to examine the impact of urban growth on vegetative greenness. The results revealed that there was a notably uneven urban growth pattern in the delta, and urban development had reduced the scaled Normalized Difference Vegetation Index (NDVI) value by 30% in the urbanized area.     

Use of Landsat Thematic Mapper for urban land use/land cover mapping

Visual interpretation of Landsat Thematic Mapper data coupled with ground checking has been used to extract information for urban areas. The emphasis has been given on development of land use/land cover scheme and image interpretation keys for interpretation and delineation purposes using satellite remote sensing data. Lucknow city and its surroundings have been studied to evaluate the usefulness and potentiality of satellite data particularly Landsat Thematic Mapper for urban area studies. This study has demonstrated that remote sensing can provide a valuable tool for urban data acquisition.     

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.     

基于遥感技术的祖厉河流域LUCC与蒸散发研究

本文以陇中黄土高原的祖厉河流域为研究区,基于Landsat系列遥感影像,利用主成分变换方法,与地面温度和植被指数相结合,进行土地利用/土地覆盖提取研究,在此基础上,探讨祖厉河流域土地利用/土地覆盖变化(LUCC)及其驱动因素。利用基于TM影像的SEBAL(Surface Energy Balance Algorithm for Land)模型,从祖厉河流域三期影像提取了区域蒸散发通量。首先进行了NDVI、比辐射率和地面温度等地表参数的计算,然后反演了地表的净辐射、土壤热通量和感热通量,根据能量守恒最终获得日蒸散发量。结合气象观测数据反演作物系数,最后计算月蒸散发量。对同期的土地利用/土地覆盖数据和陆面蒸散发量进行对比分析,探讨了土地利用类型的变化对流域内能量和水分时空分异的影响。     

Spatial resolution improvement by merging MERIS-ETM images for coastal water monitoring

The Medium Resolution Imaging Spectrometer (MERIS) launched in March 2002 and has been providing images since June 2002. Before its launch, we had implemented a method to improve its resolution by merging its images with Landsat Enhanced Thematic Mapper images in order to preserve the best characteristics of the two images (spatial, spectral, temporal). We now present the results of this method for real MERIS images (level 1b and 2) in a coastal area. The robustness of the method is studied as well as the influence of the delay between the acquisitions of the two images.     

Estimating landscape imperviousness index from satellite imagery

This letter presents a practical method for landscape imperviousness estimation through the synergistic use of Landsat Enhanced Thematic Mapper Plus (ETM+) and high-resolution imagery. A 1-m resolution color-infrared digital orthophoto was used to calibrate a stepwise multivariate statistical model for continuous landscape imperviousness estimation from medium-resolution ETM+ data. A variety of predictive variables were initially considered, but only brightness and greenness images were retained because they were account for most of the imperviousness variation measured from the calibration data. The performance of this method was assessed, both visually and statistically. Operationally, this method is promising because it does not involve any more sophisticated algorithms, such as classification tree or neural networks, but offers comparable mapping accuracy. Further improvements are also discussed.     

Estimating the Effects of Map Error on Habitat Delineation for the California Spotted Owl in Southern California

The reliability of habitat maps that have been generated using Geographic Information Systems (GIS) and image processing of remotely sensed data can be overestimated. Habitat suitability and spatially explicit population viability models are often based on these products without explicit knowledge of the effects of these mapping errors on model results. While research has considered errors in population modeling assumptions, there is no standardized method for measuring the effects of inaccuracies resulting from errors in landscape classification. Using landscape‐scale maps of existing vegetation developed for the USDA Forest Service in southern California from Landsat Thematic Mapper satellite data and GIS modeling, we performed a sensitivity analysis to estimate how mapping errors in vegetation type, forest canopy cover, and tree crown size might affect delineation of suitable habitat for the California spotted owl (Strix occidentalis occidentalis). The resulting simulated uncertainty maps showed an increase in the estimated area of suitable habitat types. Further analysis measuring the fragmentation of the additional patches showed that they were too small to be useful as habitat areas.     

太湖梅梁湖湾蓝藻生物量遥感估算

本文利用陆地卫星TM数据和图像处理方法对太湖富营养化严重的梅梁湖区的浮游蓝藻生物量作了遥感估算。1992年夏季在海梁湖开展了星地同步浮游藻类监测,获得了湖中16个采样点的现场叶绿素a生物量(Q_a)和蓝藻生物量(Q_B)数据,并利用这两组数据分别建立了与差异植被指数DVI的遥感回归模型,从而得到水体中叶绿素a以及蓝藻生物量的空间分布信息。从遥感定量模型出发,运用逐个像元积分统计技术,估算出梅梁湖叶绿素a总量为2133kg、蓝藻总量为178.2t.与地面方法相比,遥感估算方法充分利用了TM数据中的浮游藻类含量分布与变化信息,具有较高的估算精度。     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

An assessment of chlorophyll-a concentration spatio-temporal variation using Landsat satellite data,in a small tropical reservoir

Traditional approaches to monitoring aquatic systems are often limited by the need for data collection which often is time-consuming, expensive and non-continuous. The aim of the study was to map the spatio-temporal chlorophyll-a concentration changes in Malilangwe Reservoir, Zimbabwe as an indicator of phytoplankton biomass and trophic state when the reservoir was full (year 2000) and at its lowest capacity (year 2011), using readily available Landsat multispectral images. Medium-spatial resolution (30 m) Landsat multispectral Thematic Mapper TM 5 and ETM+ images for May to December 1999–2000 and 2010–2011 were used to derive chlorophyll-a concentrations. In situ measured chlorophyll-a and total suspended solids (TSS) concentrations for 2011 were employed to validate the Landsat chlorophyll-a and TSS estimates. The study results indicate that Landsat-derived chlorophyll-a and TSS estimates were comparable with field measurements. There was a considerable wet vs. dry season differences in total chlorophyll-a concentration, Secchi disc depth, TSS and turbidity within the reservoir. Using Permutational multivariate analyses of variance (PERMANOVA) analysis, there were significant differences (p < 0.0001) for chlorophyll-a concentration among sites, months and years whereas TSS was significant during the study months (p < 0.05). A strong positive significant correlation among both predicted TSS vs. chlorophyll-a and measured vs. predicted chlorophyll-a and TSS concentrations as well as an inverse relationship between reservoir chlorophyll-a concentrations and water level were found (p < 0.001 in all cases). In conclusion, total chlorophyll-a concentration in Malilangwe Reservoir was successfully derived from Landsat remote sensing data suggesting that the Landsat sensor is suitable for real-time monitoring over relatively short timescales and for small reservoirs. Satellite data can allow for surveying of chlorophyll-a concentration in aquatic ecosystems, thus, providing invaluable data in data scarce (limited on site ground measurements) environments.