Geology topics

Thomas R. Loveland

Publications and source records attributed to Thomas R. Loveland.

At least 19 recordsLinked to original sources

USGS2022

Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Frontiers in Remote Sensing↗

USGS2018

Effects of contemporary land-use and land-cover change on the carbon balance of terrestrial ecosystems in the United States

Changes in land use and land cover (LULC) can have profound effects on terrestrial carbon dynamics, yet their effects on the global carbon budget remain uncertain. While land change impacts on ecosystem carbon dynamics have been the focus of numerous studies, few efforts have been based on observational data incorporating multiple ecosystem types spanning large geographic areas over long time horizons. In this study we use a variety of synoptic-scale remote sensing data to estimate the effect of LULC changes associated with urbanization, agricultural expansion and contraction, forest harvest, and wildfire on the carbon balance of terrestrial ecosystems (forest, grasslands, shrublands, and agriculture) in the conterminous United States (i.e. excluding Alaska and Hawaii) between 1973 and 2010. We estimate large net declines in the area of agriculture and forest, along with relatively small increases in grasslands and shrublands. The largest net change in any class was an estimated gain of 114 865 km 2 of developed lands, an average rate of 3282 km 2 yr −1 . On average, US ecosystems sequestered carbon at an annual rate of 254 Tg C yr −1 . In forest lands, the net sink declined by 35% over the study period, largely a result of land-use legacy, increasing disturbances, and reductions in forest area due to land use conversion. Uncertainty in LULC change data contributed to a ~16% margin of error in the annual carbon sink estimate prior to 1985 (approximately ±40 Tg C yr −1 ). Improvements in LULC and disturbance mapping starting in the mid-1980s reduced this uncertainty by ~50% after 1985. We conclude that changes in LULC are a critical component to understanding ecosystem carbon dynamics, and continued improvements in detection, quantification, and attribution of change have the potential to significantly reduce current uncertainties.

Environmental Research Letters↗

USGS2018

2017 Landsat Science Team Summer Meeting Summary

The summer meeting of the U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) was held June 11-13, 2017, at the USGS’s Earth Resources Observation and Science (EROS) Center near Sioux Falls, SD. This was the final meeting of the Second (2012-2017) LST.1 Frank Kelly [EROS—Center Director] welcomed the attendees and expressed his thanks to the LST members for their contributions. He then introduced video-recorded messages from South Dakota’s U.S. senators, John Thune and Mike Rounds, in which they acknowledged the efforts of the team in advancing the societal impacts of the Landsat Program.

The Earth Observer↗

USGS2018

An overview of USGS-NASA Landsat Science activities during 2018

Two meetings of the U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) took place in 2018. The USGS Earth Resources Observation and Science (EROS) Center hosted the winter meeting, which took place February 21-22 in Sioux Falls, SD. The University of Colorado-Boulder hosted the summer meeting, which was held August 8-10 in Boulder, CO.

The Earth Observer↗

USGS2017

Grand challenges in understanding the interplay of climate and land changes

Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.

Earth Interactions↗

USGS2016

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4–8. Training data were selected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening ‘‘Function of mask” (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2016

Landsat 8: The plans, the reality, and the legacy

Landsat 8, originally known as the Landsat Data Continuity Mission (LDCM), is a National Aeronautics and Space Administration (NASA)-U.S. Geological Survey (USGS) partnership that continues the legacy of continuous moderate resolution observations started in 1972. The conception of LDCM to the reality of Landsat 8 followed an arduous path extending over nearly 13 years, but the successful launch on February 11, 2013 ensures the continuity of the unparalleled Landsat record. The USGS took over mission operations on May 30, 2013 and renamed LCDM to Landsat 8. Access to Landsat 8 data was opened to users worldwide. Three years following launch we evaluate the science and applications impact of Landsat 8. With a mission objective to enable the detection and characterization of global land changes at a scale where differentiation between natural and human-induced causes of change is possible, LDCM promised incremental technical improvements in capabilities needed for Landsat scientific and applications investigations. Results show that with Landsat 8, we are acquiring more data than ever before, the radiometric and geometric quality of data are generally technically superior to data acquired by past Landsat missions, and the new measurements, e.g., the coastal aerosol and cirrus bands, are opening new opportunities. Collectively, these improvements are sparking the growth of science and applications opportunities. Equally important, with Landsat 7 still operational, we have returned to global imaging on an 8-day cycle, a capability that ended when Landsat 5 ceased operational Earth imaging in November 2011. As a result, the Landsat program is on secure footings and planning is underway to extend the record for another 20 or more years.

Remote Sensing of Environment↗

USGS2015

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future

Sagebrush ( Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km 2 (1.1%) across the study area under the A1B scenario and 41.15 km 2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km 2 (4.1%) under A1B and 50.8 km 2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km 2 (3.8%) under A1B and 40.59 km 2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse ( Centrocercus urophasianus ) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.

Wyoming↗

USGS2015

A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data

The methodology for selection, creation, and application of a global remote sensing validation dataset using high resolution commercial satellite data is presented. High resolution data are obtained for a stratified random sample of 500 primary sampling units (5 km × 5 km sample blocks), where the stratification based on Köppen climate classes is used to distribute the sample globally among biomes. The high resolution data are classified to categorical land cover maps using an analyst mediated classification workflow. Our initial application of these data is to evaluate a global 30 m Landsat-derived, continuous field tree cover product. For this application, the categorical reference classification produced at 2 m resolution is converted to percent tree cover per 30 m pixel (secondary sampling unit)for comparison to Landsat-derived estimates of tree cover. We provide example results (based on a subsample of 25 sample blocks in South America) illustrating basic analyses of agreement that can be produced from these reference data. Commercial high resolution data availability and data quality are shown to provide a viable means of validating continuous field tree cover. When completed, the reference classifications for the full sample of 500 blocks will be released for public use.

Remote Sensing of Environment↗

USGS2014

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).

Remote Sensing of Environment↗

USGS2014

Landsat-8: Science and product vision for terrestrial global change research

Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.

Remote Sensing of Environment↗

USGS2014

Land cover trends dataset, 1973-2000

The U.S. Geological Survey Land Cover Trends Project is releasing a 1973–2000 time-series land-use/land-cover dataset for the conterminous United States. The dataset contains 5 dates of land-use/land-cover data for 2,688 sample blocks randomly selected within 84 ecological regions. The nominal dates of the land-use/land-cover maps are 1973, 1980, 1986, 1992, and 2000. The land-use/land-cover maps were classified manually from Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus imagery using a modified Anderson Level I classification scheme. The resulting land-use/land-cover data has a 60-meter resolution and the projection is set to Albers Equal-Area Conic, North American Datum of 1983. The files are labeled using a standard file naming convention that contains the number of the ecoregion, sample block, and Landsat year. The downloadable files are organized by ecoregion, and are available in the ERDAS IMAGINE TM (.img) raster file format.

Data Series↗

USGS2013

U.S. Geological Survey climate and land use change science strategy—A framework for understanding and responding to global change

Executive Summary The U.S. Geological Survey (USGS), a nonregulatory Federal science agency with national scope and responsibilities, is uniquely positioned to serve the Nation’s needs in understanding and responding to global change, including changes in climate, water availability, sea level, land use and land cover, ecosystems, and global biogeochemical cycles. Global change is among the most challenging and formidable issues confronting our Nation and society. Scientists agree that global environmental changes during this century will have far-reaching societal implications (Intergovernmental Panel on Climate Change, 2007; U.S. Global Change Research Program, 2009). In the face of these challenges, the Nation can benefit greatly by using natural science information in decisionmaking. Since the passage of the U.S. Global Change Research Act of 1990, the USGS has made substantial scientific contributions to understanding the interactive living and nonliving components of the Earth system. USGS natural science activities have led to fundamental advances in observing and understanding climate and land-cover change and the effects these changes have on ecosystems, natural-resource availability, and societal sustainability. Most of these major advances were pursued in partnership with other organizations within and outside the Department of the Interior. The inherent value of partnerships with other U.S. Global Change Research Program agencies and natural-resource managers is emphasized in all aspects of the planning and implementation of this Science Strategy for the coming decade. Over the next 10 years, the USGS will make substantial contributions to understanding how Earth systems interact, respond to, and cause global change. The USGS will work with science partners, decisionmakers, and resource managers at local to international levels (including Native American tribes) to improve understanding of past and present change; develop relevant forecasts; and identify those lands, resources, and communities most vulnerable to global change processes. Science will play an essential role in helping communities and land and resource managers understand local to global implications, anticipate effects, prepare for changes, and reduce the risks associated with decisionmaking in a changing environment. USGS partners and stakeholders will benefit from the data, predictive models, and decision-support products and services resulting from the implementation of this strategy. This Science Strategy recognizes core USGS strengths that are applied to key societal problems. It establishes seven goals for USGS global change science and strategic actions that may be implemented in the short term (1–5 years) and the longer term (5–10 years) to improve our understanding of the following areas of inquiry: Rates, causes, and impacts of past global changes; The global carbon cycle; Biogeochemical cycles and their coupled interactions; Land-use and land-cover change rates, causes, and consequences; Droughts, floods, and water availability under changing land-use and climatic conditions; Coastal response to sea-level rise, climatic change, and human development; and Biological responses to global change. In addition to the seven thematic goals, we address the central role of monitoring in accordance with the USGS Science Strategy recommendation that global change research should rely on existing “…decades of observational data and long-term records to interpret consequences of climate variability and change to the Nation’s biological populations, ecosystems, and land and water resources” (U.S. Geological Survey, 2007, p. 19). We also briefly describe specific needs and opportunities for coordinating USGS global change science among USGS Mission Areas and address the need for a comprehensive and sustained communications strategy.

Circular↗

USGS2013

Land-cover change in the conterminous United States from 1973 to 2000

Land-cover change in the conterminous United States was quantified by interpreting change from satellite imagery for a sample stratified by 84 ecoregions. Gross and net changes between 11 land-cover classes were estimated for 5 dates of Landsat imagery (1973, 1980, 1986, 1992, and 2000). An estimated 673,000 km 2 (8.6%) of the United States’ land area experienced a change in land cover at least one time during the study period. Forest cover experienced the largest net decline of any class with 97,000 km2 lost between 1973 and 2000. The large decline in forest cover was prominent in the two regions with the highest percent of overall change, the Marine West Coast Forests (24.5% of the region experienced a change in at least one time period) and the Eastern Temperate Forests (11.4% of the region with at least one change). Agriculture declined by approximately 90,000 km 2 with the largest annual net loss of 12,000 km 2 yr −1 occurring between 1986 and 1992. Developed area increased by 33% and with the rate of conversion to developed accelerating rate over time. The time interval with the highest annual rate of change of 47,000 km 2 yr −1 (0.6% per year) was 1986–1992. This national synthesis documents a spatially and temporally dynamic era of land change between 1973 and 2000. These results quantify land change based on a nationally consistent monitoring protocol and contribute fundamental estimates critical to developing understanding of the causes and consequences of land change in the conterminous United States.

Global Environmental Change↗

USGS2013

Next generation of global land cover characterization, mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

International Journal of Applied Earth Observation↗

USGS2013

Projected surface radiative forcing due to 2000--2050 land-cover land-use albedo change over the eastern United States

Satellite-derived contemporary land-cover land-use (LCLU) and albedo data and modeled future LCLU are used to study the impact of LCLU change from 2000 to 2050 on surface albedo and radiative forcing for 19 ecoregions in the eastern United States. The modeled 2000–2050 LCLU changes indicate a future decrease in both agriculture and forested land and an increase in developed land that induces ecoregion radiative forcings ranging from −0.175 to 0.432 W m −2 driven predominately by differences in the area and type of LCLU change. At the regional scale, these projected LCLU changes induce a net negative albedo decrease (−0.001) and a regional positive radiative forcing of 0.112 W m −2 . This overall positive forcing (i.e., warming) is almost 4 times greater than that estimated for documented 1973–2000 LCLU albedo change published in a previous study using the same methods.

Journal of Land Change Science↗

USGS2013

High-resolution global maps of 21st-century forest cover change

Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil’s well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

Science↗

USGS2012

History of land cover mapping

The historical roots of land-cover mapping reside in the early history of aerial photography and applications spanning forestry, agriculture, urban planning, and water-resources management. Considering this long span of mapping, any attempt to provide an exhaustive treatment of the full history of land-cover mapping will necessarily be incomplete. For that reason, this chapter on the history of land-cover mapping emphasizes the “modern” era of land-cover mapping, which has been arbitrarily defined to begin in the early 1970s. This was when civil space-based remote sensing came of age, and intellectual efforts focused on strategies for using new observations in understanding the characteristics of, and the changes in, land use and land cover.

Book chapter↗