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Earth Observations from Space: The First 50 Years of Scientific Achievements
10
Land-Use and Land-Cover Change
Human activities are transforming Earth’s surface at unprecedented rates by ubiquitous exploitation of Earth’s biotic, soil, and water resources. The cumulative impacts of land-use change have global consequences, altering the structure and functioning of ecosystems, which in turn can influence the climate system due to the strong linkages between land cover, energy exchange, and biogeochemical cycles. Because of the long timescale dynamics of ecosystem processes, land disturbances can affect ecosystem and climate processes for decades to centuries.
Over geologic timescales, climatic changes associated with changes in Earth’s orbit around the Sun have led to large-scale vegetation changes. For example, the Little Ice Age that ended in the 1700s eliminated forests in Iceland and a previously lush green landscape became the now arid region of the Sahara Desert 6,000 years ago (Ritchie et al. 1985). On shorter timescales, severe weather events, fires, herbivory, and human activities have modified Earth’s landscapes and converted them to new ecosystems. The impacts of ancient human activities on the landscape have been reviewed extensively (Redman 1999), including the use of fires to maintain open landscapes and the extinction of large Pleistocene mammals after the arrival of humans in North America.
More recently, over the last 300 years, human influence on the land has become globally extensive and intensive (Turner et al. 1990, Foley et al. 2005). Deforestation, agricultural expansion and intensification, desertification, and urban expansion are all significant global environmental issues today (Lepers et al. 2005). Nearly 40 percent of the global land surface is being exploited for agriculture (Foley et al. 2005), and tropical deforestation continues unabated, especially in the Amazon Basin and Southeast Asia (Lepers et al. 2005). Such large-scale changes in land use and land cover can modify regional and global climate, degrade freshwater resources, cause air pollution, fragment habitats, cause species extinction and biodiversity loss, and lead to the emergence of infectious diseases (Foley et al. 2005). Clearly, land-use and land-cover change is a major driver of global change.
Early efforts by geographers and ecologists to compile global vegetation and land-use maps were accomplished through decades of field investigations and consultations and compilation of numerous local, national, and regional vegetation maps, atlases, and other literature (Matthews 1983, Olson et al. 1983, Wilson and Henderson-Sellers 1985). These painstaking efforts took years to achieve, suffered from some degree of subjectivity, and often used sources of varying quality and time periods across different regions. Despite fundamental disagreements in land-cover classes and their distributions (DeFries and Townshend 1994a), they nevertheless greatly improved our understanding of global land-cover and land-use patterns.
The advent of satellite data has revolutionized our ability to characterize global land cover and monitor land-use patterns. Satellite sensors offer a synoptic view of Earth, as well as the objectivity associated with a consistent measurement and methodology for mapping the entire planet. Satellite data have been used to characterize patterns of land-use and land-cover change across the world at scales from a few meters to a few degrees in latitude by longitude depending on the sensor.
In 1972 the National Aeronautics and Space Administration (NASA) launched the Landsat Satellite Program (previously called the Earth Resources Technology Satellite) to study the features of Earth’s landscapes and monitor its natural resources (Box 10.1, Figure 10.1). Landsat data demonstrated early success in monitoring Earth’s croplands, forests, and other natural resources. It has since become the workhorse for mapping land-use and land-cover change across the world and now provides the longest continuous record of Earth’s changing land cover. Moreover, the free availability of epochal global orthorectified Landsat data for the 1990s, 2000s, and so forth, has been a great boon for the land-use and land-cover change community.
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BOX 10.1
The Landsat Satellite Program
While weather satellites have been around since the 1960s, there was no systematic remote monitoring of Earth’s terrain until the Landsat program (Figure 10.1). Landsat 1 was launched in July 1972 and acquired more than 300,000 images of Earth’s land surface using the Multispectral Scanner (MSS) instrument, which recorded data in four spectral bands with 79-m spatial resolution. Seven Landsat missions have been launched since then, with Landsat 7 continuing today. Landsat 1, 2, and 3 missions used the MSS instrument and demonstrated the usefulness of the acquired data for cartography, land surveys, agricultural forecasting, water resource management, forest management, and mapping sea-ice movement. Launched in 1982, Landsat 4 carried the Thematic Mapper (TM) instrument, which is still in wide use today for mapping land-cover change over large areas. The 30-m pixel size combined with seven spectral bands in the visible, near infrared, and midinfrared are well suited for mapping disturbance patterns. The value of Landsat data in land-cover mapping is highlighted by the fact that the current “data gap” in Landsat 7 data due to an instrument malfunction has been a major setback for the scientific community. Landsat 7 is currently not collecting research-grade data, and a follow-up Landsat Data Continuity Mission is therefore being planned.
FIGURE 10.1 Timeline of the Landsat satellite series. SOURCE: NASA.
The high cost and effort involved in processing Landsat data over large regions, however, led to the use of coarse- and moderate-resolution sensors (e.g., the Advanced Very High Resolution Radiometer [AVHRR], the Moderate Resolution Imaging Spectroradiometer [MODIS]) during the 1990s and early 2000s. Interestingly, the use of high-resolution commercial data (~1 m; e.g., IKONOS, QUICKBIRD) has become more common recently. Finally, while optical data are best suited for land-cover mapping, active sensors such as radar (e.g., the Japanese Earth Resources Satellite [JERS-1]) are valuable in cloudy regions and also can help derive structural characteristics of vegetation. In summary, technology seems to drive much of the research and applications, but there is always a trade-off in terms of cost and effort involved in processing the data.
MONITORING AGRICULTURAL LANDS
Monitoring food production and forecasting droughts and famines are critical for human societies. Some of the earliest applications of Landsat data included agricultural monitoring and forecasting (Landgrebe 1997). One of the most successful early experiments was LACIE (Large Area Crop Inventory Experiment), begun in November 1974. The capabilities of remote sensing in large-area crop monitoring were demonstrated by LACIE’s estimate of wheat production in the Soviet Union during the 1977 growing season to within 6 percent of the reported Soviet figures (MacDonald and Hall 1980). In 1980 this program was broadened to form AgriSTARS (Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing), which included crop commodity forecasting of all major grains. Similar programs in crop monitoring continue today, such as PECAD
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(Production Estimates and Crop Assessment Division) of the Foreign Agricultural Service of the U.S. Department of Agriculture (USDA). The USDA’s Cropland Data Layer, developed using Landsat 7 and Advanced Wide Field Sensor (AWiFS) data, is an excellent example of the use of remote sensing to monitor crop patterns and the implications for environment and society (http://www.nass.usda.gov/research/Cropland/SARS1a.htm).
Another recent successful application of satellite data in agricultural applications is the Famine Early Warning System Network (FEWS NET). This program was set up in 1985 by the U.S. Agency for International Development, initially in the Sahel and Horn of Africa and now extends to a few other arid developing nations, to incorporate satellite data in famine early warning (Hutchison 1998). This program uses AVHRR data to obtain vegetation conditions and rainfall estimates from the European Meteosat satellite. In FEWS NET, satellite information forms an important component of a multipronged approach to forecasting famines that includes both biophysical information and socioeconomic information to develop indicators for food supply, food access, and levels of development (Hutchison 1998). These and other achievements exemplify the benefits that can be gained from combining satellite observations with other available information (see Box 10.2, Figure 10.2).
ESTIMATING TROPICAL DEFORESTATION
Over the past few decades there has been increasing concern about tropical deforestation and the associated biodiversity loss and environmental consequences. Satellite data have played a crucial role in measuring both the rates and the patterns of forest loss. The first large-scale deforestation map using satellite imagery was made by Tardin and colleagues (1980) for the Brazilian Amazon. The NASA Pathfinder Humid Tropical Deforestation project has since made repeat assessments for the Amazon (Tardin and Cunha 1989, Skole and Tucker 1993) and for much of the tropics (Chomentowski et al. 1994; Figure 10.3).
Deforestation rates have been estimated for the entire tropics in several recent studies. Using a sampling of Landsat scenes, the Food and Agriculture Organization (FAO) mapped tropical deforestation for the 1980s and 1990s (FAO 2001), while the TREES II project of the Joint Research Center of the European Commission mapped deforestation rates for the humid tropics for the 1990s (Achard et al. 2002, 2004). While it is generally acknowledged that high-resolution remote sensing data are needed to identify deforestation, DeFries and colleagues (DeFries et al. 2002, Hansen and DeFries 2004) showed recently that it is also possible to estimate tropical deforestation over large areas using coarse-resolution weather satellite data (8-km resolution AVHRR Pathfinder data) calibrated against high-resolution estimates. Regardless of the specific methods used, all of these satellite-based estimates of deforestation rates were lower than those previously reported by ground-based inventories or national surveys (DeFries and Achard 2002, Hansen and DeFries 2004). The consequence of these new studies has been a lower estimate of carbon emissions from deforestation, with important implications for our understanding of the present-day carbon budget (DeFries and Achard 2002, Houghton 2003, Foley and Ramankutty 2004, Ramankutty et al. 2007).
While satellite data have been widely used to map deforestation around the world, good estimates of selective logging have not been available until recently. Asner and colleagues (2005) developed a method to estimate selective logging over the Amazon Basin using Landsat data (Figure 10.4). The study found that the area of forest damage from selective logging matched or exceeded rates of clear-cut deforestation. This implied a 25 percent increase in the estimate of gross annual anthropogenic emissions of carbon from Amazon forests over that estimated previously from deforestation alone. This has been a remarkable advance in our ability to map fine-scale patterns of land-use practices.
MAPPING GLOBAL LAND COVER
Even though monitoring and identifying regions of rapid land-cover change is a priority for the scientific community (for example, Box 10.3, Figure 10.5), baseline characterization of global land cover and land use is also important, especially for global analysis and modeling of ecosystems and their impacts. As described earlier, it is expensive and laborious to use Landsat data for large-area land-cover mapping. Therefore, moderate-resolution weather satellite sensors (~1-km resolution) have been used to characterize land-cover patterns globally (see Table 10.1). The University of Maryland pioneered the development of global land-cover classification data sets using AVHRR data. Since then there have been at least three other efforts to characterize global land cover (Table 10.1). These efforts have grouped the Earth’s landscape into numerous land-cover classes (Figure 10.6). In contrast to the discrete classifiers, the MODIS Vegetation Continuous Fields product provides a continuous description of the landscape (percentage tree cover, herbaceous and bare ground, as well as leaf type and phenology). These global data sets have provided a comprehensive global view of Earth’s land surface. They have become valuable inputs for global climate and ecosystem models used to study the influence of land-cover changes on the Earth system (DeFries et al. 1999, Feddema et al. 2005).
MAPPING GLOBAL FIRES
Fires are an important component of ecosystems; many natural communities depend on fires for their regeneration. Natural fires have been around since the presence of oxygen in the atmosphere, and humans have managed fire for more than a half-million years. However, only recently has the
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BOX 10.2
Merging Satellite and Ground-Based Data
This chapter mainly discusses approaches to land-cover change research that have directly used remote sensing observations. Many advances, however, have come from approaches that merge satellite data with other ground-based data sources such as census information and survey data. A couple of recent books, People and Pixels: Linking Remote Sensing and Social Science (Liverman et al. 1998) and People and the Environment: Approaches to Linking Household and Community Surveys to Remote Sensing and GIS (Fox 2003), present several examples of these approaches. Numerous studies have made advances in mapping global land cover, agricultural land-use practices, and urban areas by either merging census and other ancillary information with satellite data using statistical methods or using the ancillary information to guide the land-cover classification from remote sensing (e.g., Ramankutty and Foley 1998, Loveland et al. 2000, Hurtt et al. 2001, Cardille et al. 2002, Frolking et al. 2002, McIver and Friedl 2002, Kerr and Cihlar 2003, Schneider et al. 2003). One example of the “statistical data fusion” approach is the work of Ramankutty et al. (in press), who used global land-cover classification data derived from moderate-resolution remote sensors with national and subnational inventory statistics to develop a global map of the world’s croplands (Figure 10.2). Until the advent of remote sensing, our knowledge of the global distribution of agricultural lands was limited to inventory data, which has poor spatial information (available at the administrative level) and is inconsistent in quality across different countries. Therein lies the strength of remote sensing data, which provide consistent and spatially explicit estimates of land-cover across the world. The “data fusion” technique exploits the strengths of both data sources to characterize the world’s cultivated lands in a continuous fashion, depicting the percentage of each pixel that is in croplands. The global map indicates that about 12 percent of the global land area is devoted to cultivation and that some areas of the planet are more intensely cultivated than others. This global data set has been useful in various applications such as estimating the carbon cycle and climate implications of land-cover change, estimating global soil erosion, and as providing inputs to global economic models.
FIGURE 10.2 Croplands of the world in the year 2000. SOURCE: N. Ramankutty.
global distribution of fires been characterized. With the use of remote sensing, rapid progress has been made in documenting the mostly anthropogenic fires in the tropics (Pereira et al. 1999) as well as the primarily natural fires in boreal regions (Kasischke et al. 2002).
Several major efforts have also been undertaken to document fires at the global scale. The Global Burnt Area (GBA-2000) data set derived using the SPOT VEGETATION satellite was the first estimate of the global area of vegetation burned in the year 2000 (Tansey et al. 2004). The ATSR World Fire Atlas (Figure 10.7) is another global inventory of monthly fire maps from 1995 to the present, produced using
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FIGURE 10.3 Quantifying Amazon deforestation in 1988 using NASA Pathfinder Humid Tropical Deforestation project. SOURCE: Skole and Tucker (1993). Reprinted with permission from AAAS, copyright 1993.
FIGURE 10.4 Estimating selective logging over the Amazon Basin using Landsat data. SOURCE: Asner et al. (2005). Reprinted with permission from AAAS, copyright 2005.
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BOX 10.3
Monitoring Urban Areas
Although built-up areas account for less than 2 percent of Earth’s land area, more than half of the world’s population (3.3 billion people) now live in cities and over 70 percent of economic activity is concentrated in urban areas. Remotely sensed data have played a pivotal role in our ability to monitor, assess, and understand the dynamic processes in urban regions since the early urban land classification efforts of the mid-1970s and following the second generation of satellite sensors (Landsat, SPOT) in the 1980s. The most recent wave of very high resolution sensors and advances in data fusion have spawned new urban remote sensing methods to extract urban features and characterize building materials.
Data from the Landsat sensors have played a particularly important role in assessing urban expansion, primarily because of increased data availability and the synoptic view these data afford. Cities have grown so significantly in the past few decades that it is critical to have accurate and up-to-date maps to help monitor the rate and form of urban and periurban land conversion and to identify how urban expansion differs across cities from a range of geographic settings and levels of economic development. One example of such research is the work of Schneider and Woodcock (in press), who have used a combination of Landsat Thematic Mapper and Enhanced Thematic Mapper data, spatial metrics, and census data to explain differences in urban expansion in a cross-section of 25 midsized cities from around the globe (Figure 10.5). Results show that these patterns can be categorized into a taxonomy of four “city types” as shown in the figure below (yellow indicates the urban extent in 1990; orange shows the increase in urban land from 1990 to 2000). The four city types, or “templates,” for growth are low-growth cities characterized by modest rates of infilling-type expansion (e.g., Warsaw); high-growth cities with rapid, fragmented development (e.g., Bangalore); expansive-growth cities with extensive dispersion at low population densities (occurring almost exclusively in U.S. cities, e.g., Washington, D.C.); and frantic-growth cities, such as those in China, exhibiting extraordinary rates of growth at high population densities (e.g., Guangzhou). This study also showed that urban patterns outside the United States are not consistent with common conceptions of the American urban sprawl. Although nearly all sample cities are expanding at the urban-rural boundary, results confirm that the majority of non-American cities do not exhibit large, dispersed spatial forms.
FIGURE 10.5 Urban expansion in four different cities. SOURCE: Schneider and Woodcock (in press). Reprinted with permission from Urban Studies.
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TABLE 10.1 Global Land Cover Data Sets from Earth Observation Data.
Data Developer
Name of Product
Sensor
Year of Data
Spatial Resolution
Reference
University of Maryland
UMD Global Land Cover Classification
AVHRR
1987
1 degree
DeFries and Townshend (1994b)
1984
8-km
DeFries et al. (1998)
1992
1-km
Hansen et al. (2000)
Vegetation Continuous Fields MOD44B
MODIS
2001
500-m
Hansen et al. (2003)
U.S. Geological Survey’s EROS Data Center; University of Nebraska, Lincoln; and Joint Research Centre, European Commission
Global Land Cover Characterization
AVHRR
1992
1-km
Loveland et al. (2000)
Boston University
MODIS MOD12Q1 Land Cover Product
MODIS
2001
1-km
Friedl et al. (2002)
Joint Research Centre, European Commission
Global Land Cover 2000
SPOT VEGETATION
2000
1-km
Bartholome and Belward (2005)
SOURCE: Ramankutty et al. (2006). Modified by N. Ramankutty, McGill University. Reprinted with kind permission of Springer Science and Business Media, copyright 2006. Modified by Navin Ramankutty, McGill University.
FIGURE 10.6 Earth’s land-cover classes. SOURCE: Friedl et al. (2002). Reprinted with permission from Elsevier, copyright 2002.
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FIGURE 10.7 World Fire Atlas from ATSR. SOURCE: European Space Agency, http://esamultimedia.esa.int/images/EarthObservation/worldfireatlas_H.jpg.
the Along Track Scanning Radiometer (ATSR) instrument on the European Remote Sensing (ERS) and ENVISAT satellites (Arino and J.M. Rosaz 1999). GLOBSCAR, a complimentary product to GBA-2000, maps the global distribution of burned area at 1-km spatial resolution and monthly time intervals using the ATSR-2 instrument on the ERS-2 satellite (Simon et al. 2004). These products have been used to compute the emissions of greenhouse gases and aerosols from biomass burning and to explore the impacts on tropical ozone levels (Schultz 2002, Duncan et al. 2003, Palacios-Orueta et al. 2004). Other global fire mapping studies include those of Dwyer et al. (2000), who determined the spatial and seasonal distributions of active fires at the global scale between April 1992 and December 1993, and Riaño et al. (2007), who identified global patterns of fire frequency, seasonality, and periodicity for different land-cover types using 20 years of AVHRR data and established correlations with environmental variables.
UNDERSTANDING DESERTIFICATION
In the 1970s, reports of the southward advance of the Sahara Desert caused increased concern about human-induced desertification (Lamprey 1975, Desert Encroachment Control and Rehabilitation Programme 1976, Smith 1986, Lamprey 1988, Suliman 1988). Based on a survey of about 250 regional soil degradation experts, the Global Assessment of Human-Induced Soil Degradation also reported extensive worldwide desertification (Oldeman et al. 1991). Desertification became the dominant theme of an environmental convention, the United Nations Convention to Combat Desertification, which emerged from the Rio summit of 1992.
Satellite data sets have played a critical role in assessing the role of human activities in desertification. Using the long time series AVHRR record, a study by Tucker et al. (1991) discredited the widely held claims of desertification in the Sahel. The authors found that a satellite-derived vegetation index was highly correlated to measurements of rainfall over the 1980-1990 period, thereby suggesting that vegetation in the Sahel was simply responding to interannual rainfall changes rather than any human-driven causes. Another study by Prince et al. (1998) using AVHRR data for 1982-1990 also found that vegetation productivity was marching in lockstep with precipitation changes and found no evidence for a human hand. Indeed, the wetter conditions prevailing since 1994 seem to be associated with a gradual recovery in vegetation (Anyamba and Tucker 2005). Measuring and attributing desertification remains difficult because a wide variety of environmental changes are taking place at a range of spatial and temporal scales (Reynolds and Stafford-Smith 2002, Reynolds et al. 2007).
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