Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial and temporal land cover dynamics, and water depth reflect distribution of key land cover types in riparian areas. Our study area includes the San Miguel and Zanjon rivers in Northwest Mexico. We used a supervised classification and regression tree (CART) algorithm to produce thematic classifications (with accuracies higher than 78%) for 1993, 2002 and 2011 using Landsat TM scenes. Our results suggest a decline in agriculture (32.5% area decrease) and cultivated grasslands (21.1% area decrease) from 1993 to 2011 in the study area. We found constant fluctuation between adjacent land cover classes and riparian habitat. We also found that water depth restricts Riparian Vegetation distribution but not agricultural lands or induced grasslands. Using remote sensing combined with spatial analysis, we were able to reach a better understanding of how riparian habitats are being modified in arid environments and how they have changed through time.

Water availability in Southern Africa is highly variable both in time and space, thereby exposing the region to high risks in water availability. This is further compounded by numerous human activities which have significant impact on water resources. The brunt of the risks associated with water scarcity is particularly heaviest on resourceconstrained farmers who depend largely on rain-fed agriculture for subsistence. With continuously increasing demands on the water resources, the need for a better understanding of the hydrological systems becomes crucial as it forms the gateway for providing reliable information for managing water resources. It is also increasingly becoming more important to address land and water linkages because land use decisions are water use decisions. Operational hydrology provides an insight into the effects of man-made changes, the foreseeable hydrological characteristics at a given site, and the long-term prediction of the future hydrological effects of human activities. This provides for a more holistic approach in managing land and water resources as well as the impact of land use on partitioning rainfall into streamflow. This report discusses the application of the SWAT model to the B72E - F quaternary catchments in the Olifants Water Management Area to assess streamflow generation and the effects of human-environment interactions on the hydrology. Results show an expected correlation between land cover and the hydrologic response where an increase in land cover corresponds to a reduction in the streamflow. Range grass shows a higher reduction in the streamflow followed by forestry with arid land giving the highest increase in streamflow. Prediction in the similar neighbouring and ungauged B72A catchment gives a MAR of 68mm. Additionally, a rigorous analysis of the concepts of a local hydrological model, HDAM, is done with respect to rainfall which is the main driver of the model. Modifications of some of the relationships used in the model are suggested with the potential of streamlining the model and making it more applicable in the region.

The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.

No description available.

The role of remote sensing data for monitoring different parameters in the study of ecosystems has been increasing. Particularly the development of different indices has played a great role to study the properties of vegetation and vegetation dynamics in large countries. In addition to this, satellite rainfall estimate data has been used to study the pattern of precipitation in areas where station rain-gauge data is not available. The Normalized Difference Vegetation Index (NDVI) and rainfall estimates data from the National Oceanic and Atmospheric Administration (NOAA) satellites were used to investigate the spatio-tempotal pattern of precipitation and the response of vegetation to precipitation in Ethiopia from 1996 to 2008. The patterns were studied in different land cover classes using data from the Global Land Cover Network (GLCN). The spatial patternof NDVI and precipitation showed that vegetation responded directly to precipitation. The seasonal patterns showed that there was between 0 to 3 months lag between precipitationand vegetation. However it was not possible to draw conclusion regarding the annual trendsof precipitation and NDVI because of the nature of the NDVI data, which was produced using the 10 day maximum composite values.

The potential of atmospheric dust aerosols to modify the Earth's environment and climate has been recognized for some time. However, predicting the diverse impact of dust has several significant challenges. One is to quantify the complex spatial and temporal variability of dust burden in the atmosphere. Another is to quantify the fraction of dust originating from human-made sources. This thesis focuses on the spatiotemporal characterization of sources and dust outbreaks in Central and East Asia by integrating ground-based data, satellite multi-sensor observations, and modeling. A new regional dust modeling system capable of operating over a span of scales was developed. The modeling system consists of a dust module DuMo, which incorporates several dust emission schemes of different complexity, and the PSU/NCAR mesoscale model MM5, which offers a variety of physical parameterizations and flexible nesting capability. The modeling system was used to perform for the first time a comprehensive study of the timing, duration, and intensity of individual dust events in Central and East Asia. Determining the uncertainties caused by the choice of model physics, especially the boundary layer parameterization, and the dust production scheme was the focus of our study. Implications to assessments of the anthropogenic dust fraction in these regions were also addressed. Focusing on Spring 2001, an analysis of routine surface meteorological observations and satellite multi-sensor data was carried out in conjunction with modeling to determine the extent to which to this integrated data set can be used to characterize the spatiotemporal distribution of dust plumes at a range of temporal scales, addressing the active dust sources in China and Mongolia, mid-range transport and trans-Pacific, long-range transport of dust outbreaks on a case-by-case basis. This work demonstrates that adequate and consistent characterization of individual dust events is central to establishing a reliable climatology, ultimately leading to improved assessments of dust impacts on the environment and climate. This will also help to identify the appropriate temporal and spatial scales for adequate intercomparison between model results and observational data as well as for developing an integrated analysis methodology for dust studies.

The Corredor Bioceanico is a major transportation project connecting the agricultural heartlands of South America to the Atlantic and Pacific coasts. The final link is in southeastern Bolivia - an underdeveloped area that is home to two indigenous groups and globally-significant woodlands and wetlands. Infrastructure developments - comprising a major highway upgrade, revitalized railway services and increased flows along gas pipelines to Brazil - pose major threats to livelihoods and the region's ecological integrity. There are two broad objectives: (i) to map and quantify the spatial patterns of land change using a time-series of coarse and medium resolution satellite imagery; and (ii) to understand the socio-economic and political drivers of change by linking household surveys and interviews with farmers; environmental, climatic, and political data; and classified satellite imagery. Overall, large-scale deforestation has occurred along the Corredor Bioceanico for mechanized commercial production of oil-seed crops such as soybeans and sunflower. The significance of these findings is that agriculture-driven deforestation is pushing into sensitive areas threatening world-renowned ecosystems such as the Chaco, Chiquitano and Pantanal as well as noteworthy national parks. Though quantity remains relatively small compared to other parts of South America, rates of forest loss match or exceed those of more publicized regions such as Rondonia or Mato Grosso, Brazil. Moreover, rates of forest loss are accelerating linearly with time due to policies implemented by incumbent president Evo Morales. Results also show that in the first years of cultivation, pasture is the dominant land-use, but it quickly gives way to intensively cropped farmland. The main findings in terms of percentage area cleared according to forest type is that farmers appear to be favoring transitional forest types on deep and poorly drained soils of alluvial plains. Semi-structured interviews with farmers and representatives of key institutions illustrate that price determined by the global market is not proportionally the most dominant motive driving LULCC in the lowlands of Santa Cruz, Bolivia - an area seen as a quintessential neoliberal frontier.

Changes with land cover and land use are closely integrated with water and other ecological processes at the land surface. Nowhere is that more apparent than in the Edwards Aquifer region of southcentral Texas. The Edwards Aquifer contributing and recharge zones cover approximately 18,000 square kilometers in parts of 15 counties in Texas and includes San Antonio and Austin, the nation's eighth and nineteenth largest cities, respectively. Population growth within the counties that intersect the Edwards Aquifer contributing and recharge zones has taken place over the last two decades, with the logical translation being an expanded infrastructure. This implies that a greater amount of impervious surface coverage and other land cover changes have occurred. This work quantified the changes in land cover within the Edwards Aquifer contributing and recharge zones between the years 1986 and 2000. Increasing trends in impervious surface area and woodland growth were identified. Additionally, a new ArcView software tool was developed to process SSURGO soil data for use within the ArcView SWAT model. Hydrologic modeling for the Upper Sabinal River watershed, located within the Edwards Aquifer region, revealed that the high resolution SSURGO data produces different results when used in place of the existing STATSGO soils data. Finally, an index of urbanization was developed and evaluated to assist investigators in identifying potential areas of urbanization.

Dramatic land-cover changes have occurred in a broad range of spatial and temporal scales over the last decades. Satellite remote sensing, which can observe the earth's surface in a consistent manner, has been playing an important role in monitoring and evaluating land-cover changes. Meanwhile, optical remote sensing, a common approach to acquiring land-cover information, is limited by weather conditions and thus is greatly constrained in areas with frequent cloud cover and rainfall. Recent advances in polarimetric SAR (PolSAR) provide a promising means to extract timely information of land-cover changes regardless of weather conditions. SAR satellite can pass through an area from different orbits, namely ascending orbit and descending orbit. The PolSAR images from the same orbit will have similar backscattering even with different incident angles. But if images are acquired from different orbits, the backscattering will vary greatly, which causes many difficulties to land cover change detection. The proposed algorithms in this study can perform land cover change detection in three situations: 1) repeat-pass images (image from the same orbit and with same incident angle, 2) images from the same orbit but with different incident angle, and 3) images from different orbits. Using images from different orbits will largely reduce the monitoring interval which is important in the surveillance of natural disasters. The present study proposes 1) a sub-pixel automatic registration technique, 2) an automatic change detection technique and 3) an iterative framework to process a time series of PolSAR images that can be applied to the PolSAR images from different orbits. Firstly, automatic registration is crucial to the change detection task because a small positional error will largely degrade the accuracy of change detection. The automatic registration technique is based on the multi-scale Harris corner detector. To improve the efficiency and robustness, the orientation angle differencing method is proposed to reject outliers. This differencing method has been proved effective even in the experiment of using PolSAR images from different orbits when less than 5% of the feature point matches are correct. Secondly, the change detection technique can automatically detect land-cover conversions and classify the newly input image. Hierarchical segmentation has been applied in the change detection which generates objects within the constraint of the previous classification map. Multivariate kernel density estimation is applied to classify newly input PolSAR image. The experiments show that the proposed change detection technique can mitigate the effect of polarimetric orientation shift when the PolSAR images are from different orbits, and it can achieve high accuracy even when complex local deformation is appeared. Lastly, the iterative framework, which integrates the automatic registration and automatic change detection techniques, is proposed to process a time series of PolSAR images. In the iterative process, no obvious decrease of classification accuracy is observed. Therefore, the proposed framework provides a potential treatment to derive land-cover dynamics from a time series of PolSAR images from different orbits. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy

In arid and semiarid environments, various natural and anthropogenic stressors have resulted in land cover change that has negatively impacted the ecological integrity of the landscape. Society, however, relies on many ecological processes and functions provided by the landscape to enhance its wellbeing. The direct and indirect benefits society receives from the landscape are collectively termed "ecosystem services." The overarching goal of this thesis was to examine how the landscape has changed and to analyze how these changes impact the ecosystem services supplied by the landscape. The Upper San Pedro watershed in southeastern Arizona and northern Sonora was used as a case study to link land cover change with an array of ecosystem services to understand the spatiotemporal dynamics of ecosystem service supply. To accomplish this, a multitemporal land cover dataset for the watershed was updated to extend the temporal coverage to 37 years across 5 land cover datasets. Indicators serving as proxy variables for a variety of ecosystem services were assessed for each land cover class. This linkage between land cover and ecosystem services enabled an analysis of the tradeoffs and synergies within the array of services each land cover class can provide. Combined with the multitemporal land cover dataset, the spatiotemporal dynamics of potential ecosystem service supply were analyzed across the watershed for a 37 year period. Rather than examining the impacts of land cover change on the biophysical aspects of the environment, this approach enables land managers and decision makers to explore the implications of a changing landscape on human wellbeing.