Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery
Introduction
Densely populated rural, urban and suburban landscapes now cover as great an extent of Earth's land surface as do tropical rainforests and many other globally important ecosystems (Achard et al., 2002, Ellis, 2004, Foley et al., 2003). Ecosystem processes and their spatial patterns within these anthropogenic landscapes are profoundly altered by human activity and contribute disproportionately more per unit area to global changes in climate, biogeochemical cycles and biodiversity (Foley et al., 2005, Hope et al., 2003, Kalnay and Cai, 2003, Kaye et al., 2004, Matson et al., 1997, Vitousek et al., 1997). Given their global impacts and the fact that most humans live within them (∼5.8 of 6.2 billion persons live in areas with ≥ 25 persons km− 2; Oak Ridge National Laboratory, 2004), the measurement and mediation of long-term ecological changes within densely populated landscapes are a matter of serious global, regional, and local concern (Foley et al., 2005, Vitousek et al., 1997).
In comparison with deforestation, urban expansion, and other extensive landscape transformations that often precede dense human occupation, the causes and consequences of long-term ecological changes within densely populated landscapes are only beginning to be understood (Foster, 1992, Green et al., 2005, Grimm et al., 2000, Heilig, 1994, Hope et al., 2003, Kaye et al., 2004, Lambin et al., 2001, Turner et al., 1994). This is not surprising, considering that landscape transformations within densely populated landscapes incorporate a wide variety of complex land management practices that are characterized by fine-scale changes in landscape structure (< 30 m) caused by the creation, transformation, and abandonment of anthropogenic features with distinct boundaries, such as buildings, roads, yards and small agricultural plots (Ellis et al., 2000a, Foster, 1992, Jensen and Cowen, 1999). These complex fine-scale changes in land use are a challenge to measure by conventional remote sensing approaches (Cihlar and Jansen, 2001, Forster, 1985, Guindon et al., 2004, Lobell and Asner, 2004, Price, 2003, Rindfuss et al., 2004, Thomas et al., 2003, Woodcock and Strahler, 1987) and are usually left out of global and regional ecological change estimates, potentially introducing substantial errors into these (Easterling, 1997, Houghton, 2003, House et al., 2003, Hurtt et al., 2003, Johnes and Butterfield, 2002, Turner et al., 1994).
Long-term ecological changes within anthropogenic landscapes are the combined result of changes in landscape structure, land management practices, and ecosystem processes (Binford et al., 2004, Ellis, 2004, Grimm et al., 2000, Kaye et al., 2004, Matson et al., 1998, Rindfuss et al., 2004, Turner et al., 1994). Usually, these are measured by different methods, each with different land units, and the results are then integrated to estimate ecological change (review by Rindfuss et al., 2004). However, the simplest way to link these measurements is to use the same land units when measuring changes in landscape structure, when obtaining land management data by interviewing local land managers, and when sampling and measuring ecological parameters in the field (Ellis, 2004). To accomplish this, landscapes must first be stratified into ecologically distinct features identifiable to both land managers and ecologists in the field and in imagery, a task best accomplished by field-validated interpretation of ecologically distinct features in high spatial resolution imagery (≤ 1 m; Ellis, 2004, Jensen and Cowen, 1999, Thomas et al., 2003). Moreover, to measure ecological changes over the long term (> 50 y) and across the full range of anthropogenic landscapes, from rural to urban, from floodplain to mountainous, and from pre-industrial to contemporary, a standardized a priori ecological classification procedure is necessary that can produce consistent results from historical aerial photographs and other sources of high spatial resolution imagery, including IKONOS and Quickbird (Cihlar and Jansen, 2001, Jansen and Gregorio, 2002, Kadmon and Harari-Kremer, 1999, Sawaya et al., 2003, Thomlinson et al., 1996).
This study applies the first standardized fine-scale ecological mapping procedure designed explicitly for densely populated landscapes by measuring long-term ecological changes, circa 1950 to 2002, within six densely populated ecological research sites across rural China and in urban and suburban Baltimore, Maryland, USA. Consistent with the ecosystem concept, which combines biotic and abiotic components within a single unit (the ecosystem), our procedure stratifies landscapes into ecologically distinct units (ecotopes) based on a combination of biotic and abiotic classification terms (Ellis et al., 2000a, Klijn and Udo De Haes, 1994). The procedure maps ecotope features based on relatively stable boundaries between ecologically distinct classes of land management and vegetation cover observable in the field at ground level, facilitating ecological fieldwork and land management interviews and providing a consistent mapping product from the complex layered mixture of land cover and land use that predominates in densely populated landscapes (Fig. 1; Sawaya et al., 2003, Jensen and Cowen, 1999). To our knowledge, all existing a priori standards for thematic environmental mapping are either lower resolution pixel based methods (30–1000 m; e.g. Cruickshank and Tomlinson, 1996, Hansen et al., 2000, Homer et al., 2004, Latifovic et al., 2004) or are site- or sensor-based systems that were not designed for consistent long-term change measurements across different regions of the world prior to the 1990s (e.g. Akbari et al., 2003, Anderson et al., 1976, Freeman and Buck, 2003, Kadmon and Harari-Kremer, 1999, Lu et al., 2004, Thomas et al., 2003, Thomlinson et al., 1999). Moreover, we know of no other existing ecological mapping system designed specifically to stratify anthropogenic landscapes into ecologically distinct features that are as useful for surveying local land management practices as they are for sampled ecological measurements in the field.
There are several key challenges in measuring fine-scale ecological changes based on field-validated feature-based mapping. First of all, like land management surveys and ecological sampling in the field, fine-scale mapping is highly resource-intensive. As a result, regional measurements of fine-scale ecological changes are best accomplished by the parsimonious application of these methods within regionally stratified sampling designs linked to data obtained by coarser-resolution remote sensing, such as area frame sampling (Achard et al., 2002, Cihlar et al., 2000, Ellis, 2004, Gallego, 2004, Gallego et al., 1994, Hurtt et al., 2003, Price, 2003, Stehman, 2005). Our fine-scale mapping procedure was therefore designed expressly for application within sample cells selected by regional analysis, so that fine-scale measurements within sample cells, such as crop area, tree biomass, and fertilizer inputs can be linked directly with regional remote sensing data to estimate the causes and consequences of fine-scale ecological changes at regional scales (Ellis, 2004).
Another issue in measuring fine-scale ecological changes is that changes measured by comparing current and historical maps are highly sensitive to “false change” errors caused by misregistration between maps (Foody, 2002, Townshend et al., 1992). This error can be avoided by estimating changes in ecological map class areas across entire sample cells, if sample cells are large enough (Wang & Ellis, 2005b). Another major source of error is disagreement between trained interpreters in both feature mapping (shape-based error) and feature classification, even when maps are field-validated by interpreters (Cherrill and McClean, 1999, Ellis and Wang, submitted for publication, Green and Hartley, 2000, Powell et al., 2004). Though interpreter error is unavoidable, it can be reduced by standardized collaborative training to calibrate results across interpreters, by scale-explicit rules for mapping and classification, and by the continuous centralized supervision of local mapping efforts (Cherrill and McClean, 1999, Cherrill et al., 1995, Ellis and Wang, submitted for publication, Powell et al., 2004). Most importantly, interpreter error in map class area estimates can be quantified to predict conservative error intervals that prevent false detection of changes and differences in map class areas (Type I error; Ellis and Wang, submitted for publication, Schenker and Gentleman, 2001).
This study will demonstrate that fine-scale change measurements reveal substantial and often unexpected long-term ecological changes that would not be observable by coarser scale approaches. The general utility of a priori hierarchical ecological classification will be established by comparing land form, use and cover across environmentally diverse rural and urban landscapes across China and in the USA, and by comparing long-term changes in land use and land cover across sites. The causes and consequences of pronounced long-term changes in land use and land cover across sites are then investigated using the more detailed information available in fine-scale ecotope maps of densely populated landscapes.
Section snippets
Sites and imagery
Six square 1 km2 sites were selected for study across a broad range of environmental, developmental, and population density conditions within existing ecological research sites in the USA and China (Table 1). Prior to site selection, regions were stratified into 500 × 500 m cells by imposing a 500 m2 sampling frame across the landscape. This provided sample units practical both for fieldwork and integration with regional and global remote sensing data (Ambrosio Flores and Iglesias Martinez, 2000,
Changes in fine-scale ecological features
Ecotope maps revealed fine-scale landscape heterogeneity within all sites and demonstrated increases in this heterogeneity over time (Fig. 2, Table 5). The total number, perimeter and size of ecotope features varied greatly, but the median size of ecotope features was always small, < 0.2 ha within all sites, with a cross-site median of 520 m2, explaining the large number and extensive edges of features in all sites (Table 5). The diversity of ecologically distinct anthropogenic landscape
The global importance of fine-scale ecological change
The results of this study confirm that fine-scale ecological changes within densely populated landscapes are both abundant and complex, and that the complexity of these landscapes has generally increased over time. Though land cover changed substantially across every site, by 20% to 50% of total site area, most of this change occurred at very fine scales, in patches smaller than 0.4 ha in all sites, and in patches smaller than 0.1 ha in two sites (Table 6, Fig. 6). These fine-scale changes are
Acknowledgments
This material is based upon the work supported by the US National Science Foundation (NSF) under Grant DEB-0075617 awarded to Erle C. Ellis in 2000. Work in the Baltimore area was inspired and supported by Richard Pouyat of the USDA Forest Service, Northern Global Change Program and Research Work Unit (NE-4952) and the Center for Urban Environmental Research and Education at the University of Maryland, Baltimore County (US Environmental Protection Agency grant DEB 97-14853), and the Baltimore
References (84)
- et al.
Determination of deforestation rates of the world's humid tropical forests
Science
(2002) - et al.
Analyzing the land cover of an urban environment using high-resolution orthophotos
Landscape and Urban Planning
(2003) - et al.
Land cover estimation in small areas using ground survey and remote sensing
Remote Sensing of Environment
(2000) - et al.
A land use and land cover classification system for use with remote sensor data
(1976) - et al.
Sampling design for an integrated socioeconomic and ecological survey by using satellite remote sensing and ordination
PNAS
(2004) - Center for International Earth Science Information Network (CIESIN), International Food Policy Research Institute...
- et al.
The reliability of ‘Phase 1’ habitat mapping in the UK: The extent and types of observer bias
Landscape and Urban Planning
(1999) - et al.
A comparison of land cover types in an ecological field survey in Northern England and a remotely sensed land cover map of Great Britain
Biological Conservation
(1995) - et al.
From land cover to land use: A methodology for efficient land use mapping over large areas
Professional Geographer
(2001) - et al.
Selecting representative high resolution sample images for land cover studies: Part 1. Methodology
Remote Sensing of Environment
(2000)
Application of CORINE land cover methodology to the U.K.—some issues raised from Northern Ireland
Global Ecology and Biogeography Letters
Why regional studies are needed in the development of full-scale integrated assessment modelling of global change processes
Global Environmental Change—Human and Policy Dimensions
Long-term ecological changes in the densely populated rural landscapes of China
Long-term change in village-scale ecosystems in China using landscape and statistical methods
Ecological Applications
Changes in village-scale nitrogen storage in China's Tai Lake Region
Ecological Applications
Green surprise? How terrestrial ecosystems could affect earth's climate
Frontiers in Ecology and the Environment
Global consequences of land use
Science
Status of land cover classification accuracy assessment
Remote Sensing of Environment
Examination of some problems and solutions in monitoring urban areas from satellite platforms
International Journal of Remote Sensing
Land-use history (1730–1990) and vegetation dynamics in central New-England, USA
Journal of Ecology
Development of an ecological mapping methodology for urban areas in New Zealand
Landscape and Urban Planning
Remote sensing and land cover area estimation
International Journal of Remote Sensing
Two stage area frame on squared segments for farm surveys
Survey Methodology
IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region
Remote Sensing of Environment
Integrating photointerpretation and GIS for vegetation mapping: Some issues of error
Farming and the fate of wild nature
Science
Integrated approaches to long-term studies of urban ecological systems
BioScience
Landsat urban mapping based on a combined spectral–spatial methodology
Remote Sensing of Environment
Global land cover classification at 1 km spatial resolution using a classification tree approach
International Journal of Remote Sensing
Neglected dimensions of global land-use change — reflections and data
Population and Development Review
Landscape modelling using integrated airborne multi-spectral and laser scanning data
International Journal of Remote Sensing
Development of a 2001 national landcover database for the United States
Photogrammetric Engineering and Remote Sensing
Socioeconomics drive urban plant diversity
PNAS
Why are estimates of the terrestrial carbon balance so different?
Global Change Biology
Reconciling apparent inconsistencies in estimates of terrestrial CO2 sources and sinks
Tellus. Series B, Chemical and Physical Meteorology
IKONOS imagery for the large scale biosphere–atmosphere experiment in Amazonia (LBA)
Remote Sensing of Environment
Beyond potential vegetation: Combining lidar data and a height-structured model for carbon studies
Ecological Applications
Multispectral NIIRS reference guide
Civil NIIRS reference guide
Parametric land cover and land-use classifications as tools for environmental change detection
Agriculture, Ecosystems & Environment
Remote sensing of urban/suburban infrastructure and socio-economic attributes
Photogrammetric Engineering and Remote Sensing
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Current address: Environmental Sciences Institute, Florida A and M University, Tallahassee, Florida, USA.