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10 Land-Use and Land-Cover Change Human activities are transforming Earthâs surface at diseases (Foley et al. 2005). Clearly, land-use and land-cover unprecedented rates by ubiquitous exploitation of Earthâs change is a major driver of global change. biotic, soil, and water resources. The cumulative impacts Early efforts by geographers and ecologists to compile of land-use change have global consequences, altering the global vegetation and land-use maps were accomplished structure and functioning of ecosystems, which in turn can through decades of field investigations and consultations and influence the climate system due to the strong linkages compilation of numerous local, national, and regional veg- between land cover, energy exchange, and biogeochemical etation maps, atlases, and other literature (Matthews 1983, cycles. Because of the long timescale dynamics of ecosys- Olson et al. 1983, Wilson and Henderson-Sellers 1985). tem processes, land disturbances can affect ecosystem and These painstaking efforts took years to achieve, suffered climate processes for decades to centuries. from some degree of subjectivity, and often used sources of Over geologic timescales, climatic changes associated varying quality and time periods across different regions. with changes in Earthâs orbit around the Sun have led to Despite fundamental disagreements in land-cover classes large-scale vegetation changes. For example, the Little Ice and their distributions (DeFries and Townshend 1994a), they Age that ended in the 1700s eliminated forests in Iceland nevertheless greatly improved our understanding of global and a previously lush green landscape became the now land-cover and land-use patterns. arid region of the Sahara Desert 6,000 years ago (Ritchie The advent of satellite data has revolutionized our abil- et al. 1985). On shorter timescales, severe weather events, ity to characterize global land cover and monitor land-use fires, herbivory, and human activities have modified Earthâs patterns. Satellite sensors offer a synoptic view of Earth, as landscapes and converted them to new ecosystems. The well as the objectivity associated with a consistent measure- impacts of ancient human activities on the landscape have ment and methodology for mapping the entire planet. Satel- been reviewed extensively (Redman 1999), including the lite data have been used to characterize patterns of land-use use of fires to maintain open landscapes and the extinction and land-cover change across the world at scales from a few of large Pleistocene mammals after the arrival of humans in meters to a few degrees in latitude by longitude depending North America. on the sensor. More recently, over the last 300 years, human influence In 1972 the National Aeronautics and Space Admin- on the land has become globally extensive and intensive istration (NASA) launched the Landsat Satellite Program (Turner et al. 1990, Foley et al. 2005). Deforestation, agricul- (previously called the Earth Resources Technology Satel- tural expansion and intensification, desertification, and urban lite) to study the features of Earthâs landscapes and monitor expansion are all significant global environmental issues today its natural resources (Box 10.1, Figure 10.1). Landsat data (Lepers et al. 2005). Nearly 40 percent of the global land demonstrated early success in monitoring Earthâs croplands, surface is being exploited for agriculture (Foley et al. 2005), forests, and other natural resources. It has since become the and tropical deforestation continues unabated, especially in the workhorse for mapping land-use and land-cover change Amazon Basin and Southeast Asia (Lepers et al. 2005). Such across the world and now provides the longest continuous large-scale changes in land use and land cover can modify record of Earthâs changing land cover. Moreover, the free regional and global climate, degrade freshwater resources, availability of epochal global orthorectified Landsat data for cause air pollution, fragment habitats, cause species extinction the 1990s, 2000s, and so forth, has been a great boon for the and biodiversity loss, and lead to the emergence of infectious land-use and land-cover change community. 84
LAND-USE AND LAND-COVER CHANGE 85 BOX 10.1 The Landsat Satellite Program While weather satellites have been around since the 1960s, there was no systematic remote monitoring of Earthâs terrain until the Landsat program (Figure 10.1). Landsat 1 was launched in July 1972 and acquired more than 300,000 images of Earthâs 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 âdata gapâ 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. 2010 1970 1975 1980 1985 1990 1995 2000 2005 FIGURE 10.1â Timeline of the Landsat satellite series. SOURCE: NASA. The high cost and effort involved in processing Landsat Monitoring Agricultural Lands data over large regions, however, led to the use of coarse- and Monitoring food production and forecasting droughts moderate-resolution sensors (e.g., the Advanced Very High and famines are critical for human societies. Some of the Resolution Radiometer [AVHRR], the Moderate Resolution earliest applications of Landsat data included agricultural Imaging Spectroradiometer [MODIS]) during the 1990s monitoring and forecasting (Landgrebe 1997). One of the and early 2000s. Interestingly, the use of high-resolution most successful early experiments was LACIE (Large Area commercial data (~1 m; e.g., IKONOS, QUICKBIRD) has Crop Inventory Experiment), begun in November 1974. The become more common recently. Finally, while optical data capabilities of remote sensing in large-area crop monitoring are best suited for land-cover mapping, active sensors such as were demonstrated by LACIEâs estimate of wheat produc- radar (e.g., the Japanese Earth Resources Satellite [JERSâ1]) tion in the Soviet Union during the 1977 growing season to are valuable in cloudy regions and also can help derive struc- within 6 percent of the reported Soviet figures (MacDonald tural characteristics of vegetation. In summary, technology and Hall 1980). In 1980 this program was broadened to form seems to drive much of the research and applications, but AgriSTARS (Agriculture and Resources Inventory Surveys there is always a trade-off in terms of cost and effort involved Through Aerospace Remote Sensing), which included crop in processing the data. commodity forecasting of all major grains. Similar pro- grams in crop monitoring continue today, such as PECAD
86 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS (Production Estimates and Crop Assessment Division) of than those previously reported by ground-based inventories the Foreign Agricultural Service of the U.S. Department of or national surveys (DeFries and Achard 2002, Hansen and Agriculture (USDA). The USDAâs Cropland Data Layer, DeFries 2004). The consequence of these new studies has developed using Landsat 7 and Advanced Wide Field Sen- been a lower estimate of carbon emissions from deforesta- sor (AWiFS) data, is an excellent example of the use of tion, with important implications for our understanding of remote sensing to monitor crop patterns and the implica- the present-day carbon budget (DeFries and Achard 2002, tions for environment and society (http://www.nass.usda. Houghton 2003, Foley and Ramankutty 2004, Ramankutty gov/research/Cropland/SARS1a.htm). et al. 2007). Another recent successful application of satellite data in While satellite data have been widely used to map agricultural applications is the Famine Early Warning System deforestation around the world, good estimates of selective Network (FEWS NET). This program was set up in 1985 logging have not been available until recently. Asner and by the U.S. Agency for International Development, initially colleagues (2005) developed a method to estimate selec- in the Sahel and Horn of Africa and now extends to a few tive logging over the Amazon Basin using Landsat data other arid developing nations, to incorporate satellite data in ( Â FigureÂ 10.4). The study found that the area of forest damage famine early warning (Hutchison 1998). This program uses from selective logging matched or exceeded rates of clear- AVHRR data to obtain vegetation conditions and rainfall cut deforestation. This implied a 25 percent increase in the estimates from the European Meteosat satellite. In FEWS estimate of gross annual anthropogenic emissions of carbon NET, satellite information forms an important component of from Amazon forests over that estimated previously from a multipronged approach to forecasting famines that includes deforestation alone. This has been a remarkable advance in both biophysical information and socioeconomic informa- our ability to map fine-scale patterns of land-use practices. tion to develop indicators for food supply, food access, and levels of development (Hutchison 1998). These and other Mapping Global Land Cover achievements exemplify the benefits that can be gained from combining satellite observations with other available Even though monitoring and identifying regions of rapid information (see Box 10.2, Figure 10.2). land-cover change is a priority for the scientific community (for example, Box 10.3, Figure 10.5), baseline characteriza- tion of global land cover and land use is also important, Estimating Tropical Deforestation especially for global analysis and modeling of ecosystems Over the past few decades there has been increasing and their impacts. As described earlier, it is expensive and concern about tropical deforestation and the associated laborious to use Landsat data for large-area land-cover biodiversity loss and environmental consequences. Satellite mapping. Therefore, moderate-resolution weather satellite data have played a crucial role in measuring both the rates sensors (~1âkm resolution) have been used to characterize and the patterns of forest loss. The first large-scale defores- land-cover patterns globally (see Table 10.1). The University tation map using satellite imagery was made by Tardin and of ÂMaryland pioneered the development of global land-cover colleagues (1980) for the Brazilian Amazon. The NASA classification data sets using AVHRR data. Since then there Pathfinder Humid Tropical Deforestation project has since have been at least three other efforts to characterize global made repeat assessments for the Amazon (Tardin and Cunha land cover (Table 10.1). These efforts have grouped the 1989, Skole and Tucker 1993) and for much of the tropics Earthâs landscape into numerous land-cover classes (Fig- (Chomentowski et al. 1994; Figure 10.3). ureÂ 10.6). In contrast to the discrete classifiers, the MODIS Deforestation rates have been estimated for the entire Vegetation Continuous Fields product provides a continuous tropics in several recent studies. Using a sampling of ÂLandsat description of the landscape (percentage tree cover, herba- scenes, the Food and Agriculture Organization (FAO) mapped ceous and bare ground, as well as leaf type and phenology). tropical deforestation for the 1980s and 1990s (FAO 2001), These global data sets have provided a comprehensive global while the TREES II project of the Joint Research Center of view of Earthâs land surface. They have become valuable the European Commission mapped deforestation rates for inputs for global climate and ecosystem models used to study the humid tropics for the 1990s (Achard et al. 2002, 2004). the influence of land-cover changes on the Earth system While it is generally acknowledged that high-Âresolution (DeFries et al. 1999, Feddema et al. 2005). remote sensing data are needed to identify deforestation, DeFries and colleagues (DeFries et al. 2002, Hansen and Mapping Global Fires DeFries 2004) showed recently that it is also Â possible to estimate tropical deforestation over large areas using coarse- Fires are an important component of ecosystems; many resolution weather satellite data (8-km resolution AVHRR natural communities depend on fires for their regeneration. Pathfinder data) calibrated against high-Âresolution esti- Natural fires have been around since the presence of oxygen mates. Regardless of the specific methods used, all of these in the atmosphere, and humans have managed fire for more satellite-based estimates of deforestation rates were lower than a half-million years. However, only recently has the
LAND-USE AND LAND-COVER CHANGE 87 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 Link- ing Household and Community Surveys to Remote Sensing and GIS (Fox 2003), present several examples of these a Â pproaches. 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)ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ . ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ One example of the âstatistical data fusionâ 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 worldâs croplands (Figure 10.2). Until the advent of remote sensing, our knowledge of the global distribution of agriÂcultural 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 âdata fusionâ technique exploits the strengths of both data sources to characterize the worldâs 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 10.2â Croplands of the world in the year 2000. SOURCE: N. Ramankutty. global distribution of fires been characterized. With the use of document fires at the global scale. The Global Burnt Area remote sensing, rapid progress has been made in document- (GBA-2000) data set derived using the SPOT VEGETATION ing the mostly anthropogenic fires in the tropics (Pereira et al. satellite was the first estimate of the global area of vegeta- 1999) as well as the primarily natural fires in boreal regions tion burned in the year 2000 (Tansey et al. 2004). The ATSR (Kasischke et al. 2002). World Fire Atlas (Figure 10.7) is another global inventory of Several major efforts have also been undertaken to monthly fire maps from 1995 to the present, produced using
88 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS FIGURE 10.3â 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 10.4â Estimating selective logging over the Amazon Basin using Landsat data. SOURCE: Asner et al. (2005). Reprinted with permission from AAAS, copyright 2005.
LAND-USE AND LAND-COVER CHANGE 89 BOX 10.3 Monitoring Urban Areas Although built-up areas account for less than 2 percent of Earthâs land area, more than half of the worldâs 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 followÂing 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 âcity typesâ 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 âtemplates,â 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); Âexpansive-growth cities with extensive dispersion at low population densities (occurring almost exclusively in U.S. cities, e.g., ÂWashington, 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 Âcommon 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 10.5â Urban expansion in four different cities. SOURCE: Schneider and Woodcock (in press). Reprinted with permission from Urban Studies.
90 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS TABLE 10.1â Global Land Cover Data Sets from Earth Observation Data. Year of Spatial Data Developer Name of Product Sensor Data Resolution Reference University of Maryland UMD Global Land Cover AVHRR 1987 1 degree DeFries and Townshend (1994b) Classification 1984 8-km DeFries et al. (1998) 1992 1-km Hansen et al. (2000) Vegetation Continuous Fields MODIS 2001 500-m Hansen et al. (2003) MOD44B U.S. Geological Surveyâs Global Land Cover AVHRR 1992 1-km Loveland et al. (2000) EROS Data Center; Characterization University of Nebraska, Lincoln; and Joint Research Centre, European Commission Boston University MODIS MOD12Q1 Land Cover MODIS 2001 1-km Friedl et al. (2002) Product Joint Research Centre, Global Land Cover 2000 SPOT 2000 1-km Bartholome and Belward (2005) European Commission VEGETATION 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 10.6â Earthâs land-cover classes. SOURCE: Friedl et al. (2002). Reprinted with permission from Elsevier, copyright 2002. 10-6
LAND-USE AND LAND-COVER CHANGE 91 FIGURE 10.7â 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 of about 250 regional soil degradation experts, the Global on the European Remote Sensing (ERS) and ENVISAT Assessment of Human-Induced Soil Degradation also satellites (Arino and J.M. Rosaz 1999). GLOBSCAR, a reported extensive worldwide desertification (Oldeman et complimentary product to GBA-2000, maps the global al. 1991). Desertification became the dominant theme of an distribution of burned area at 1-km spatial resolution and environmental convention, the United Nations Convention monthly time intervals using the ATSR-2 instrument on the to Combat Desertification, which emerged from the Rio ERS-2 satellite (Simon et al. 2004). These products have summit of 1992. been used to compute the emissions of greenhouse gases and Satellite data sets have played a critical role in assessing aerosols from biomass burning and to explore the impacts the role of human activities in desertification. Using the long on tropical ozone levels (Schultz 2002, Duncan et al. 2003, time series AVHRR record, a study by Tucker et al. (1991) Palacios-Orueta et al. 2004). Other global fire mapping stud- discredited the widely held claims of desertification in the ies include those of Dwyer et al. (2000), who determined the Sahel. The authors found that a satellite-derived vegetation spatial and seasonal distributions of active fires at the global index was highly correlated to measurements of rainfall over scale between April 1992 and December 1993, and RiaÃ±o et the 1980-1990 period, thereby suggesting that vegetation al. (2007), who identified global patterns of fire frequency, in the Sahel was simply responding to interannual rainfall seasonality, and periodicity for different land-cover types changes rather than any human-driven causes. Another study using 20 years of AVHRR data and established correlations by Prince et al. (1998) using AVHRR data for 1982-1990 with environmental variables. 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 Understanding Desertification since 1994 seem to be associated with a gradual recovery In the 1970s, reports of the southward advance of the in vegetation (Anyamba and Tucker 2005). Measuring and Sahara Desert caused increased concern about human- attributing desertification remains difficult because a wide induced desertification (Lamprey 1975, Desert Encroach- variety of environmental changes are taking place at a range ment Control and Rehabilitation Programme 1976, Smith of spatial and temporal scales (Reynolds and Stafford-Smith 1986, Lamprey 1988, Suliman 1988). Based on a survey 2002, Reynolds et al. 2007).