ID: 53047
Title: Using simulated Terrestrial Laser Scanning to analyse errors in high-resolution scan data of irregular surfaces
Author: Rebecca A Hodge
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Laser scanning, Error, Model, Geomorphology
Abstract: Terrestrial Laser Scanning (TLS) is increasingly being used to collect mm-resolution surface data from a broad range of environments. When scanning complex surfaces, interactions between the surface topography, laser footprint and scanner precision can introduce errors into the point cloud. Quantification of these errors is, however, limited by the availability of independent measurement techniques. This research presents simulated TLS as a new approach to error quantification. Two sets of experiments are presented. The first set demonstrates that simulated TLS is able to reproduce real TLS data from a plane and a pebble. The second set uses simulated TLS to assess a methodology developed for the collection and processing of field TLS data. Simulated TLS data is collected from surfaces up to ~1m2 created from regular arrays of uniform spheres (sphere diameters of 10 to 100mm) and irregular arrays of mixed spheres (median sphere diameters of 16 to 94 mm). These data were analysed to (i) assess the effectiveness of the processing methodology at removing erroneous points; (ii) quantify the magnitude of errors in a digital surface model (DSM) interpolated from the processed point cloud; and (iii) investigate the extent to which the interpolated DSMs retained the geometric properties of the original surfaces. The processing methodology was found to be effective, especially on data from coarser surfaces, with the retained points typically having an inter-quartile range (IQR) of point errors of ~2mm. DSM errors varied as a function of sphere size and packing, with DSM errors having an IQR of ~2 mm for the regular surfaces and ~4mm for the irregular surfaces. Finally, whilst in the finer surfaces point and DSM errors were a substantial proportion of the sphere diameters, geometrical analysis indicated that the DSMs still reproduced properties of the original surface such as semivariance and some percentiles of the surface elevation distribution.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53046
Title: Bias-corrected rational polynomial coefficients for high accuracy geo-positioning of QuickBird stereo imagery
Author: Xiaohua Tong, Shijie Liu, Qihao Weng
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: QuickBird, Rational polynomial coefficient, Bias correction, Geo-positioning accuracy
Abstract: The rational function model (RFM) is widely used as an alternative to physical sensor models for 3D ground point determination with high-resolution satellite imagery (HRSI). However, owing to the sensor orientation bias inherent in the vendor-provided rational polynomial coefficients (RPCs), the geo-positioning accuracy obtianed from these RPCs is limited. In this paper, the performances of two schemes for orientation bias correction (i.e RPCs modification and RPCs regeneration) is presented based on one separate - orbit QuickBird stereo image pair in Shanghai, and four cases for bias correction, including shift bias correction, shift and drift bias correction, affine model bias correction and second-order polynomial bias correction, are examined. A 2-step least squares adjustment method is adopted for correction parameter estimation with a comparison with the RPC bundle adjustment method. The experiment results demonstrate that in general the accuracy of te 2-step least squares adjustment method is almost identical to that of the RPC bundle adjustment method. With the shift bias correction method and minimal 1 ground control point (GCP), the modified RPCs improve the accuracy from the original 23m to 3m in planimetry and 17m to 4 m in height. With the shift and drift bias correction method, the regenerated RPCs achieve a further improved positioning accuracy of 0.6m in planimetry and 1m in height with minimal 2 well-distributed GCPs. The affine model bias correction yields a geo-positioning accuracy of better than 0.5m in planimetry and 1m in height with 3 well-positioned GCPs. Further tests with the second-order polynomial bias correction model indicate the existence of potential high-order error signals in the vendor-provided RPCs, and on condition that an adequate redundancy in GCP number is available, an accuracy of 0.4 m in planimetry and 0.8 m in height is attainable.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53045
Title: Real-time registration of airborne lase data with sub-decimeter accuracy
Author: Jan Skaloud, Philipp Schaer, Yannick Stebler, Phillip Tome
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: LIDAR, Georeferencing, GPS/INS, Real-time -laser scanning, Accuracy
Abstract: This paper presents a methodology for the precise registering of airborne laser data directly in flight with an accuracy that is sufficient for the majority of derived products, such as digital terrain models. We first present the strategy that integrates GPS/INS/LiDAR data for generating laser point clouds directly in flight and analyzes their accuracy. The latter requires the implementation of a functional covariance propagation on-line for all the system components (i.e. trajectory, laser, system calibration) to which influences of scanning geometry are added at the end of a flight line The study of scanning geometry necessitates the classification of vegetation and coarse estimation of the terrain normal. This is achieved by a method that we formerly proposed for off-line quality analysis. The second part of the paper focuses on the positioning component. In high resolution scanning performed close to the terrain, the absolute accuracy of the resulting point cloud depends mainly on the quality of the trajectory which is related to the type of GPS solution (e.g. absolute positioning, DGPS, RTK). To reach sub-decimeter accuracy for the point cloud in the real-time, an RTK-GPS solution is needed. This requires teh establishment of a communication link for the transmission of GPS corrections (or measurements). We analyze the usability of RTK-GPS/ALS acquired during several flights using different communication methods in the particular context of helicopter based missions. We focus mainly on the exploitation of nation-wide reference GNSS networks and confirm experimentally that the real-time registration of airborne laser data is feasible with sub-decimeter accuracy. Such quality is sufficient not only for a wide range of applications, but it also opens new opportunities for monitoring missions that require a short reaction time. Finally, we concentrate on situations when the phase and code corrections cannot be transmitted, and the quality of the differential carrier-phase positioning needs to be predicted. We validate the previously introduced indicators of positioning quality by simulated degradation of the input data.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53044
Title: A DEM genralization by minor valley branch detection and grid filling
Author: Tinghua Ai, Jingzhong Li
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: DEM, Map generalization, Terrain representation, Multi-scale representation
Abstract: As an important method of terrain representation, a DEM usually needs to be generalized at multiple resolutions in order to adapt to different applications. The preservation of main landscape features is an important constraint in DEM generalization. The traditional generalization method based on signal processing by resampling or low-pass filtering is just a data compression operation rather than the abstraction of real information. This study develops a structured analysis method to generalize DEM data through the identification of minor valleys and filling the corresponding depression positions. The generalization process has two steps: geographic decision and geometric operation. According to their hydrological significance , the unimportant valley branches are detected and their corresponding coverage is filled by raising the terrain to make the terrain surface smoother. In constrast to the conventional algorithms based on image processing, this method is able to retain the main geographical characteristics more effectively in terrain representation.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53043
Title: Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery
Author: Hugh Eva, Silvia Carboni, Frederic Achard, Nicolas Stach, Laurent Durieux, Jean-Francois Faure, Danilo Mollicone
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Forestry, Change detection, Sampling, Landsat, SPOT
Abstract: A global systematic sampling scheme has been developed by the UN FAO and the EC TREES project to estimate rates of deforestation at global or continental levels at intervals of 5 to 10 years. This global scheme can be intensified to produce results at the national level. In this paper, using surrogate observations, we compare the deforestation estimates derived from these two levels of sampling intensities (one, the global, for the Brazilian Amazon the other, national, for French Guiana) to estimates derived from the official inventories. We also report the precisions that are achieved due to sampling errors and , in the case of French Guiana, compare such precision with the official inventory precision. We extract nine sample data sets from the official wall-to-wall deforestation map derived from satellite interpretations produced for the Brazilian Amazon for the year 2002 to 2003. This global sampling scheme estimate gives 2.81 million ha of deforestation (mean from nine simulated replicates ) with a standard error of 0.10 million ha. This compares with the full population estimate from the wall-to-wall interpretations of 2.73 million ha deforested, which is within one standard error of our sampling test estimate. The relative difference between the mean estimate from sampling approach and the full population estimate is 3.1% and the standard error represents 4.0% of the full population estimate. This global sampling is then intensified to a territorial level with a case study over French Guiana to estimate deforestation between the years 1990 and 2006. For the historical reference period, 1990, Landsat-5 Thematic Mapper data were used. A coverage of SPOT-HRV imagery at 20m x 20m resolution acquired at the Cayenne receiving station in French Guiana was used for year 2006. Our estimates from the intensified global sampling scheme over French Guiana are compared with those produced by the national authority to report on deforestation rates under the Kyoto protocol rules for its overseas department. The latter estimates come from a sample of nearly 17,000 plots analyzed from same spatial imagery acquired between year 1990 and year 2006. This smapling scheme is derived from the traditional forest inventory methods carried out by IFN (Inventaire Forestier National). Our intensified global smapling scheme leads to an estimate of 96,650 ha deforested between 1990 and 2006, which is within the 95% confidence interval of the IFN sampling scheme, which gives an estimate of 91,722 ha, representing a relative difference from the IFN of 5.4%. These results demonstrate that the intensification of the global sampling scheme can provide forest area change estimates close to those achieved by official forest inventories (<6%), with precisions of between 4% and 7%, although we only estimate errors from sampling, not from the use of surrogate data. Such methods could be used by developing countries to demonstrate that they are fulfillilng requirements for reducing emissions from deforestation in the framework of an REDD (Reducing Emissions from Deforestation in Developing Countries) mechanism under discussion within the United Nations Framework Convention on Climate Change (UNFCCC). Monitoring systems at national levels in tropical countries can also benefit from pan-tropical and regional observations, to ensure consistency between different national monitoring systems.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53042
Title: Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model
Author: Yichun Xie, Zongyao Sha, Yongfei Bai
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Classification, GIS, History, Landsat, Vegetation
Abstract: Large and growing archives of orbital imagery of the earth ' s surface collected over the past 40 years provide an important resource for deocumenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53041
Title: Delineation and geometric modeling of road networks
Author: Charalambos Poullis, Suya You
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Road extraction, Network delineation, Road detection, Road modeling
Abstract: In this work we present a novel vision- based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation -based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentation with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53040
Title: Consistency of accuracy assessment indices for soft classification: Simulation analysis
Author: Jin Chen, Xiaolin Zhu, Hidefumi Imura, Xuehong Chen
Editor: George Vosselman
Year: 2010
Publisher: Elsevier, Vol 65, issue 2, March 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Soft classification, Accuracy assessment, Sub-pixel confusion matrix, RMSE, Consistency
Abstract: Accuracy assessment plays a crucial role in the implementation of soft classification. Even though many indices of accuracy assessment for soft classification have been proposed, the consistencies among these indices are not clear, and the impact of sample size on these consistencies has not been investigated. This paper examines two kinds of indices: map-level indices, including root mean square error (rmse), kappa, and overall accuracy (oa) from the sub-pixel confusion matrix (SCM); and category-level indices, including crmse, user accuracy (ua) and producer accuracy (pa). A careful simulation was conducted to investigate the consistency of these indices and the effect of sample size. The major findings were as follows: (1) The map-level indices are highly consistent with each other, whereas the category-level indices are not. (2) The consistency among map-level and category-level indices becomes weaker when the sample size decreases. (3) The rmse is more affected by error distribution among classes than are kappa and oa. Based on these results, we recommend that rmse can be used for map-level accuracy due to its simplicity, although kappa and oa may be better alternatives when the sample size is limited because the two indices are affected less by the error distribution among classes. We also suggest that crmse should be provided when map users are not concerned about the error source, whereas ua and pa are more useful when the complete information about different errors is required. The results of this study will be of benefit to the development and application of soft classifiers.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53039
Title: Mixture model for the segmentation of the InSAR coherence map
Author: Riadh Abdelfattah, Jean-Marie Nicolas
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, Supplement 1, February 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Interferometric SAR, Mixture model, Coherence map, Classification
Abstract: In this work, we classify the interferometric SAR (InSAR) coherence map, computed with the second kind statistics (Abdelfattah and Nicolas, 2006a), into three classes using Bayes ' theorem. The segmentation procedure is performed using a mixture modelling of the coherence map. The multimodal density of the mixture comprises three component functions characterizing different land surface categories (lake, bare soil, urban..). This work is an amelioration version of that published by the authors in (Abdelfattah and Nicolas, 2007, 2006b). We test the performance of the proposed mixture model on three different datasets. The results of this study could be used as a supervised learning step for an automatic land cover classification algorithm. This new method classifying the image considering the corresponding InSAR coherence map is particularly powerful for the detection of layover and shadow regions.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53038
Title: A comparative study for unmixing based Landsat ETM+ and ASTER image fusion
Author: Nouha Mezned, Saadi Abdeljaoued, Mohamed Rached Boussema
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, Supplement 1, February 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Landsat ETM+, ASTER, Multispectral image fusion, Hybrid image, Linear unmixing, Mine tailing cartography
Abstract: The mineral environment of the Bouaouane -Jebel (Hill) Hallouf mine, in the north of Tunisia, is monitored and analysed by making use of the laboratory analysis and remote sensing images, ETM + as well as ASTER VNIR and SWIR data, acquired in the same period. The main contribution of this paper consists of a methodology using multispectral multi-sensor fusion for the refinement of the mine tailing cartography around the studied mine. The developed methodology is based on the linear spectral unmixing approach which is applied to a multispectral hybrid image. This image was generated from the fusion of Landsat ETM+ and ASTER SWIR data. A comparative study is made between the hybrid and ASTER (VNIR and SWIR) images classification with respect to laboratory analysis. The given results show that the fusion of Landsat ETM+ and ASTER SWIR multispectral image yields the best mineral detection.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53037
Title: Remote sensing of the link between arable field and elephant (Loxodonta africana) distribution change along a tsetse eradication gradient in teh Zambezi valley, Zimbabwe
Author: Amon Murwira, Andrew K. Skidmore, H.J.G.Huizing, H.H.T.Prins
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, Supplement 1, February 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Landsat TM, Remote sensing, Tsetse eradication, Arable fields, GIS
Abstract: We investigated whether the proportion of remotely sensed arable fields increased along a tsetse eradication gradient in the Sebungwe region. We also investigated whether and to what extent this increase in arable fields affected the distribution of the African elephant (Loxodonta africana) between the 1980s and 1990s. Results showed a relatively higher increase in the proportion of arable fields in the zone cleared of tsetse by 1986 compared to the zone that was still tsetse infested by the same date. Results also showed contrasting patterns in the relationship between the proportion of the habitat under arable fields and elephant distribution between the two periods. Specifically, in the 1980s, when arable field cover was between 0% and 11%, there was a weak (p>0.05) positive relationship between elephant presence and the proportion of the habitat under arable fields. In contrast, a significant (p<0.05) negative relationship emerged in the 1990s. When arable field cover ranged between 0% and 88%. Furthermore, the results demonstrated that the change in the probability of elephant presence between the early 1980s and the early 1990s was significantly (p<0.05) related to the change in the proportion arable fields. In conclusion, this study demonstrated that the expansion of arable fields in teh Sebungwe was greater in areas where tsets had been eradicated compared with areas that were still tsetse infested. Overall, the results suggest that using remotely sensed data, we can conclude that tsetse eradication led to the redistribution of elephants in response to arable field expansion.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53036
Title: Simple and effective monitoring of historic changes in nearshore environments using the free archive of Landsat imagery
Author: Anders Knudby, Candace Newman, Yohanna Shaghude, Christopher Muhando
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, Supplement 1, February 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Landsat, Time series, Change detection, Nearshore environments, Coral, Seagrass, Algae
Abstract: The recently released archive of Landsat imagery can be used to detect historic changes in nearshore environments. We used a series of free Landsat images spanning the years from 1984 to 2009 to detect changes in the spatial extent of dominant substrate types, coral, algae, and seagrass, around Bawe and Chumbe islands in Zanzibar, and we compared the use of true-colour composites and supervised classifications. Results indicate temporal changes in the spatial extent of seagrass meadows are easily mapped with Landsat imagery, whereas temporal changes in algae cover and particularly coral cover pose greater challenges because of the similarities in spectral reflectance properties between the relevant substrate types. Supervised classification requires substantially more processing than the simple display of true-colour composites, but does not improve interpretation in our study. We suggest that historic Landsat imagery, obtained at no cost and processed minimally with free software, is the best available data source for studies of historic changes in the nearshore environments of East Africa.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53035
Title: Long-term analysis of snow-covered area in the Moroccan High-Atlas through remote sensing
Author: A. Boudhar, B. Duchemin, L. Hanich, L. Jarlan, A. Chaponniere, P. Maisongrande, G. Boulet, A. Chehbouni
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, Supplement 1, February 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: High-Atlas, Snow cover, Optical remote sensing, SPOT-VEGETATION, Snow Index, Nival hydrology
Abstract: The High-Atlas mountainous region in Morocco is a true water tower for the neighbouring arid plains, where the water resources are intensively and increasingly subjected to exploitation for agriculture and tourism. In order to manage this resource sustainably, it is necessary to describe accurately all the processes that contribute to the hydrological cycle of the area, and, in particular, to know the respective contributions of liquid and solid precipitations to runoff. In this context, a seven-year time series of SPOT-VEGETATION images is used for mapping snow-covered areas. The spatial and temporal variations of the snow cover are analyzed for the entire High-Atlas region as well as by altitudinal zones. The spatial distribution of snow-covered areas appears logically controlled by elevation, and its temporal fluctuations can be clearly used to identify dry and wet seasons. In addition, a possible control of snowfalls by the Northern Atlantic climate variability, and, in particular, the North Altantic Oscillation, is highlighted. Finally, this study shows how satellite remote sensing can be useful for the long-term observation of the intra-and inter-annual variability of snowpacks in rather inaccessible regions where the network of meteorological stations is deficient.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53034
Title: Using satellite remote sensing to assess evapotranspiration:Case study of the upper Ewaso Ng ' iro North Basin, Kenya
Author: Jeniffer Kinoti Mutiga, Zhongbo Su, Tsahaei Woldai
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, Supplement 1, February 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Ewaso Ng ' iro, SEBAL, Actual evapotranspiration, Remote sensing, Land cover/landuse, Energy balance, Water balance
Abstract: Actual evapotranspiration (ETa) is one of the most useful indicators to explain whether the water is being used as "intended". ETa variations, both in space and time and for different land use types are seen to be highly indicative for the adequacy, reliability and equity in water use; the knowledge of these conditions is essential for judicious water resources management. Unfortunately, ETa estimation usder actual field conditions is still a big challenge for both scientists and wate managers. The complexity associated with the estimation of ETa has led to the developmetn of various methodological approaches for estimating ETa over time. During the past two to three decades, significant progress has been made in estimating actual evapotranspiration using satellite remote sensing. These methods provide powerful means for computing ETa from a pixel scale to that of an entire basin. In this study, surface energy balance algorithm for land (SEBAL) was used to compute a complete radiation and energy balance along with the resistances for momentum, heat and water vapor transport for each pixel in the Upper Ewaso Ng ' iro North River Basin, in Kenya. This was then applied to assess the spatio-temporal distribution of ETa in the basin. The mean annual ETa estimated from SEBAL for 2000, 2003 and 2006 were compared with the mean annual ETa calculated from water balance method for the same periods and a good correlation of about 70% was observed. It was further observed that ETa increased gradually from 2000 to 2006 with an annual rate of about 15%. The estimated daily, monthly and annually ETa distribution for the period of study were used to anlayze water use patterns across the basin thus giving more insights into the underlying factors impacting on the water resources which could be used to facilitate the formulation of appropriate water resources management strategies for the basin.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 53033
Title: Estimation of spatial-temporal rainfall distribution using remote sensing techniques: A case study of Makanya catchment, Tanzania
Author: Kinoti Jeniffer, Zhongbo Su, Tsahaei Woldai, Ben Maathuis
Editor: Alfred Stein
Year: 2010
Publisher: Elsevier, Vol 12, Supplement 1, February 2010
Source: Centre for Ecological Science,Indian Institute of Science, Bangalore-12
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: MSG, Rainfall estimation, Remote sensing, TRMM, Ungauged catchments and spatio-temporal, variability
Abstract: Rainfall-runoff modeling provides an opportunity to easily simulate the response of a watershed, thus providing an option for sustainable water resources management particularly in dry regions of sub-Saharan Africa (SSA). Analysis of rainfall-runoff relationships in a catchment forms the basis of hydrological modeling. However, rainfall is a highly dynamic process constantly changing in form and intensity as it passes over a given area. Traditionally, rainfall is measured using limited rain guages at ground stations and often, the dynamics are not captured and yet it is the main input variable in any hydrological modeling. Without improved rainfall estimation, flow discharge estimates from rainfall-runoff relationship in both gauged and ungauged catchments particularly in arid and semi-arid regions remain a major challenge. Application of remote sending informaiton becomes crucial in the process of estimating rainfall patterns of these areas. The estimation of rainfall in this study was based on the blending of teh geostationary MeteroSat Second Generation (MSG), infrared channel with the low-earth orbiting passive Tropical Rainfall Measuring Mission (TRMM), and microwave channel satellite data. To combine these two satellite data, a regression function associated with a threshold as an upper cloud temperature limit where rain occurs was determined. In this way, Makanya catchment rainfall maps (daily, monthly, and seasonal) with 3 km pixel size from 2004 to 2006 were generated by aggregating the 15 min rainfall values. Comparison of the results obtained from the blended TRMM-MSG with the available ground gauge data for 2004 and 2005 periods, gave a good correlation of about 80%. In conclusion, the developed TRMM-MSG blending procedure was foudn to be a reliable and robust way of obtaining spatial-temporal rainfall distribution of a given area and particularly so for arid and semi-arid lands (ASALs) such as Makanya with sparse data acquisition networks.
Location: 231
Literature cited 1: None
Literature cited 2: None