Land Cover Fractions Research Articles

Assessing the Utility of Satellite Imagery with Differing Spatial Resolutions for Deriving Proxy Measures of Slum Presence in Accra, Ghana

Little research has been conducted on how differing spatial resolutions or classification techniques affect image-driven identification and categorization of slum neighborhoods in developing nations. This study assesses the correlation between satellite-derived land cover and census-derived socioeconomic variables in Accra, Ghana to determine whether the relationship between these variables is altered with a change in spatial resolution or scale. ASTER and Landsat TM satellite images are each used to classify land cover using spectral mixture analysis (SMA), and land cover proportions are summarized across Enumeration Areas in Accra and compared to socioeconomic data for the same areas. Correlation and regression analyses compare the SMA results with a Slum Index created from various socioeconomic data taken from the Census of Ghana, as well as to data derived from a "hard" per-pixel classification of a 2.4 m Quickbird image. Results show that the vegetation fraction is significantly correlated with the Slum Index (Pearson's r ranges from -0.33 to -0.51, depending on which image-derived product is compared), and the use of a spatial error model improves results (multivariate model pseudo-R 2 ranges from 0.37 to 0.40 by image product). We also find that SMA products derived from ASTER are a sufficient substitute for classification products derived from higher spatial resolution QB data when using land cover fractions as a proxy for slum presence, suggesting that SMA might be more cost-effective for deriving land cover fractions than the use of high-resolution imagery for this type of demographic analysis.

Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data

Spatial and temporal resolution is essential for understanding the spatial and temporal characteristics and dynamics of wetland ecosystems. However, single satellite imagery with both high spatial resolution and high temporal frequency is currently unavailable. Instead, the development of a bi-sensor monitoring technique utilizing spatial details of middle-to-high resolution data and temporal details of coarse spatial resolution data is highly desirable. For the initial work on our time-series bi-sensor wetland mapping, the applicability of multiple endmember spectral mixture analysis (MESMA) using single-date bi-sensor imagery with different orbiting periods was investigated. Landsat-5 Thematic Mapper (TM) and Terra Moderate Resolution Image Spectrometer (MODIS) data were utilized in the Poyang Lake area in China and the Great Salt Lake area in the USA to examine three decisive elements in utilizing MESMA: (1) the method of optimal endmember selection; (2) the threshold between two- and three-endmember models; and (3) the treatment of shade fractions. As a result, we found that (1) the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions of maximum and minimum land-cover fractions and root mean square error (RMSE) within a class (In_CoB), the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions outside of a class (Out_CoB), the ratio of In_CoB to Out_CoB multiplied by the inverse number of spectra within the class (CoBI) and the endmember average RMSE (EAR) were optimal endmember selection methods for the TM maps, whereas CoBI, EAR and minimum average spectral angle (MASA) were optimal endmember selection methods for the MODIS maps; (2) the MODIS maps were more sensitive to change in the two- and three-endmember modelling thresholds than the TM maps; and (3) the addition of shade fractions to dark water fractions were an appropriate shade treatment. This research demonstrated how MESMA can be applied for multi-scale mapping of wetland ecosystems, how the difference in observation dates between the TM and MODIS data affects the agreement in land-cover fractions and how spectral similarity between dark water and shade affects the agreement in land-cover fractions.

Consequences of Uncertainty in Global-Scale Land Cover Maps for Mapping Ecosystem Functions: An Analysis of Pollination Efficiency

Mapping ecosystem services (ESs) is an important tool for providing the quantitative information necessary for the optimal use and protection of ecosystems and biodiversity. A common mapping approach is to apply established empirical relationships to ecosystem property maps. Often, ecosystem properties that provide services to humanity are strongly related to the land use and land cover, where the spatial allocation of the land cover in the landscape is especially important. Land use and land cover maps are, therefore, essential for ES mapping. However, insight into the uncertainties in land cover maps and how these propagate into ES maps is lacking. To analyze the effects of these uncertainties, we mapped pollination efficiency as an example of an ecosystem function, using two continental-scale land cover maps and two global-scale land cover maps. We compared the outputs with maps based on a detailed national-scale map. The ecosystem properties and functions could be mapped using the GLOBCOVER map with a reasonable to good accuracy. In homogeneous landscapes, an even coarser resolution map would suffice. For mapping ESs that depend on the spatial allocation of land cover in the landscape, a classification of satellite images using fractional land cover or mosaic classes is an asset.

Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing

There is an increasing need to understand the dynamics in urbanization not only temporally but also spatially for the improvement of urban environments. In spite of an enormous number of previous studies in urban remote sensing applications, only a few studies have been conducted on the techniques in the quantification, qualification, and visualization of the changes in time-series urban land cover fractions (LCFs) derived through spectral unmixing. To examine the urbanization process in four major cities around the Poyang Lake area in Jiangxi Province, China – Nanchang, Jingdezhen, Yingtan, and Poyang – using a time-series Landsat-5 TM dataset in 1987, 1993, 1999, 2004, and 2009, we investigated: (1) the approach to the derivation of LCFs in urban areas using multi-temporal remotely-sensed data set; and (2) the approach to the summarization and cartographic manipulation of the changes in time-series LCFs. To account for the complex spectral confusion among different land cover materials in built-up areas, the Multiple Endmember Spectral Mixture Analysis (MESMA) was used for unmixing the pixels. The Land Cover Change Intensity (LCCI) was proposed to derive the average daily change rate in terms of the area within a pixel for the land cover classes of green vegetation, non-photosynthetic vegetation and soil, and built-up areas between two consecutive TM observation dates. The dominant LCCI (DLCCI) was proposed to determine in which period the urban areas were developed most rapidly and how intense the urbanization process was in each pixel of the time-series LCCI maps. Our results showed that MESMA could accurately model the pixels in urban areas with complex spectral confusion of different land cover materials. The comparison of derived land cover fractions with socioeconomic statistics disclosed the strong positive correlation between built-up fractions and urban population as well as gross GDP and GDPs in secondary and tertiary industries. LCCI and DLCCI revealed two mechanisms of urbanization, which are new land developments and redevelopments of built-up areas. Consequently, we found that the four cities around the Poyang Lake were urbanized through different mechanisms.

Land cover, climate, and the summer surface energy balance in Phoenix, AZ, and Portland, OR

AbstractChanges in land use and land cover alter the local energy balance and contribute to distinct urban climates. This paper presents a local‐scale above‐canopy study of intra‐urban land cover mixes in two cities to analyse the relative effects of surface morphology and local climate on the surface energy balance (SEB). The study is conducted for urban areas in Phoenix, Arizona, and Portland, Oregon, cities with distinct climates but similarly warm and dry summers. A Local‐Scale Urban Meteorological Parameterization Scheme (LUMPS) is used to analyse the relative contributions of local weather extremes and land cover variations on the urban energy balance. The partitioning of net all‐wave radiation into turbulent sensible and latent heat fluxes as well as heat storage is investigated for a typical dry summer month and two extreme weather scenarios in the two cities. Results of sensitivity analyses show that incoming solar radiation is an important driver of the SEB in LUMPS and should be considered in the generation of climate scenarios. The relationship between individual land cover fractions and SEB fluxes is not clear because of interrelated effects of surface characteristics in the land cover mix. Daytime Bowen ratios vary inversely with vegetation fraction between and within cities for all weather scenarios. Impervious surface cover is positively correlated to the available energy that is partitioned into sensible heat. Cumulative evapotranspiration (ET) is similar for average weather conditions across medium wet sites in Phoenix and Portland but varies more in Portland than in Phoenix under extreme weather conditions. Results suggest that land cover manipulation could offset influences of weather extremes on ET in Portland to a certain degree but not in Phoenix. These findings highlight the importance of spatial and climatic context in the urban design process to mitigate the effects of urbanization. Copyright © 2011 Royal Meteorological Society

Multi-resolution and temporal characterization of land-use classes in a Mediterranean wetland with land-cover fractions

Four different methods for analysing land-use and land-cover fractions at multiple scales, namely composite operator, t-test, Dutilleul’s modified t-test and ternary diagrams of physical models for process pathways, were applied to sets of multi-resolution images in order to evaluate the usefulness of coarse-resolution satellite data (e.g. the Moderate Resolution Imaging Spectroradiometer; MODIS) in obtaining similar results to those obtainable with moderate-resolution satellite data (e.g. Landsat). A spectral-mixture model based on three endmembers (soil, vegetation and water) was used to determine the land-cover fractions of the main land-use classes of a wetland in southeast Spain. The land-use map was produced by applying the unsupervised k-means classification method to the moderate-resolution image. Spatial and temporal changes in the mixture fractions at multiple resolutions and their corresponding land-cover fraction maps were assessed. Three different t-tests (paired-samples, independent-samples and Dutilleul’s modified t-tests) were used to evaluate the effects of pixel aggregation on land-cover fractions and land-use maps in terms of surface-area estimations. Ternary plots of land-use classes characterized by land-cover fractions were used to visualize environmental processes pathways describing temporal changes in the landscape. The results obtained with moderate- and coarse-resolution data were not significantly different from each other. Land-use and land-cover surface-area estimations were not significantly different between Landsat moderate-resolution (30 m) and Landsat resampled coarse-resolution (300 m) data. Spatial autocorrelation had an important effect when comparing Landsat moderate-resolution (30 m) with MODIS coarse-resolution (250 m) data. In order to minimize these effects Dutilleul’s modified t-test was applied for the comparison of Landsat with MODIS image data. However, this test did not reveal significant differences between both datasets, whereas with the ordinary t-test, significant differences were found, which suggest the existence of a bias by spatial autocorrelation that must be taken into account for up-scaling or down-scaling of remote-sensing data. The results suggest the possibility of using coarse-resolution images (MODIS) to characterize environmental changes with a similar accuracy to those of moderate-resolution images (Landsat), as long as potential spatial autocorrelation effects are taken into account. This finding indicates that a substantial reduction in the costs of conducting wetland management and monitoring tasks can be achieved by using free or low-cost coarse-resolution satellite images.

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.

Spectral unmixing model to assess land cover fractions in Mongolian steppe regions

The land cover fractions (LCFs) and spectral reflectance of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare soil were measured at 58 sites in semi-arid and arid regions of Mongolia in the summers of 2005 and 2006. These data sets allowed a detailed assessment of the impact of measurement geometry as represented by the solar zenith angle θs, sensor view zenith angle θv and azimuth view angle ϕ in the estimation of LCF values by means of the spectral unmixing model (SUM). The bidirectional distribution function (BRDF) was fitted to the reflectance data and then used to produce reflectance at various measurement geometries. LCFs from these reflectance data for a given combination of θs, θv, and ϕ were compared with visually determined LCFs. It was found that θs in the range of 30–45° produced a better agreement of LCFs. For θv, the agreement is not very sensitive to the choice of angle for the range 30–70°, although θv=50° showed a slightly better performance. The azimuth view angle does not have strong influences to the LCF estimation, except for the case of ϕ=180° (view toward the sun), which does not allow precise fitting of BRDF function over a tall vegetation site. Overall, this study verified the results of earlier studies obtained mostly for the American continents that SUM is capable of producing LCF estimates accurately and also found that its accuracy was, in general, much better than that by the more traditional approach of the supervised classification method (SCM) applied to images of a digital camera.

Spectral indices for estimating ecological indicators of karst rocky desertification

Karst rocky desertification is a process of land desertification associated with human disturbance of the fragile eco-geological setting of karst ecosystems. The fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil and exposed bedrock are key ecological indicators of the extent and degree of land degradation in karst regions. In this study, field spectral-reflectance measurements were used to develop a karst rocky desertification synthesis index (KRDSI) based on unique spectral features observed in non-vegetation land-cover types (NPV, bare soil and exposed bedrock) and were used to estimate the fractional cover of NPV, bare soil and exposed bedrock. Compared with linear spectral unmixing (LSU) using a tied-spectrum transform, the KRDSI is more consistent with the field measurement of non-vegetation land-cover fractions. This study indicates that ecological indicators of karst rocky desertification can be extracted relatively simply with the combination of vegetation indices and KRDSI values.

Palynological and satellite-based MODIS observations of modern vegetational gradients in China

The MODIS-based estimates of woody, herbaceous and bare cover percentages within a 21 × 21, 11 × 11 and 0.5 × 0.5 km window around pollen sampling sites were attributed to the respective modern pollen spectra. Reconstructions of modern woody cover using the pollen–MODIS reference data set and the modern analogue approach matched well to the original MODIS-based estimates for three selected land-cover fractions. The best of all correspondence between the reconstructed and observed values was obtained for the 21 × 21 km window, including woody ( r = 0.83), herbaceous ( r = 0.80) and bare ( r = 0.90) cover. Discrepancies in the pollen–MODIS cross-validation may be caused by long-distance pollen transport, patchy vegetation, particularly in the densely populated eastern areas and along the steep mountain slopes, under-representation of curtain woody taxa (e.g. Larix and Populus in pollen records), and possible uncertainties in the MODIS data. A series of tests performed with the reference data set including 1452 surface-pollen spectra and representing all vegetation and climatic units of China show the pollen-based reconstructions correspond well to the MODIS-based estimations of land-cover percentages within a 21 × 21 km window, supporting previously obtained results from northern Asia and North America. Results obtained for the smaller window sizes are less accurate, particularly when reconstructing woody cover. The pollen–MODIS-based modern analogue approach can reproduce present-day characteristics of land cover in China with a reasonably high degree of accuracy despite the complexity of the topography and regionally intensive land use. The generally strong correlations encourage the application of the approach to Chinese fossil pollen records.

Remote sensing based evapotranspiration and runoff modeling of agricultural, forest and urban flux sites in Denmark: From field to macro-scale

Evapotranspiration ( E ) and runoff ( R T) was modeled for the island of Sjælland (≈7330 km 2) in Denmark at multiple spatial scales encompassing agricultural, forest and urban land surfaces. National data were used to represent spatial variations in climate, soil properties and lower boundary conditions, and the EOS/MODIS Normalized Difference Vegetation Index ( NDVI) was used to map (a) the temporal development in leaf area index for agricultural fields, (b) a dynamic “canopy” coefficient ( K c) of forests being scaled between its minimum and maximum values for use in the FAO Penman–Monteith equation, and (c) the impervious land cover fraction of urban regions. At field level, the use of local-scale model parameters, NDVI time series and site-specific methodologies to simulate E of the 3 major land surface types (agricultural land, forests and urban regions) explained 67–79% of the observed variability in eddy covariance latent heat fluxes. The “effective” spatial resolution needed to adopt local-scale model parameters for spatial-deterministic hydrological modeling was assessed using a high-spatial resolution (30 m) variogram analysis of the NDVI. The use of the NDVI variogram to evaluate land surface heterogeneity is based on the assumption that sub-class soil heterogeneity can be indirectly represented by the observed spatial variations in NDVI due to its close affiliation with vegetation growth, soil water uptake and evapotranspiration. Multiple spatial resolution water balance simulations were compared to validate the identified effective spatial resolution (500 m) model representation of land cover, NDVI and drainage pattern. Simulated R T of 30 catchments were compared with the fast-flow component of stream discharge data ( Q − Q b) which is insensitive to groundwater abstraction and most sensitive to the spatial land surface representation. A good agreement was observed in the timing and size of peak flows in catchment dominated by agricultural, forest and urban land uses in periods when E has important control on the water balance and soil water percolation to groundwater is negligible (Days 125–300). The presence/absence of pipe drains, urban surface runoff and forest parameterization cause very large differences in the water balance of agricultural, forest and urban regions. The results show that the use of local-scale standard model parameters and NDVI time series representing agricultural, forest and urban land surfaces in physically based hydrological modeling makes it possible to reproduce much of the observed variability (48–73%) in stream flow ( Q − Q b) when data and modeling is applied at an effective spatial resolution capable of representing land surface heterogeneity. In order to further improve the results, (1) advanced spatial parameterization methods are needed to improve the modeling of bare soil E of agricultural fields, (2) the impact of local conditions, such as tree age and nutrient levels, should be used to parameterize the maximum K c used for forest E modeling, (3) high accuracy remote sensing based estimation of vegetation parameters is particularly important during sparsely vegetated conditions, and (4) the use of component stream flow data to evaluate the physical consistency of spatial-deterministic models appears to be feasible and should be further explored.

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well.

An analytical framework for estimating the urban effect on climate

AbstractThe surface energy budget has been used to illustrate the influence of urban landscape on both global and regional climate. This was done using empirical as well as remotely sensed data of components of the surface energy equation.At the global scale, the urban land cover has the least impact on the sensible and latent heat fluxes compared to the other land cover types. Replacing the urban land cover with vegetation did not result in a significant change to the proportionate values of the turbulent fluxes originally due to vegetation. The least impact of current urbanization on the global climate in terms of radiation and surface fluxes is because the urban land cover has the smallest fraction of all the land cover types.The relative importance of the urban landscape at the regional scale was illustrated using the example of Chester County and surroundings near Philadelphia, Pennsylvania, in the USA. The urban effect becomes more important as the fraction of urban land cover to the total increases. This is illustrated by computing turbulent fluxes for 1987, 1988, 1991, 1993 and 1996 over the Chester County area. Urbanization in Chester County and surrounding areas increased from 11% in 1987 to 19% in 1996. In 1996, urban land cover produced the largest proportionate sensible (21.4 Wm−2) and latent (14.2 Wm−2) heat fluxes during winter. During the 1996 summer, urban and vegetation land cover produced the largest proportionate sensible heat (59.2 Wm−2) while urban land cover produced the second largest proportionate latent heat flux (39.5 Wm−2). The implications of this simple analytical study point to the need to account for the urban landscape particularly in regional studies. Copyright © 2009 Royal Meteorological Society

Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy). Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution). Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification). The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.

Hydrologic prediction for urban watersheds with the Distributed Hydrology–Soil–Vegetation Model

AbstractSome relatively straightforward modifications to the Distributed Hydrology–Soil–Vegetation Model (DHSVM) are described that allow it to represent urban hydrological processes. In the modified model, precipitation that falls on impervious surfaces becomes surface runoff, and a spatially varying (depending on land cover) fraction of surface runoff is connected directly to the stream channel, with the remainder stored and slowly released to represent the effects of stormwater detention. The model was evaluated through application to Springbrook Creek watershed in a partially urbanized area of King County, Washington. With calibration, the modified DHSVM simulates hourly streamflow from these urbanized catchments quite well. It is also shown how the revised model can be used to study the effects of continuing urbanization in the much larger Puget Sound basin. Model simulations confirm many previous studies in showing that urbanization increases peak flows and their frequency, and decreases peak flow lag times. The results show that the urbanization parameterizations for DHSVM facilitate use of the model for prediction and/or reconstruction of a range of historic and future changes in land cover that will accompany urbanization as well as other forms of vegetation change. Copyright © 2008 John Wiley & Sons, Ltd.

Remote sensing of within-class change in urban neighborhood structures

Two conceptual models for urban neighborhood change and urban landscape ecology are used to devise an integrative methodology for measuring temporal changes in the urban morphology of Los Angeles, California, based on the techniques of multiple endmember spectral mixture analysis (MESMA), landscape metrics, and fuzzy logic. To illustrate an application of the methodology, the research utilizes two Landsat Thematic Mapper images acquired in 1990 and 2000. Through a wall-to-wall exercise, the paper discusses: (1) how the spatially continuous character of the urban morphology in Los Angeles is analyzed through the use of MESMA technique to capture and quantify within-class changes in land cover at the sub-pixel level; (2) how the magnitude of land cover changes is assessed through fuzzy logic; and (3) how landscape metrics are applied to quantify patterns of change in land cover at the census tract level. The results of the case study indicate that the integration of MESMA, fuzzy logic and landscape metrics can provide a systematic way to derive comparable measures of change in urban neighborhoods in Los Angeles. Further research is however needed to develop more rigorous methods for assessing the accuracy of land cover fractions generated by MESMA. The paper concludes with a discussion of how the proposed methodology may present a step towards a closer integration of remote sensing technology into the reformation of planning policies concerning urban sustainable development.

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval.

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.

GIS analysis of urban schoolyard landcover in three U.S. cities

Although there has been considerable interest in the rejuvenation and greening of inner-city schoolyards for several decades, recent studies on the behavioral and environmental impacts of greenspace, particularly tree cover, suggest that greenspace on schools may be more important than previously understood. However, little is known about the conditions and landcover of urban schoolyards. To understand the structure of the landcover on city schoolyards, this study used Geographic Information System software to classify and compare landcover on 258 U.S. public elementary and middle schools in Baltimore, MD, Boston, MA, and Detroit, MI. For all three cities, schoolyard was found to cover, on average, more than 68% of the school property, which was an average of 1.0–2.5 ha in size. Boston’s schoolyards (circa 1995) were notably smaller from those in Baltimore and Detroit, and they had far more impervious surface. On average, schoolyards were dominated by turf grass and impervious surface, while tree canopy occupied the smallest fraction of schoolyard landcover (approximately 10%). In light of these findings, we conclude by discussing how greening might be achieved on these and other yards.

Mapping northern land cover fractions using Landsat ETM+

The goal of fractional mapping is to obtain land cover fraction estimates within each pixel over a region. Using field, Ikonos and Landsat data at three sites in northern Canada, we evaluate a physical unmixing method against two modeling approaches to map five land cover fractions that include bare, grass, deciduous shrub, conifer, and water along an 1100 km north–south transect crossing the tree-line of northern Canada. Error analyses are presented to assess factors that affect fractional mapping results, including modeling method (linear least squares inversion (LLSI) vs. linear regression vs. regression trees), number of Landsat spectral bands (3 vs. 5), local and distant fraction estimation using locally and globally calibrated models, and spatial resolution (30 m vs. 90 m). The ultimate purpose of this study is to determine if reliable land cover fractions can be obtained for biophysical modeling over northern Canada from a three band, resampled 90 m Landsat ETM+ mosaic north of the tree-line. Of the three modeling methods tested, linear regression and regression trees with five spectral bands produced the best local fraction estimates, while LLSI produced comparable results when unmixing was sufficiently determined. However, distant fraction estimation using both locally and globally calibrated models was most accurate using the three spectral bands available in the Landsat mosaic of northern Canada at 30 m resolution, and only slightly worse at 90 m resolution. While local calibrations produced more accurate fractions than global calibrations, application of local calibration models requires stratification of areas where local endmembers and models are representative. In the absence of such information, globally calibrated linear regression and regression trees to estimate separate fractions is an acceptable alternative, producing similar root mean square error, and an average absolute bias of less than 2%.

Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil

The spatial and spectral variability of urban environments present fundamental challenges to deriving accurate remote sensing products for urban areas. Multiple endmember spectral mixture analysis (MESMA) is a technique that potentially addresses both challenges. MESMA models spectra as the linear sum of spectrally pure endmembers that vary on a per-pixel basis. Spatial variability is addressed by mapping sub-pixel components of land cover as a combination of endmembers. Spectral variability is addressed by allowing the number and type of endmembers to vary from pixel to pixel. This paper presents an application of MESMA to map the physical components of urban land cover for the city of Manaus, Brazil, using Landsat Enhanced Thematic Mapper (ETM+) imagery. We present a methodology to build a regionally specific spectral library of urban materials based on generalized categories of urban land-cover components: vegetation, impervious surfaces, soil, and water. Using this library, we applied MESMA to generate a total of 1137 two-, three-, and four-endmember models for each pixel; the model with the lowest root-mean-squared (RMS) error and lowest complexity was selected on a per-pixel basis. Almost 97% of the pixels within the image were modeled within the 2.5% RMS error constraint. The modeled fractions were used to generate continuous maps of the per-pixel abundance of each generalized land-cover component. We provide an example to demonstrate that land-cover components have the potential to characterize trajectories of physical landscape change as urban neighborhoods develop through time. Accuracy of land-cover fractions was assessed using high-resolution, geocoded images mosaicked from digital aerial videography. Modeled vegetation and impervious fractions corresponded well with the reference fractions. Modeled soil fractions did not correspond as closely with the reference fractions, in part due to limitations of the reference data. This work demonstrates the potential of moderate-resolution, multispectral imagery to map and monitor the evolution of the physical urban environment.