Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

NASA Earth Resources Survey Symposium. Volume 3: Summary reports

This document contains the proceedings and summaries of the earth resources survey symposium, sponsored by the NASA Headquarters Office of Applications and held in Houston, Texas, June 9 to 12, 1975. Topics include the use of remote sensing techniques in agriculture, in geology, for environmental monitoring, for land use planning, and for management of water resources and coastal zones. Details are provided about services available to various users. Significant applications, conclusions, and future needs are also discussed

Detecting the Scale and Resolution Effects in Remote Sensing and GIS.

This study examines the relationship between resolution and fractal dimensions of remotely sensed images. Based on the results of testing for the reliability of the algorithms on hypothetical surfaces, the isarithm algorithm is selected for determining the fractal dimensions of remotely sensed images. This algorithm is then applied to simulated fractal Brownian motion images and four calibrated airborne multispectral remotely sensed image data sets with different true and artificial resolutions for Puerto Rico. The results from applying the fractal method to images at different levels of resolution suggest that the higher the resolution of an image, the higher the fractal dimension of the image and the more complex the image surface. This relationship between resolution and fractal dimension is further verified by results from analysis employing the local variance method for the same data sets; where it is found that the higher the resolution, the higher the local variance or the more complex the image surface. The images with artificial resolutions were found to be unrealistic in simulating images with different resolutions because the aggregate method used in generating these images dose not exactly simulate the sensor\u27s response to resolution changes. The aggregate method has been widely used in image resampling and cautious use of this algorithm is suggested in future studies. The findings show that the fractal method is a useful tool in detecting the scale and resolution effects of remotely sensed images and in evaluating the trade-offs between data volume and data accuracy. More studies employing fractals and other spatial statistics to images with different artificial resolutions generated using better aggregation algorithms are needed in the future in order to further detect the scale and resolution effects in remote sensing and GIS

THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

Trends in vegetation productivity and seasonality for Namaqualand, South Africa between 1986 and 2011: an approach combining remote sensing and repeat photography

This thesis presents an assessment of vegetation change and its drivers across a subset of Namaqualand, South Africa. Namaqualand forms part of the Succulent Karoo biome, which is characterised by exceptionally high species biodiversity but which has undergone severe transformation since the arrival of pastoral colonists. Vegetation productivity in Namaqualand is of great importance since there is a high dependence on natural resources, livestock and agriculture for both subsistence and income. However, there is considerable debate on the relative contribution of land-use change and climate change to vegetation change and land degradation in Namaqualand. Early studies based on bioclimatic envelop models suggest that an increase in temperature and more arid conditions could result in the vegetation cover of the Succulent Karoo being significantly reduced. On the other hand, more recent studies show that less extreme changes in rainfall could result in the vegetation of the biome remaining fairly stable with possible increases in the spatial extent by 2050. Furthermore, field observations and repeat photography, suggest that the change in vegetation in the region over the course of the 20th century generally portrays an increase in cover largely as a result of changes in land-use. By combining repeat photography and satellite data from NOAA-AVHRR and TERRA-MODIS sensors as well as baseline climatology data from the CRU TS 3.2 data set this study aimed to: (1) Determine the critical pathways of inter-annual and intra-seasonal vegetation change in the Namaqualand; (2) Investigate the role of land-use and climate variability as key drivers of vegetation change in Namaqualand

Characterizing geomorphological change to support sustainable river restoration and management

The hydrology and geomorphology of most rivers has been fundamentally altered through a long history of human interventions including modification of river channels, floodplains, and wider changes in the landscape that affect water and sediment delivery to the river. Resultant alterations in fluvial forms and processes have negatively impacted river ecology via the loss of physical habitat, disruption to the longitudinal continuity of the river, and lateral disconnection between aquatic, wetland, and terrestrial ecosystems. Through a characterization of geomorphological change, it is possible to peel back the layers of time to investigate how and why a river has changed. Process rates can be assessed, the historical condition of rivers can be determined, the trajectories of past changes can be reconstructed, and the role of specific human interventions in these geomorphological changes can be assessed. To achieve this, hydrological, geomorphological, and riparian vegetation characteristics are investigated within a hierarchy of spatial scales using a range of data sources. A temporal analysis of fluvial geomorphology supports process-based management that targets underlying problems. In this way, effective, sustainable management and restoration solutions can be developed that recognize the underlying drivers of geomorphological change, the constraints imposed on current fluvial processes, and the possible evolutionary trajectories and timelines of change under different future management scenarios. Catchment/river basin planning, natural flood risk management, the identification and appraisal of pressures, and the assessment of restoration needs and objectives would all benefit from a thorough temporal analysis of fluvial geomorphology

Assessing water availability in Mediterranean regions affected by water conflicts through MODIS data time series analysis

Water scarcity is a widespread problem in arid and semi-arid regions such as the western Mediterranean coastal areas. The irregularity of the precipitation generates frequent droughts that exacerbate the conflicts among agriculture, water supply and water demands for ecosystems maintenance. Besides, global climate models predict that climate change will cause Mediterranean arid and semi-arid regions to shift towards lower rainfall scenarios that may exacerbate water conflicts. The purpose of this study is to find a feasible methodology to assess current and monitor future water demands in order to better allocate limited water resources. The interdependency between a vegetation index (NDVI), land surface temperature (LST), precipitation (current and future), and surface water resources availability in two watersheds in southeastern Spain with serious difficulties in meeting water demands was investigated. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and LST products (as proxy of drought), precipitation maps (generated from climate station records) and reservoir storage gauging information were used to compute times series anomalies from 2001 to 2014 and generate regression images and spatial regression models. The temporal relationship between reservoir storage and time series of satellite images allowed the detection of different and contrasting water management practices in the two watersheds. In addition, a comparison of current precipitation rates and future precipitation conditions obtained from global climate models suggests high precipitation reductions, especially in areas that have the potential to contribute significantly to groundwater storage and surface runoff, and are thus critical to reservoir storage. Finally, spatial regression models minimized spatial autocorrelation effects, and their results suggested the great potential of our methodology combining NDVI and LST time series to predict future scenarios of water scarcity.Published versio