Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction

Toward Renewable Eenergy Geo-information Infrastructures: Applications of GIScience and Remote Sensing that Build Institutional Capacity

Sustained policy support is necessary in order to drive a transition toward renewable energy (RE). The ability to realize RE policy objectives is constrained by a range of geographic factors related to resource potential, the distribution of resources, land availability/suitability, the absorptive capacity of proximal infrastructure, and local socio-political acceptance. With this in mind, this paper provides a systematic review of how geographic information science and remote sensing techniques have been applied to reduce uncertainties surrounding renewable energy development, with emphasis on policy and planning needs. The concept of a ‘geo-information infrastructure’ is used to bring coherence and direction to this growing body of literature. The review highlights four underdeveloped research areas, including: Resolving issues of scalar discordance through comprehensive analysis at local and regional scales; mapping interactions in space of multiple supply options to deliver more accurate and sophisticated estimates of RE potential in an area and to identify competitive and symbiotic land-use situations; using energy resource maps as primary inputs into the development of technology road-maps; and developing geographically explicit indicators which can signal priority areas for RE recovery based on social and environmental returns on investments. In each case, suggestions moving forward are provided. The paper identifies knowledge-based institutional networking as a pathway through which local and regional public authorities can be equipped with the resources necessary to build and mobilize a geo-information infrastructure

GIS and Remote Sensing for Renewable Energy Assessment and Maps

This book aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making

Application of Remote Sensing to the Chesapeake Bay Region. Volume 2: Proceedings

A conference was held on the application of remote sensing to the Chesapeake Bay region. Copies of the papers, resource contributions, panel discussions, and reports of the working groups are presented

Coverage of in situ climatological observations in the world's mountains

Many mountainous environments and ecosystems around the world are responding rapidly to ongoing climate change. Long-term climatological time-series from such regions are crucial for developing improving understanding of the mechanisms driving such changes and ultimately delivering more reliable future impact projections to environmental managers and other decision makers. Whilst it is already established that high elevation regions tend to be comparatively under-sampled, detailed spatial and other patterns in the coverage of mountain climatological data have not yet been comprehensively assessed on a global basis. To begin to address this deficiency, we analyse the coverage of mountainous records from the Global Historical Climatological Network-Daily (GHCNd) inventory with respect to space, time, and elevation. Three key climate-related variables—air temperature, precipitation, and snow depth—are considered across 292 named mountain ranges. Several additional datasets are also introduced to characterize data coverage relative to topographic, hydrological, and socio-economic factors. Spatial mountain data coverage is found to be highly uneven, with station densities in several “Water Tower Units” that were previously identified as having great hydrological importance to society being especially low. Several mountainous regions whose elevational distribution is severely undersampled by GHCNd stations are identified, and mountain station density is shown to be only weakly related to the human population or economic output of the corresponding downstream catchments. Finally, we demonstrate the capabilities of a script (which is provided in the Supplementary Material) to produce detailed assessments of individual records' temporal coverage and measurement quality information. Overall, our contribution should help international authorities and regional stakeholders identify areas, variables, and other monitoring-related considerations that should be prioritized for infrastructure and capacity investment. Finally, the transparent and reproducible approach taken will enable the analysis to be rapidly repeated for subsequent versions of GHCNd, and could act as a basis for similar analyses using other spatial reporting boundaries and/or environmental monitoring station networks

Earth resources: A continuing bibliography with indexes, issue 18

This bibliography lists 434 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1978. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Rainfall-runoff modeling in arid areas

The Wadi Dhuliel catchment/ North east Jordan, as any other arid area has distinctive hydrological features with limited water resources. The hydrological regime is characterized by high variability of temporal and spatial rainfall distributions, flash floods, absence of base flow, and high rates of evapotranspiration. The aim of this Ph.D. thesis was to apply lumped and distributed models to simulate stream flow in the Wadi Dhuliel arid catchment. Intensive research was done to estimate the spatial and temporal rainfall distributions using remote sensing. Because most rainfall-runoff models were undertaken for other climatic zones, an attempt was made to study limitations and challenges and improve rainfall-runoff modeling in arid areas in general and for the Wadi Dhuliel in particular. The thesis is divided into three hierarchically ordered research topics. In the first part and research paper, the metric conceptual IHACRES model was applied to daily and storm events time scales, including data from 19 runoff events during the period 1986-1992. The IHACRES model was extended for snowfall in order to cope with such extreme events. The performance of the IHACRES model on daily data was rather poor while the performance on the storm events scale shows a good agreement between observed and simulated streamflow. The modeled outputs were expected to be sensitive when the observed flood was relatively small. The optimum parameter values were influenced by the length of a time series used for calibration and event specific changes. In the second research paper, the Global Satellite Mapping of Precipitation (GSMaP_MVK+) dataset was used to evaluate the precipitation rates over the Wadi Dhuliel arid catchment for the period from January 2003 to March 2008. Due to the scarcity of the ground rain gauge network, the detailed structure of the rainfall distribution was inadequate, so an independent from interpolation techniques was used. Three meteorological stations and six rain gauges were used to adjust and compare with GSMaP_MVK+ estimates. Comparisons between GSMaP_MVK+ measurements and ground rain gauge records show distinct regions of correlation, as well as areas where GSMaP_MVK+ systematically over- and underestimated ground rain gauge records. A multiple linear regression (MLR) model was used to derive the relationship between rainfall and GSMaP_MVK+ in conjunction with temperature, relative humidity, and wind speed. The MLR equations were defined for the three meteorological stations. The ‘best’ fit of the MLR model for each station was chosen and used to interpolate a multiscale temporal and spatial distribution. Results show that the rainfall distribution over the Wadi Dhuliel is characterized by clear west-east and north-south gradients. Estimates from the monthly MLR model were more reliable than estimates obtained using daily data. The adjusted GSMaP_MVK+ dataset performed well in capturing the spatial patterns of the rainfall at monthly and annual time scales, while daily estimation showed some weakness for light and moderate storms. In the third research paper, the HEC-HMS and IHACRES rainfall runoff models were applied to simulate a single streamflow event in the Wadi Dhuliel catchment that occurred in 30-31.01.2008. Both models are considered suitable for arid conditions. The HEC-HMS model application was done in conjunction with the HEC-GeoHMS extension in ArcView 3.3. Streamflow estimation was performed on hourly data. The aim of this study was to develop a new framework of rainfall-runoff model applications in arid catchment by integrating a re-adjusted satellite derived rainfall dataset (GSMaP_MVK+) to determine the location of the rainfall storm. Each model has its own input data sets. HEC-HMS input data include soil type, land use/land cover map, and slope map. IHACRES input data sets include hourly rainfall and temperature. The model was calibrated and validated using observed stream flow data collected from Al-Za’atari discharge station. IHACRES shows some weaknesses, while the flow comparison between the calibrated streamflow results agrees well with the observed streamflow data of the HEC-HMS model. The Nash-Sutcliffe efficiency (Ef) for both models was 0.51, and 0.88 respectively. The application of HEC-HMS model in this study is considered to be satisfactory

Earth resources: A continuing bibliography with indexes (issue 52)

This bibliography lists 454 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1 and December 31, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Rainwater Harvesting in Rural Jordan: A GIS and remote sensing-based analysis of ancient and modern adaptations to water scarcity in a changing environment

This study investigates the topic of rainwater harvesting on the Karak Plateau located in rural Jor-dan. The term rainwater harvesting describes various methods and structures employed for the collection, storage and use of rainwater and resulting (on-site) surface runoff. Within the scope of traditional water management, over millennia, many of these techniques were developed, refined and applied in Jordan, as well as in other, especially semiarid, regions of the world. This tradition is rooted in the natural water shortage of the plateau and frequent absence of other reliable sources of fresh water. Today, population growth, climate change and local effects of globalization and global change are leading to growing water shortages in the MENA region (Middle East and North Africa) and many other parts of the world. In the search for sustainable solutions for this problem, traditional as well as new strategies of rainwater harvesting have recently been receiving increasing interest. The present study contributes to an enhanced understanding of the applicability and the potential of some of the most widely-used, traditional rainwater harvesting methods, especially the use of cisterns. The mapped structures were examined taking into account the settlement history and the respective circumstances of the natural and human environment. Possible determining factors concerning site preferences and resulting patterns in the spatial distribution of rainwater harvesting sites have been detected. The diachronic comparative analysis revealed changes in human-environment-interactions, particularly with regard to the significance and management of local water resources under natural shortage. The collected data enabled the detailed estimation of the rainwater harvesting potential and the suggestion of possible ways to improve and expand current rainwater harvesting schemes and efforts. The integration and possible role of rainwater harvesting strategies were discussed with regard to modern, sustainable water management and supply. Additionally, the applicability of modern geoinformation techniques was evaluated. Remote sensing techniques and methods of image analysis, particularly with regard to the interpretation of satellite images of very high resolution, were examined especially. The combination of ground truth and other information from fieldwork and remote sensing-based data and techniques has proven most suitable and efficient. The mostly remote sensing-based mapping of rainwater harvesting structures and the establishment of a comprehensive database formed the basis for all subsequent analysis and possible further, sustainable planning steps. The semiautomatic analysis of the satellite imagery provided detailed information on land use/land cover and building rooftops and thus decisively contributed to the improvement of the (input) data basis. All in all, the collected data enabled a significantly enhanced, quantitative estimation of the rainwater harvesting potential of the study area. Many of the gained findings and insights can be transferred onto other dry areas and regions with similar environmental or socio-economic conditions

Balancing water for food and environment : hydrological determinants across scales in the Thukela River Basin.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2008.In this study, geophysical measurements (Electrical Resistivity Tomography-ERT) and remote sensing techniques were applied in the Thukela river basin at various scales to complement the classical hydrometeorological networks. Detailed process hydrological studies were carried out at the Potshini catchment in the Thukela river basin to provide an in-depth understanding of the influence of different land use management practices, notably the impact of conservation tiJlage practices, on runoff generation and soil moisture retention characteristics at field scale. The general trend that was observed in the field studies is that conservation tillage systems influenced the partitioning of rainfall, by significantly reducing surface runoff over agricultural lands under conservation tillage practices, with a reduction ranging from 46 to 67%. The field soil-water balance studies also indicated that more soil moisture was retained in plots under conservation tillage practices compared to plots under conventional tillage and hence the wider adoption of such a practice could influence the partitioning of rainfall across scales. The field based study was integrated into catchment process studies where a classical hydrometrical network was complemented with geophysical measurements (ERT) along catchment transects to determine the interaction of the surface and sub-surface water and the relative contribution of the subsurface water to catchment response. The study revealed that the shallow ground water contributes significantly, close to 75%, of the stream flows in the Potshini catchment, especially during the dry seasons, with the response of the shallow ground water being a function of both the rainfall intensity and daily total amount. The potential of integrating the catchment process studies with the larger river basin scale was explored through the evaporative term of the water balance by applying the Surface Energy Balance Algorithm for Land (SEBAL), a remote sensing methodology, to estimate total evaporation (ET) from the Moderate Imaging Spectroradiometer (MODIS) satellite images. This was validated with ground measurements from a Large Aperture Scintilometer (LAS) installed in the Potshini catchment. Good comparison was established between the remotely sensed estimates and LAS measurements with a deviation range of between -14 to 26% on discrete days, where the deviation was defined as the departure of the remotely sensed estimates of ET from the respective LAS measurements. The results from this study compare well with results from similar studies in other countries with different climatic conditions. Subsequently, the evaporative water use of various land uses in the upper Thukela river basin was assessed using MODIS images. Commercial forestry was identified to be the land use with a consistent and relatively high evaporative water use In the study area. High evaporation rates over water bodies were observed during the wet summer season when both the natural and man made water bodies were at full capacity. Nevertheless, it is recognized that the inherent low resolution ofthe MODIS images could have impacted on the SEBAL results. Finally, a conceptual framework, drawing the strengths of classical hydrometeorological networks, geophysical measurements, isotope tracers and remote sensing is suggested with the potential of enhancing our understanding and conceptualization of hydrological determinants across scales. The relevance of the framework to water resources management is highlighted through its application to the Potshini catchment and the Thukela river basin using results and findings from this study