Analyzing the Variability of Remote Sensing and Hydrologic Model Evapotranspiration Products in a Watershed in Michigan

The goal of this paper was to statistically explore the spatiotemporal performance of remotely sensed actual evapotranspiration (ETa) datasets and a remotely sensed ensemble in a region that lacks observed data. The remotely sensed datasets were further compared with ETa results from a physically based hydrologic model (Soil and Water Assessment Tool) to examine the differences and determine the level of agreement between the ETa datasets and the model outputs. ETa datasets were compared on temporal (i.e., monthly and seasonal basis) and spatial (i.e., landuse) scales at both watershed and subbasin levels. The results showed a lack of consistent similarities and differences among the datasets when evaluating the monthly ETa variations; however, the seasonal aggregated data presented more consistent similarities and differences during the spring and summer compared to the fall and winter. Meanwhile, spatial analysis of the datasets showed the MOD16A2 500 m ETa product was the most versatile of the tested datasets, being able to differentiate between landuses during all seasons. Furthermore, the use of an averaging ensemble was able to improve overall ETa performance in the study area. This study showed that the remotely sensed ETa products are not similar throughout the year, but the appropriate application periods for different ETa products were identified. Finally, spatial variabilities of the ETa products are more in tune with landuse and climate characteristics.     

Multiscale Indicators of Land Degradation in the Patagonian Monte,Argentina

Depletion of vegetation by overgrazing in arid environments has long-lasting effects on the environmental quality over extended geographic areas. An adequate inspection of habitat changes requires scaled up procedures that would allow assessing end-points of environmental status in broad areas that would be based on processes occurring at the plant canopy level. Our purpose was to find indicators of land degradation–conservation status for use in land monitoring programs and in planning management practices that would be amenable to further up-scaling for use with remotely sensed imagery. In several sites of the Patagonian Monte differing in the impact of grazing management, we evaluated vegetation attributes at three spatial scales. At the population scale, we found that the severity of grazing impact was characterized by the reduction of the palatable grass, P. ligularis, outside and inside shrub canopies. At the vegetation patch scale, we found that land degradation by domestic herbivore impact was characterized by changes in attributes of patch shape (radius, height, internal canopy cover) and patch abundance. At the plant community scale, we found that the structure of the plant canopy as described using Fourier analysis of cover data changed after long-term grazing impact consistently with the modifications in plant population and patch structures. We present a conceptual multiscale scenario of structural changes triggered by domestic herbivore impact, and quantitative indicators of plant structure and processes useful to develop management strategies of the Patagonian-Monte that would conserve its natural habitats. The developed end-points are also amenable for use in land conservation assessment through remotely sensed imagery.     

Survey on environmental monitoring requirements of European ports

A group of 26 European ports was interviewed to understand their requirements for environmental information and to establish how widespread the use of Earth Observation (EO) data was amongst them. Aspects covered by the research included port profile characterisation, environmental management activities, environmental needs and current monitoring practices. The study reflected the diversity amongst European ports and their environmental performances. Most of the ports were publicly owned and located in estuaries and rivers. General cargo was the most popular commodity handled. Practically all the ports had an environmental policy in place and half of them had been accredited by an external body. The main environmental parameters that ports required to be monitored were marine related issues (currents, waves and tide), water quality, meteorological parameters, turbidity and sediment processes. The principal driver for monitoring was maintaining key port operations, followed by legislation, and local responsibilities. Ports in general collected their own data in situ and only one-third had used remotely sensed data (data from Earth Observation satellites or from airplane mounted sensors) for environmental purposes. Half of them used computer modelling. This study was conducted within the framework of the European funded port environmental information collector project (PEARL).     

Towards an Integrated Approach to Natural Hazards Risk Assessment Using GIS: With Reference to Bushfires

This paper develops a GIS-based integrated approach to risk assessment in natural hazards, with reference to bushfires. The challenges for undertaking this approach have three components: data integration, risk assessment tasks, and risk decision-making. First, data integration in GIS is a fundamental step for subsequent risk assessment tasks and risk decision-making. A series of spatial data integration issues within GIS such as geographical scales and data models are addressed. Particularly, the integration of both physical environmental data and socioeconomic data is examined with an example linking remotely sensed data and areal census data in GIS. Second, specific risk assessment tasks, such as hazard behavior simulation and vulnerability assessment, should be undertaken in order to understand complex hazard risks and provide support for risk decision-making. For risk assessment tasks involving heterogeneous data sources, the selection of spatial analysis units is important. Third, risk decision-making concerns spatial preferences and/or patterns, and a multicriteria evaluation (MCE)-GIS typology for risk decision-making is presented that incorporates three perspectives: spatial data types, data models, and methods development. Both conventional MCE methods and artificial intelligence-based methods with GIS are identified to facilitate spatial risk decision-making in a rational and interpretable way. Finally, the paper concludes that the integrated approach can be used to assist risk management of natural hazards, in theory and in practice.     

Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach

The increasing availability of multi‐scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model‐assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model, which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses predefined boundary conditions that are unique to each pixel for the “hot” and “cold” reference conditions. The SSEBop model was used for computing ET for 12 years (2000‐2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEBop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000‐2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the predefined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.     

MONITORING SUSPENDED PARTICULATE MATTER IN PUERTO RICO: FIELD MEASUREMENTS AND REMOTE SENSING1

ABSTRACT: The spatial distribution of suspended particulatematter (SPM) was estimated in Mayagüez Bay on the west coast of Puerto Rico by using traditional ship board measurements and remotely sensed data acquired over four days during January 1990. This effort was part of a joint project between NASA and the University of Puerto Rico to develop techniques to monitor the water quality of a Caribbean coastal zone. This paper presents the methods and algorithms developed to map and analyze short term changes in the source and spatial distribution of SPM in Mayagüez Bay by using remotely sensed data acquired by the Calibrated Airborne Multispectral Scanner (CAMS). A PC-based data acquisition system was developed to record continuous ship measurements of select in- water variables. Spectral reflectances derived from CAMS red and near-IR data were corrected for atmospheric effects and then used to generate maps of SPM. These maps displayed SPM plumes associated with the mouths of the bay's three river systems. Significant day-to-day differences in the spatial characteristics were observed, suggesting that changes in river discharge occurred. However, an analysis of estimated river discharge, sediment yield, local wind field, and thermal river plume indicates that observed sediment plumes result primarily from wind-driven resuspension events.     

The consequences of land use change on nutrient exports: a regional scale assessment in south-west Victoria, Australia

Estimation of nutrient load production based on multi-temporal remotely sensed land use data for the Glenelg-Hopkins region in south-west Victoria, Australia, is discussed. Changes in land use were mapped using archived Landsat data and computerised classification techniques. Land use change has been rapid in recent history with 16% of the region transformed in the last 22 years. Total nitrogen and phosphorus loads were estimated using an export coefficient model. The analysis demonstrates an increase in modelled nitrogen and phosphorus loadings from 1980 to 2002. Whilst such increases were suspected from past anecdotal and ad-hoc evidence, our modelling estimated the magnitude of such increases and thus demonstrated the enormous potential of using remote sensing and GIS for monitoring regional scale environmental processes.     

Techniques for accuracy assessment of tree locations extracted from remotely sensed imagery

Remotely sensed imagery is becoming a common source of environmental data. Consequently, there is an increasing need for tools to assess the accuracy and information content of such data. Particularly when the spatial resolution of imagery is fine, the accuracy of image processing is determined by comparisons with field data. However, the nature of error is more difficult to assess. In this paper we describe a set of tools intended for such an assessment when tree objects are extracted and field data are available for comparison. These techniques are demonstrated on individual tree locations extracted from an IKONOS image via local maximum filtering. The locations of the extracted trees are compared with field data to determine the number of found and missed trees. Aspatial and spatial (Voronoi) analysis methods are used to examine the nature of errors by searching for trends in characteristics of found and missed trees. As well, analysis is conducted to assess the information content of found trees.     

Spatial analysis of resources and environmental carrying capacity in Iran

Urbanization and mass movement of the population from rural areas and small cities to megacities have led to environmental, economic, and social problems in Iran. In dealing with these challenges, assessing resource and environmental carrying capacity (RECC) is considered an effective method to leverage space and capital to achieve sustainable development. This study aimed to rank the provincial RECC in Iran. Toward this purpose, environmental indices were generated from remotely sensed and statistical census data. Then, the provinces were scored in terms of environmental, economic, and infrastructural carrying capacities, and RECC using the mean variance analysis method. Results demonstrated that in most areas, there is no relationship between economic and infrastructural capacities and development. Statistically, a correlation coefficient of −0.53 between economic and environmental carrying capacities indicated excessive use of environmental capacities. Moreover, the spatial distribution pattern of environmental, economic, and infrastructural carrying capacity was entirely heterogeneous between the provinces; there was a northeast–southwest pattern in terms of infrastructural capacity and an economic pattern from north to south. The distribution pattern of RECC is most consistent with the environmental capacity, pointing at the high weight of the indicators of the RECC model. In conclusion, this research offers a new vision for policymakers and provides a theoretical and applicable framework for implementing sustainable strategies in land-use planning. It is recommended that the RECC concept and tools can be used not only for planning but also for measuring the efficiency of spatial development programs and establishing land balances in the region.     

Upscaling of Environmental Information: Support of Land-Use Management Decisions by Spatio-Temporal Regionalization Approaches

This article presents several case studies in southwest Germany, which aimed to support land use management decisions by a process-oriented statistical upscaling of point-related environmental monitoring data to the landscape scale. When techniques of data subsetting were used in a sensible way and corresponding to the appropriate scale for the evaluation envisaged, multiple linear regression offered a data mining technique which was able to spatially predict relatively complex environmental patterns with parsimonious, interpretable and accurate models, whereby different evaluation scales were best represented by different DTM resolutions. Scenario models based upon the regression formulas were a valuable tool for visualizing management options and evaluating management impacts (tree species selection) on soil functions (carbon storage), which qualifies the presented methodology as a useful aid in decision making. Such upscaling techniques may be used for forecasting long-term effects of ecosystem management, but they provided no information on temporal dynamics. Therefore, time trends of point information on soil solution data were scaled by linking them to soil chemical data which was available in higher spatial resolution, using both statistical and process-oriented methods.     

Spectrally Driven Classification of High Spatial Resolution,Hyperspectral Imagery: A Tool for Mapping In-Stream Habitat

Streams represent an essential component of functional ecosystems and serve as sensitive indicators of disturbance. Accurate mapping and monitoring of these features is therefore critical, and this study explored the potential to characterize aquatic habitat with remotely sensed data. High spatial resolution, hyperspectral imagery of the Lamar River, Wyoming, USA, was used to examine the relationship between spectrally defined classes and field-mapped habitats. Advantages of this approach included enhanced depiction of fine-scale heterogeneity and improved portrayal of gradational zones between adjacent features. Certain habitat types delineated in the field were strongly associated with specific image classes, but most included areas of diverse spectral character; spatially buffering the field map polygons strengthened this association. Canonical discriminant analysis (CDA) indicated that the ratio of the variability among groups to that within a group was an order of magnitude greater for spectrally defined image classes (20.84) than for field-mapped habitat types (1.82), suggesting that unsupervised image classification might more effectively categorize the fluvial environment. CDA results also suggested that shortwave-infrared wavelengths were valuable for distinguishing various in-stream habitats. Although hyperspectral stream classification seemed capable of identifying more features than previously recognized, the technique also suggested that the intrinsic complexity of the Lamar River would preclude its subdivision into a discrete number of classes. Establishing physically based linkages between observed spectral patterns and ecologically relevant channel characteristics will require additional research, but hyperspectral stream classification could provide novel insight into fluvial systems while emerging as a potentially powerful tool for resource management.     

A Conceptual Framework for Selecting and Analyzing Stressor Data to Study Species Richness at Large Spatial Scales

/ In this paper we develop a conceptual framework for selectingstressor data and analyzing their relationship to geographic patterns ofspecies richness at large spatial scales. Aspects of climate and topography,which are not stressors per se, have been most strongly linked withgeographic patterns of species richness at large spatial scales (e.g.,continental to global scales). The adverse impact of stressors (e.g., habitatloss, pollution) on species has been demonstrated primarily on much smallerspatial scales. To date, there has been a lack of conceptual developmenton how to use stressor data to study geographic patterns of speciesrichness at large spatial scales.The framework we developed includes four components: (1) clarification of theterms stress and stressor and categorization of factors affecting speciesrichness into three groups-anthropogenic stressors, natural stressors, andnatural covariates; (2) synthesis of the existing hypotheses for explaininggeographic patterns of species richness to identify the scales over whichstressors and natural covariates influence species richness and to providesupporting evidence for these relationships through review of previousstudies; (3) identification of three criteria for selection of stressor andcovariate data sets: (a) inclusion of data sets from each of the threecategories identified in item 1, (b) inclusion of data sets representingdifferent aspects of each category, and (c) to the extent possible, analysisof data quality; and (4) identification of two approaches for examiningscale-dependent relationships among stressors, covariates, and patterns ofspecies richness-scaling-up and regression-tree analyses.Based on this framework, we propose 10 data sets as a minimum data base forexamining the effects of stressors and covariates on species richness atlarge spatial scales. These data sets include land cover, roads, wetlands(numbers and loss), exotic species, livestock grazing, surface water pH,pesticide application, climate (and weather), topography, and streams.KEY WORDS: Anthropogenic impacts; Biodiversity; Environmental gradients;Geographic information systems; Hierarchy     

Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem

Geospatial information technology is changing the nature of fire mapping science and management. Geographic information systems (GIS) and global positioning system technology coupled with remotely sensed data provide powerful tools for mapping, assessing, and understanding the complex spatial phenomena of wildland fuels and fire hazard. The effectiveness of these technologies for fire management still depends on good baseline fuels data since techniques have yet to be developed to directly interrogate understory fuels with remotely sensed data. We couple field data collections with GIS, remote sensing, and hierarchical clustering to characterize and map the variability of wildland fuels within and across vegetation types. One hundred fifty six fuel plots were sampled in eight vegetation types ranging in elevation from 1150 to 2600 m surrounding a Madrean 'sky island' mountain range in the southwestern US. Fuel plots within individual vegetation types were divided into classes representing various stages of structural development with unique fuel load characteristics using a hierarchical clustering method. Two Landsat satellite images were then classified into vegetation/fuel classes using a hybrid unsupervised/supervised approach. A back-classification accuracy assessment, which uses the same pixels to test as used to train the classifier, produced an overall Kappa of 50% for the vegetation/fuels map. The map with fuel classes within vegetation type collapsed into single classes was verified with an independent dataset, yielding an overall Kappa of 80%.     

Assessing Land-Use Impacts on Natural Resources

/ Much information is available on changes that occur in natural resources from both spatially-explicit data on environmental conditions and models of the interactions of these conditions and resources with human activities. The strategy for assessing land-use impacts on natural resources developed in this paper provides a framework for using relevant data and models to address questions of how management practices can promote both use and protection of resources. This assessment strategy integrates spatially explicit environmental data using geographic information systems (GIS) with computer models that simulate changes in land cover in response to land-use impacts. The computer models also simulate susceptibility of species to changes in habitat suitability and landscape patterns. The approach is applied to management of limestone barrens on the Oak Ridge Reservation in East Tennessee. Potential limestone barrens habitats are identified by overlaying appropriate soils, geology, slope, and land-use/land-cover conditions. Their validity is tested against known sites containing rare species that occur in these habitats. The location of habitats at risk in the aftermath of human activities is determined by using an available area model that identifies the size and proximity of sites that particular types of species can no longer use as habitat. The resulting risk map can be used in land management planning. The approach uses readily available in situ and remotely sensed data and is applicable to a wide range of locations and land-use scenarios. This approach can be refined based on needs identified by land managers and on the sensitivity of the results to the resolution of available resource information.KEY WORDS: Land management; Assessment; Habitat characterization; Limestone barrens; Ecological modeling; Geographic information systems     

A landscape ecological approach to address scaling problems in conservation management and monitoring

Management of many African game reserves is today often still an art based on experience and intuition, rather than a science. Decision-making is based on an informal integration of accumulated individual knowledge and keen field observations. Data are generally poorly captured and curated. Until fairly recently, denominators of biological parameters (such as the unit of land or unit of plant production used as measurement) have generally been treated as being homogenous. The patchiness of landscapes and the issue of ecological scaling were ignored, often because of a lack of appropriate technical tools. The ecological data available on the 49,000-ha Songimvelo Game Reserve (SGR) result from a number of discrete survey and monitoring projects undertaken by different researchers, with different objectives, at different spatial and temporal scales. A landscape ecological approach towards research and monitoring is appropriate for an area of the size and diversity of the SGR. A combination of a database approach and spatial representation was used to consolidate and integrate data across temporal and spatial scales. Herbivore spatial and temporal distribution patterns were explored across three spatial scales. An understanding was achieved of the importance of landscape patchiness in controlling resource availability for herbivores. This insight is important in guiding management and monitoring of the SGR by placing perceived patch overutilization in its proper landscape context. The landscape ecological approach bridges the traditional scale-independent view to a more contemporary scale-related understanding of ecosystem diversity and functioning.     

Land Use Change and Land Degradation in Southeastern Mediterranean Spain

The magnitude of the environmental and social consequences of soil erosion and land degradation in semiarid areas of the Mediterranean region has long been recognized and studied. This paper investigates the interrelationship between land use/cover (LULC) changes and land degradation using remotely sensed and ancillary data for southeastern Spain. The area of study, the Xaló River catchment situated in the north of the Alicante Province, has been subjected to a number of LULC changes during the second half of the 20th century such as agricultural abandonment, forest fires, and tourist development. Aerial photographs dating back to 1956 were used for the delineation of historic LULC types; Landsat ETM+ data were used for the analysis and mapping of current conditions. Two important indicators of land degradation, namely, susceptibility to surface runoff and soil erosion, were estimated for the two dates using easily parametrizable models. The comparison of 1956 to 2000 conditions shows an overall “recuperating” trend over the catchment and increased susceptibility to soil erosion only in 3% of the catchment area. The results also identify potential degradation hot-spots where mitigation measures should be taken to prevent further degradation. The readily implemented methodology, based on modest data requirements demonstrated by this study, is a useful tool for catchment to regional scale land use change and land degradation studies and strategic planning for environmental management.     

HYDROLOGIC MODELING OF LAND PROCESSES IN PUERTO RICO USING REMOTELY SENSED DATA1

ABSTRACT: An integrated, multi-disciplinary effort to model land processes affecting Mayaguez Bay in western Puerto Rico is described. A modeling strategy was developed to take advantage of remotely sensed data. The spatial, interannual, and seasonal variability of sediment discharges to the bay were also evaluated. Classified images of remotely sensed data revealed the spatial distribution and quantities of land use classes in the region and aided in the discretization of the watershed into homogeneous regions. These regions were modeled using a geomorphic modeling technique based upon spatially averaged parameters. Simulation results from the modeling effort compared favorably with observations at two locations within the watershed. Results showed that runoff and sediment loads from the area exhibit a marked seasonal trend and that deforested areas located in the foothill regions of the watershed contribute a disproportionate share of the sediment load to the bay. In years when rainfall distributions are uniformly distributed over the area, the sediment yields may be up to 100 percent higher than years when the rainfall is concentrated in the heavily forested mountainous regions.     

IMPROVED RAINFALL/RUNOFF ESTIMATES USING REMOTELY SENSED SOIL MOISTURE1

ABSTRACT: Remotely sensed soil moisture data measured during the Southern Great Plains 1997 (SGP97) experiment in Oklahoma were used to characterize antecedent soil moisture conditions for the Soil Conservation Service (SCS) curve number method. The precipitation‐adjusted curve number and the soil moisture were strongly related (r²= 0.70). Remotely sensed soil moisture fields were used to adjust the curve numbers and the runoff estimates for five watersheds, in the Little Washita watershed; the results ranged from 2.8 km² to 601.6 km². The soil moisture data were applied at two spatial scales, a finer one (800 m) measuring spatial resolution and a coarser one (28 km). The root mean square error (RMSE) and the mean absolute error (MAE) of the runoff estimated by the standard SCS method was reduced by nearly 50 percent when the 800 m soil moisture data were used to adjust the curve number. The coarser scale soil moisture data also significantly reduced the error in the runoff predictions with 41 percent and 28 percent reductions in MAE and RMSE, respectively. The results suggest that remote sensing of soil moisture, when combined with the SCS method, can improve rainfall runoff predictions at a range of spatial scales.     

Environmental mapping based on spatial variability

Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Multiple schemes of image classification used in environmental mapping are either traditionally statistical or heuristic. While the former methods do not take account of spatial variability in space and aerial data, the latter ones does not lend themselves to optimal solutions we present. Novel probabilistic models of piecewise-homogeneous images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Parameters of the model are estimated by using a stochastic approximation technique. Its convergence to the desired values is studied experimentally. Addition of spatial attributes appears to be necessary in most areas where the differences in spatial data between regions in the image occur. Experiments in generating the pairs of images and environmental maps and in segmenting the simulated as well as real images are discussed.     

A Multi-scale Spatial Analysis of Native and Exotic Plant Species Richness Within a Mixed-Disturbance Oak Savanna Landscape

Impacts of human land use pose an increasing threat to global biodiversity. Resource managers must respond rapidly to this threat by assessing existing natural areas and prioritizing conservation actions across multiple spatial scales. Plant species richness is a useful measure of biodiversity but typically can only be evaluated on small portions of a given landscape. Modeling relationships between spatial heterogeneity and species richness may allow conservation planners to make predictions of species richness patterns within unsampled areas. We utilized a combination of field data, remotely sensed data, and landscape pattern metrics to develop models of native and exotic plant species richness at two spatial extents (60- and 120-m windows) and at four ecological levels for northwestern Ohio’s Oak Openings region. Multiple regression models explained 37–77 % of the variation in plant species richness. These models consistently explained more variation in exotic richness than in native richness. Exotic richness was better explained at the 120-m extent while native richness was better explained at the 60-m extent. Land cover composition of the surrounding landscape was an important component of all models. We found that percentage of human-modified land cover (negatively correlated with native richness and positively correlated with exotic richness) was a particularly useful predictor of plant species richness and that human-caused disturbances exert a strong influence on species richness patterns within a mixed-disturbance oak savanna landscape. Our results emphasize the importance of using a multi-scale approach to examine the complex relationships between spatial heterogeneity and plant species richness.