Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.     

Insights for managers from modeling species interactions across multiple scales in an idealized landscape

In recent years there has been a shift in biodiversity efforts from protected areas to one of interlinked habitat patches across multiple land tenure types. Much work remains on how managers can intervene in such systems to achieve basic goals. We use an agent-based model of a metapopulation with predator–prey dynamics and density-dependent migration to examine theoretically the capacity of a manager to modify the ecosystem to achieve conservation goals. We explore management strategies aimed at maintaining one of two goals – local or global coexistence of species. To achieve their goal, the manager varies the connectivity between patches based on one of three strategies – the monitoring of predator, prey, or the vegetation carrying capacity of the patches. We find that strategies that lead to highest coexistence monitor mid-tier populations globally. Our goal is to use our model results to advance decision-making in conservation beyond protected areas, typical in today's conservation.     

Spatial pattern analysis for monitoring protected areas

The management of diverse biota within protected areas is affected by both land cover change within a protected area and habitat loss and fragmentation in the surrounding landscape. Satellite images provide a synoptic view of land cover patterns, but the use of such imagery requires careful consideration of sensor type, resolution, extent, and the metrics used to quantify ecologically significant change. We examined these factors for landscape monitoring applications in four small National Parks near Washington, DC: Antietam National Battlefield, Catoctin Mountain Park, Prince William Forest Park and Rock Creek Park. Using 4 m Ikonos, 10 m SPOT, 15 m pan-sharpened Landsat ETM+ and 30 m Landsat ETM+ imagery, the parks and surrounding areas were mapped to National Land Cover system classes. For each park, we examined four methods for defining map extent, including park administrative boundaries, two variable buffer widths, and watershed boundaries, and then analyzed patterns of forest habitat for the maps using a graph theoretic approach (critical dispersal threshold distance) and common landscape metrics (number of patches, percent forest, forest edge density, and forest area-weighted mean patch size). As expected, landscape metrics for maps derived at differing resolutions varied significantly, but map extent often yielded even larger differences. We found that for most applications, coarser scale data (e.g., 30 m Landsat) are adequate for characterizing landscape pattern, although ultimately data from multiple sensors may be appropriate or necessary based on different objectives of landscape monitoring (e.g., mapping single trees vs. forest stands) and the scale at which a resource of interest interacts with the larger landscape (e.g., birds vs. herptiles). Our results provide a strong caution regarding the practical issues associated with combining data sources from multiple satellite sensors for monitoring applications.     

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically-distributed every 0.5° over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

Assessing the landscape context and conversion risk of protected areas using satellite data products

Since the establishment of the first national park (Yellowstone National Park in 1872) and the first wildlife refuge (Pelican Island in 1903), dramatic changes have occurred in both ecological and cultural landscapes across the U.S. The ability of these protected areas to maintain current levels of biodiversity depend, at least in part, on the integrity of the surrounding landscape. Our objective was to quantify and compare the extent and pattern of natural land cover, risk of conversion, and relationships with demographic and economic variables in counties near National Park Service units and U.S. Fish and Wildlife Service refuges with those counties distant from either type of protected area in the coterminous United States. Our results indicate that landscapes in counties within 10 km of both parks and refuges and those within 10 km of just parks were more natural, more intact, and more protected than those in counties within 10 km of just refuges and counties greater than 10 km from either protected area system. However, they also had greater human population density and change in population, indicating potential conversion risk since the percent of landscape protected averaged < 5% in both groups and human population dynamics are primary drivers of change in many landscapes. Conversion outweighed protection by at least two times (Conservation Risk Index > 2) in 76% of counties near both parks and refuges, 81% of counties near just parks, 91% of counties near just refuges, and 93% of distant counties. Thirteen percent of counties in the coterminous U.S. had moderate to high amounts of natural land cover (> 60%), low protection (< 20%), and the greatest change in population (> 20%). Although these areas are not the most critically endangered, they represent the greatest conservation opportunity, need, and urgency. Our approach is based on national level metrics that are simple, general, informative, and can be understood by broad audiences and by policy makers and managers to assess the health of lands surrounding parks and refuges. Regular monitoring of these metrics with satellite data products in counties surrounding protected areas provides a consistent, national level assessment of management opportunities and potentially adverse changes on adjacent lands.     

Landsat urban mapping based on a combined spectral-spatial methodology

Urban mapping using Landsat Thematic Mapper (TM) imagery presents numerous challenges. These include spectral mixing of diverse land cover components within pixels, spectral confusion with other land cover features such as fallow agricultural fields and the fact that urban classes of interest are of the land use and not the land cover category. A new methodology to address these issues is proposed. This approach involves, as a first step, the generation of two independent but rudimentary land cover products, one spectral-based at the pixel level and the other segment-based. These classifications are then merged through a rule-based approach to generate a final product with enhanced land use classes and accuracy. A comprehensive evaluation of derived products of Ottawa, Calgary and cities in southwestern Ontario is presented based on conventional ground reference data as well as inter-classification consistency analyses. Producer accuracies of 78% and 73% have been achieved for urban ‘residential’ and ‘commercial/industrial’ classes, respectively. The capability of Landsat TM to detect low density residential areas is assessed based on dwelling and population data derived from aerial photography and the 2001 Canadian census. For low population densities (i.e. below 3000 persons/km²), density is observed to be monotonically related to the fraction of pixels labeled ‘residential’. At higher densities, the fraction of pixels labeled ‘residential’ remains constant due to Landsat's inability to distinguish between high-rise apartment dwellings and commercial/industrial structures.     

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.     

全球环境变化视角下的土地覆盖分类系统研究综述

全球环境变化需要精确和最新的区域到全球尺度的土地覆盖数据集以支撑生态系统评估、生物多样性保护、气候变化研究和环境建模。然而,在土地覆盖分类数据集建立过程中,建立科学标准的分类系统至关重要,它影响着数据产品的集成与共享,数据的应用领域与范围。通过对区域尺度、全球尺度及可扩展的FAO土地覆盖分类系统进行评述,指出：① 目前国内外没有普遍认可并广泛应用的标准土地覆盖分类系统,这种分类系统的非标准化影响了数据产品的应用以及对土地覆盖变化的监测;② 复合特征是土地覆盖的固有属性,有效地表达与特征量化复合类型是需要不断努力去解决的问题;③ 我国迫切需要建立一套标准的土地覆盖分类系统,一方面能够与国际土地覆盖产品接轨,另一方面充分体现我国的自然环境特征。     

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.     

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

A novel approach to mapping land conversion using Google Earth with an application to East Africa

Effective conservation planning relies on the accurate identification of anthropogenic land cover. However, accessing localized information can be difficult or impossible in developing countries. Additionally, global medium-resolution land use land cover datasets may be insufficient for conservation planning purposes at the scale of a country or smaller. We thus introduce a new tool, GE Grids, to bridge this gap. This tool creates an interactive user-specified binary grid laid over Google Earth's high-resolution imagery. Using GE Grids, we manually identified anthropogenic land conversion across East Africa and compared this against available land cover datasets. Nearly 30% of East Africa is converted to anthropogenic land cover. The two highest-resolution comparative datasets have the greatest agreement with our own at the regional extent, despite having as low as 44% agreement at the country level. We achieved 83% consistency among users. GE Grids is intended to complement existing remote sensing datasets at local scales.     

Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments

Remotely sensed images and processing techniques are a primary tool for mapping changes in tropical forest types important to biodiversity and environmental assessment. Detailed land cover data are lacking for most wet tropical areas that present special challenges for data collection. For this study, we utilize decision tree (DT) classifiers to map 32 land cover types of varying ecological and economic importance over an 8000 km² study area and biological corridor in Costa Rica. We assess multivariate QUEST DTs with unbiased classification rules and linear discriminant node models for integrated vegetation mapping and change detection. Predictor variables essential to accurate land cover classification were selected using importance indices statistically derived with classification trees. A set of 35 variables from SRTM-DEM terrain variables, WorldClim grids, and Landsat TM bands were assessed.

Of the techniques examined, QUEST trees were most accurate by integrating a set of 12 spectral and geospatial predictor variables for image subsets with an overall cross-validation accuracy of 93% ± 3.3%. Accuracy with spectral variables alone was low (69% ± 3.3%). A random selection of training and test set pixels for the entire landscape yielded lower classification accuracy (81%) demonstrating a positive effect of image subsets on accuracy. A post-classification change comparison between 1986 and 2001 reveals that two lowland forest types of differing tree species composition are vulnerable to agricultural conversion. Tree plantations and successional vegetation added forest cover over the 15-year time period, but sometimes replaced native forest types, reducing floristic diversity. Decision tree classifiers, capable of combining data from multiple sources, are highly adaptable for mapping and monitoring land cover changes important to biodiversity and other ecosystem services in complex wet tropical environments.     

Monitoring land cover change and ecological integrity in Canada's national parks

Canada's national parks system includes 43 terrestrial parks covering 3% (276,275 km²) of the country's landmass and representing its full range of natural regions. Considering the vast and often remote areas under protection, Parks Canada Agency envisions Earth Observation technology to be the basis for a Park Ecological Integrity Observing System (Park-EIOS), and integral component of a larger national parks ecological integrity (EI) monitoring program. Park-EIOS is planned for operational use beginning in 2008 and includes coarse filter EI indicators corresponding to landscape pattern, succession and retrogression, net primary productivity, and focal species distributions within parks and their surrounding greater park ecosystems. A primary input to produce all four indicators is a time series of land cover information derived from medium (~ 30 m) resolution, Landsat-class sensors. This paper describes a generic, end-to-end change detection framework developed for Park-EIOS, labelled Automated Multi-temporal Updating through Signature Extension (AMUSE). AMUSE involves radiometric normalization steps, production of a baseline land cover, change vector analysis to identify changed pixels, and a new constrained signature extension approach to update the land cover of changed areas. We present the method and results applied to six pilot parks using time series of Landsat TM/ETM+ imagery from 1985-2005.     

Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity

Maintaining and restoring landscape connectivity is currently a central concern in ecology and biodiversity conservation, and there is an increasing demand of user-driven tools for integrating connectivity in landscape planning. Here we describe the new Conefor Sensinode 2.2 (CS22) software, which quantifies the importance of habitat patches for maintaining or improving functional landscape connectivity and is conceived as a tool for decision-making support in landscape planning and habitat conservation. CS22 is based on graph structures, which have been suggested to possess the greatest benefit to effort ratio for conservation problems regarding landscape connectivity. CS22 includes new connectivity metrics based on the habitat availability concept, which considers a patch itself as a space where connectivity occurs, integrating in a single measure the connected habitat area existing within the patches with the area made available by the connections between different habitat patches. These new metrics have been shown to present improved properties compared to other existing metrics and are particularly suited to the identification of critical landscape elements for connectivity. CS22 is distributed together with GIS extensions that allow for directly generating the required input files from a GIS layer. CS22 and related documentation can be freely downloaded from the World Wide Web.     

ALOS影像数据土地覆盖分类及景观特征研究

通过马氏距离法、最大似然法、支持向量机三种途径对土地覆盖进行分类,以混淆矩阵对分类结果做精度评价,结果显示,最大似然法和支持向量机分类有较好的效果。以最大似然法为例,通过引入归一化植被指数（NDVI）、基于灰度共生矩阵的纹理特征等进行不同特征组合的分类,探讨其对分类的影响。研究表明,NDVI、对比度、均值参与分类后,对分类精度都有不同程度的提高,而三者与原始波段的结合分类精度最高。基于分类结果做景观格局定量分析。结果表明,研究区景观类型较为丰富,以耕地为主导,再加上城镇和农村聚落用地,约占到整个研究区的82%,表明景观所受的人类活动干扰和压力很大、生态风险高。因此,必须强化黑河中游绿洲荒漠区的土地利用规划和管理,适当约束耕地和聚落用地的扩张,提高土地利用效率;要加强生态保护和建设,提高景观的抗干扰能力。     

基于RS与GIS的野鸭湖湿地土地利用/土地覆盖动态变化研究

通过对野鸭湖湿地的Landsat—TM影像和印度的IRS影像进行融合处理，得到卫星影像分类图。结合实地调查，标定土地利用类型，运用ArcView的解译及数据统计功能，分析研究野鸭湖湿地6年来土地利用／土地覆盖的变化。研究结果表明：耕地、居民点及工矿用地面积增加，水域面积减少，湿地生态环境受到严重破坏。其主要是由自然条件、人口和经济增长所致。水域面积、植被覆盖率的减少，使栖息和越冬鸟类丧失了大量的栖息地。为保护湿地环境，应逐步退耕还草、还林；恢复芦苇、沼泽，确保区内生态平衡和系统生态质量不断优化。     

The effects of aggregated land cover data on estimating NPP in northern Wisconsin

Ecosystem models are routinely used to estimate net primary production (NPP) from the stand to global scales. Complex ecosystem models, implemented at small scales (< 10 km²), are impractical at global scales and, therefore, require simplifying logic based on key ecological first principles and model drivers derived from remotely sensed data. There is a need for an improved understanding of the factors that influence the variability of NPP model estimates at different scales so we can improve the accuracy of NPP estimates at the global scale. The objective of this study was to examine the effects of using leaf area index (LAI) and three different aggregated land cover classification products-two factors derived from remotely sensed data and strongly affect NPP estimates-in a light use efficiency (LUE) model to estimate NPP in a heterogeneous temperate forest landscape in northern Wisconsin, USA. Three separate land cover classifications were derived from three different remote sensors with spatial resolutions of 15, 30, and 1000 m. Average modeled net primary production (NPP) ranged from 402 gC m^− 2 year^− 1 (15 m data) to 431 gC m^− 2 year^− 1 (1000 m data), for a maximum difference of 7%. Almost 50% of the difference was attributed each to LAI estimates and land cover classifications between the fine and coarse scale NPP estimate. Results from this study suggest that ecosystem models that use biome-level land cover classifications with associated LUE coefficients may be used to model NPP in heterogeneous land cover areas dominated by cover types with similar NPP. However, more research is needed to examine scaling errors in other heterogeneous areas and NPP errors associated with deriving LAI estimates.     

Assessing the impact of urban land development on net primary productivity in the southeastern United States

The southeastern United States (SE-US) has undergone one of the highest rates of landscape changes in the country due to changing demographics and land use practices over the last few decades. Increasing evidence indicates that these changes have impacted mesoscale weather patterns, biodiversity and water resources. Since the Southeast has one of the highest rates of land productivity in the nation, it is important to monitor the effects of such changes regularly. Here, we propose a remote sensing based methodology to estimate regional impacts of urban land development on ecosystem structure and function. As an indicator of ecosystem functioning, we chose net primary productivity (NPP), which is now routinely estimated from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We used the MODIS data, a 1992 Landsat-based land cover map and nighttime data derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) for the years 1992/1993 and 2000 to estimate the extent of urban development and its impact on NPP. The analysis based on the nighttime data indicated that in 1992/1993, urban areas amounted to 4.5% of the total land surface of the region. In the year 2000, the nighttime data showed an increase in urban development for the southeastern United States of 1.9%. Estimates derived from the MODIS data indicated that land cover changes due to urban development that took place during the 1992-2000 period reduced annual NPP of the southeastern United States by 0.4%. Despite the uncertainties in sensor fusion and the coarse resolution of the data used in this study, results show that the combination of MODIS products such as NPP with nighttime data could provide rapid assessment of urban land cover changes and their impacts on regional ecosystem resources.