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Results for “Land Use and Land Cover”

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At least 775 records · Page 43Linked to original sources
USGS2009

Completion of the National Land Cover Database (NLCD) 1992–2001 Land Cover Change Retrofit product

The Multi-Resolution Land Characteristics Consortium has supported the development of two national digital land cover products: the National Land Cover Dataset (NLCD) 1992 and National Land Cover Database (NLCD) 2001. Substantial differences in imagery, legends, and methods between these two land cover products must be overcome in order to support direct comparison. The NLCD 1992-2001 Land Cover Change Retrofit product was developed to provide more accurate and useful land cover change data than would be possible by direct comparison of NLCD 1992 and NLCD 2001. For the change analysis method to be both national in scale and timely, implementation required production across many Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) path/rows simultaneously. To meet these requirements, a hybrid change analysis process was developed to incorporate both post-classification comparison and specialized ratio differencing change analysis techniques. At a resolution of 30 meters, the completed NLCD 1992-2001 Land Cover Change Retrofit product contains unchanged pixels from the NLCD 2001 land cover dataset that have been cross-walked to a modified Anderson Level I class code, and changed pixels labeled with a 'from-to' class code. Analysis of the results for the conterminous United States indicated that about 3 percent of the land cover dataset changed between 1992 and 2001.

Open-File Report↗
USGS2019

Challenges for monitoring the extent and land use/cover changes in monarch butterflies’ migratory habitat across the United States and Mexico

This paper presents a synopsis of the challenges and limitations presented by existing and emerging land use/ land cover (LULC) digital data sets when used to analyze the extent, habitat quality, and LULC changes of the monarch (Danaus plexippus) migratory habitat across the United States of America (US) and Mexico. First, the characteristics, state of the knowledge, and issues related to this habitat are presented. Then, the characteristics of the existing and emerging LULC digital data sets with global or cross-border coverage are listed, followed by the data sets that cover only the US or Mexico. Later, we discuss the challenges for determining the extent, habitat quality, and LULC changes in the monarchs’ migratory habitat when using these LULC data sets in conjunction with the current state of the knowledge of the monarchs’ ecology, behavior, and foraging/roosting plants used during their migration. We point to approaches to address some of these challenges, which can be categorized into: (a) LULC data set characteristics and availability; (b) availability of ancillary land management information; (c) ability to construct accurate forage suitability indices for their migration habitat; and (d) level of knowledge of the ecological and behavioral patterns of the monarchs during their journey.

Land↗
USGS2018

Species distribution modeling in regions of high need and limited data: waterfowl of China

Background A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources. Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China. Methods Faced with limited data, we built species distribution models using a habitat suitability approach for China’s breeding and non-breeding (hereafter, wintering) waterfowl. An extensive review of the literature was used to determine model parameters for habitat modeling. Habitat relationships were implemented in GIS using land cover covariates. Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available. Results We developed suitability models for 42 waterfowl species (30 breeding and 39 wintering) at 1 km resolution for the extent of China, along with cumulative and genus level species richness maps. Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China. Wintering waterfowl suitability was highest in the lowland regions of southeastern China. Validation measures indicated strong performance in predicting species presence. Comparing our model outputs to China’s protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized. Conclusions These suitability models are the first available for many of China’s waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.

Avian Research↗
USGS2008

Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005

Land cover is a critical component of the Earth system, infl uencing land-atmosphere interactions, greenhouse gas fl uxes, ecosystem health, and availability of food, fi ber, and energy for human populations. The recent Integrated Global Observations of Land (IGOL) report calls for the generation of maps documenting global land cover at resolutions between 10m and 30m at least every fi ve years (Townshend et al., in press). Moreover, despite 35 years of Landsat observations, there has not been a unifi ed global analysis of land-cover trends nor has there been a global assessment of land-cover change at Landsat-like resolution. Since the 1990s, the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) have supported development of data sets based on global Landsat observations (Tucker et al., 2004). These land survey data sets, usually referred to as GeoCover ™, provide global, orthorectifi ed, typically cloud-free Landsat imagery centered on the years 1975, 1990, and 2000, with a preference for leaf-on conditions. Collectively, these data sets provided a consistent set of observations to assess land-cover changes at a decadal scale. These data are freely available via the Internet from the USGS Center for Earth Resources Observation and Science (EROS) (see http://earthexplorer.usgs.gov or http://glovis.usgs.gov). This has resulted in unprecedented downloads of data, which are widely used in scientifi c studies of land-cover change (e.g., Boone et al., 2007; Harris et al., 2005; Hilbert, 2006; Huang et al. 2007; Jantz et al., 2005, Kim et al., 2007; Leimgruber, 2005; Masek et al., 2006). NASA and USGS are continuing to support land-cover change research through the development of GLS2005 - an additional global Landsat assessment circa 20051 . Going beyond the earlier initiatives, this data set will establish a baseline for monitoring changes on a 5-year interval and will pave the way toward continuous global land-cover monitoring at Landsat-like resolution in the next decade.

Photogrammetric Engineering and Remote Sensing↗
USGS2026

Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data

Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.

Michigan, Minnesota, Wisconson↗
USGS2017

How well do route survey areas represent landscapes at larger spatial extents? An analysis of land cover composition along Breeding Bird Survey routes

The occurrence of birds in a survey unit is partly determined by the habitat present. Moreover, some bird species preferentially avoid some land cover types and are attracted to others. As such, land cover composition within the 400 m survey areas along a Breeding Bird Survey (BBS) route clearly influences the species available to be detected. Ideally, to extend survey results to the larger landscape, land cover composition within the survey area should be similar to that at larger spatial extents defining the landscape. Such representativeness helps minimize possible roadside effects (bias), here defined as differences in bird species composition and abundance along a roadside as compared to a larger surrounding landscape. We used land cover data from the 2011 National Land Cover Database to examine representativeness of land cover composition along routes. Using ArcGIS, the percentages of each of 15 land cover types within 400 m buffers along 2,696 U.S. BBS routes were calculated and compared to percentages in 2 km, 5 km, and 10 km buffers surrounding each route. This assessment revealed that aquatic cover types and highly urbanized land tend to be slightly underrepresented in the survey areas. Two anthropogenic cover types (pasture/hay and cropland) may be slightly overrepresented in the survey areas. Over all cover types, 92% of the 2,696 routes exhibited “good” representativeness, with <5 percentage points per cover type difference in proportional cover between the 400 m and 10 km buffers. This assessment further supports previous research indicating that any land-cover-based roadside bias in the bird data of the BBS is likely minimal.

Condor↗
USGS1998

Combining satellite data with ancillary data to produce a refined land-use/land-cover map

As part of the U.S. Geological Survey's National Water-Quality Assessment Program in the Western Lake Michigan Drainages Study Unit, a current map of land use and land cover is needed to gain a better understanding of how land use and land cover may influence water quality. Satellite data from the Landsat Thematic Mapper provides a means to map and measure the type and amount of various land-cover types across the Study Unit and can be easily updated as changes occur in the landscape or in water quality. Translating these land cover categories to land use, however, requires the use of other thematic maps or ancillary data layers, such as wetland inventories, population data, or road networks. This report describes a process of (1) using satellite imagery to produce a land-cover map for the Fox/Wolf River basin, a portion of the Western Lake Michigan Drainages NAWQA Study Unit and (2) improving the satellite-derived land-cover map by using other thematic maps. The multiple data layers are processed in a geographic information system (GIS), and the combination provides more information than individual sources alone.

Michigan, Wisconsin↗
USGS2016

Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California

A daily precipitation-runoff model, referred to as the Los Angeles Basin watershed model (LABWM), was used to estimate recharge and runoff for a 5,047 square kilometer study area that included the greater Los Angeles area and all surface-water drainages potentially contributing recharge to a 1,450 square kilometer groundwater-study area underlying the greater Los Angeles area, referred to as the Los Angeles groundwater-study area. The recharge estimates for the Los Angeles groundwater-study area included spatially distributed recharge in response to the infiltration of precipitation, runoff, and urban irrigation, as well as mountain-front recharge from surface-water drainages bordering the groundwater-study area. The recharge and runoff estimates incorporated a new method for estimating urban irrigation, consisting of residential and commercial landscape watering, based on land use and the percentage of pervious land area. The LABWM used a 201.17-meter gridded discretization of the study area to represent spatially distributed climate and watershed characteristics affecting the surface and shallow sub-surface hydrology for the Los Angeles groundwater study area. Climate data from a local network of 201 monitoring sites and published maps of 30-year-average monthly precipitation and maximum and minimum air temperature were used to develop the climate inputs for the LABWM. Published maps of land use, land cover, soils, vegetation, and surficial geology were used to represent the physical characteristics of the LABWM area. The LABWM was calibrated to available streamflow records at six streamflow-gaging stations. Model results for a 100-year target-simulation period, from water years 1915 through 2014, were used to quantify and evaluate the spatial and temporal variability of water-budget components, including evapotranspiration (ET), recharge, and runoff. The largest outflow of water from the LABWM was ET; the 100-year average ET rate of 362 millimeters per year (mm/yr) accounted for 66 percent of the combined water inflow of 551 mm/yr, including 488 mm/yr from precipitation and 63 mm/yr from urban irrigation. The simulated ET rate varied from a minimum of 0 mm/yr for impervious areas to high values of more than 1,000 mm/yr for many areas, including the south-facing slopes of the San Gabriel Mountains, stream channels underlain by permeable soils and thick root zones, and pervious locations receiving inflows both from urban irrigation and surface water. Runoff was the next largest outflow, averaging 145 mm/yr for the 100-year period, or 26 percent of the combined precipitation and urban-irrigation inflow. Recharge averaged 45 mm/yr, or about 8 percent of the combined inflow from precipitation and urban irrigation. Simulation results indicated that recharge in response to urban irrigation was an important component of spatially distributed recharge, contributing an average of 56 percent of the total recharge to the eight LABWM subdomains containing the Los Angeles groundwater study area. The 100‑year average recharge rate for the eight subdomains was 41 mm/yr, or 8,473 hectare-meters per year (ha-m/yr), with urban irrigation included in the simulation compared to a recharge rate of 18 mm/yr, or 3,741 ha-m/yr, with urban irrigation excluded. In contrast to recharge, the effect of urban irrigation on runoff was slight; runoff was 72,667 ha-m/yr with urban irrigation included compared to 72,618 ha-m/yr with urban irrigation excluded, an increase of only 48 ha-m/yr (about 0.1 percent). Simulation results also indicated that potential recharge from hilly drainages outside of, but bordering and tributary to, the lower-lying area of the Los Angeles groundwater study area, in this study referred to as mountain-front recharge, could provide an important contribution to the total recharge for the groundwater basins. The time-averaged recharge rate was similar to the combined direct and mountain-front recharge components estimated in a previous study and used as input for a calibrated groundwater model. The annual (water year) recharge estimates simulated in this study, however, indicated much greater year-to-year variability, which was dependent on year-to-year variability in the magnitude and distribution of daily precipitation, compared to the previous estimates.

California↗
USGS2022

Influence of anthropogenic subsidies on movements of common ravens

Anthropogenic subsidies can benefit populations of generalist predators such as common ravens (ravens; Corvus corax ), which in turn may depress populations of many types of species at lower-trophic levels, including desert tortoises (Gopherus agassizii ) or greater sage-grouse (Centrocercus urophasianus). Management of subsidized ravens often has targeted local breeding populations that are presumed to affect species of concern and ignored “urban” populations of ravens. However, little is known about how ravens move, especially in response to the presence of anthropogenic subsidies. Therefore, subsidized ravens from distant populations that are not managed may influence local prey. To better understand this issue, we deployed global positioning system – global system for mobile communications transmitters to track movements of 19 ravens from September to December 2020 relative to 2 land cover types that provide subsidies: developed areas and cultivated crops. On average, ravens moved 41.5 km (±30.5) per day, although daily movement distances ranged from 0.13– 206.1 km. Raven movement among cover types during the non-breeding season varied widely, with 100% of individuals each using land cover types that provide subsidy and other types at least once in the season. On 100% of days ravens used areas that did not provide subsidy, on 86.7% of days they used developed areas, and on 20.5% of days they used cultivated crops. Although on some days a raven would stay exclusively in areas that did not provide subsidy, there were no days in which a single raven ever stayed exclusively in developed or cultivated crops. Ravens moved shorter distances on days when they used subsidies more frequently. Further, time spent in developed areas and cultivated crops increased when ravens roosted closer to them, although this effect was greater for developed areas than for cultivated crops. Individual ravens were not associated exclusively with either of the subsidy-providing landscapes we considered, but instead all birds used both subsidized and other landscapes. Our research suggests that management of ravens during the non-breeding season and possibly during the breeding season, intended to reduce risk of predation on desert tortoises, will be most effective if conducted on a broad scale because of distances the birds travel and the lack of separation between putative “urban” and “natural” populations of ravens.

California↗
USGS2007

Enhanced Historical Land-Use and Land-Cover Data Sets of the U.S. Geological Survey

Historical land-use and land-cover data, available from the U.S. Geological Survey (USGS) for the conterminous United States and Hawaii, have been enhanced for use in geographic information systems (GIS) applications. The original digital data sets were created by the USGS in the late 1970s and early 1980s and were later converted by USGS and the U.S. Environmental Protection Agency (USEPA) to a geographic information system (GIS) format in the early 1990s. These data were made available on USEPA's Web site since the early 1990s and have been used for many national applications, despite minor coding and topological errors. During the 1990s, a group of USGS researchers made modifications to the data set for use in the National Water-Quality Assessment Program. These edited files have been further modified to create a more accurate, topologically clean, and seamless national data set. Several different methods, including custom editing software and several batch processes, were applied to create this enhanced version of the national data set. The data sets are included in this report in the commonly used shapefile and Tagged Image Format File (TIFF) formats. In addition, this report includes two polygon data sets (in shapefile format) representing (1) land-use and land-cover source documentation extracted from the previously published USGS data files, and (2) the extent of each polygon data file.

Data Series↗
USGS1989

A Computerized Data-Base System for Land-Use and Land-Cover Data Collected at Ground-Water Sampling Sites in the Pilot National Water Quality Assessment Program

Data-base software has been developed for the management of land-use and land-cover data collected by the U.S. Geological Survey as part of a pilot program to test and refine concepts for a National Water-Quality Assessment Program. This report describes the purpose, use, and design of the land-use and land-cover data-base software. The software provides capabilities for interactive storage and retrieval of land-use and land-cover data collected at ground-water sampling sites. Users of the software can add, update, and delete land-use and land-cover data. The software also provides capabilities to group, print, and summarize the data. The land-use and land-cover data-base software supports multiple data-base systems so that data can be accessed by persons in different offices. Data-base systems are organized in a tiered structure. Each data-base system contains all the data stored in the data-base systems located in the lower tiers of the structure. Data can be readily transmitted from lower tiers to high tiers of the structure. Therefore, the data-base system at the highest tier of the structure contains land-use and land-cover data for the entire pilot program.

Water-Resources Investigations Report↗
USGS2012

Role of remote sensing for land-use and land-cover change modeling

As the impacts of land-use and land-cover (LULC) change on carbon dynamics, climate change, hydrology, and biodiversity have been recognized, modeling of this transformational force has become increasingly important. Given the wide variety of applications that rely on the availability of LULC projections, modeling approaches have originated from a variety of disciplines, including geography, landscape ecology, economics, biology, and others. Initial modeling was often isolated within each discipline, but multidisciplinary modeling frameworks were developed as LULC modelers began to integrate the socioeconomic and biophysical components of LULC change. The empirical and theoretical basis for this work falls within land-use science, and this ‡eld documents both land-use and land-cover change, explains the coupled human-environment dynamics that produce the changes, and provides tools for producing spatially explicit LULC models (Mertens and Lambin, 1999; Rindfuss et al., 2004).

Book chapter↗
USGS2021

Developing a set of indicators to identify, monitor, and track impacts and change in forests of the United States

United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment (NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.

Climatic Change↗
USGS1987

BIA interpretation techniques for vegetation mapping using thematic mapper false color composites (interim report for San Carlos Reservation)

The Bureau of Indian Affairs (BIA) is responsible for the natural resource management of approximately 52 million acres of Trust lands in the contiguous United States. The lands are distributed in a "patchwork" fashion throughout the country. Management responsibilities on these areas include: minerals, range, timber, fish and wildlife, agricultural, cultural, and archaeological resources. In an age of decreasing natural resources and increasing natural resource values, effective multiple resource management is critical. BIA has adopted a "systems approach" to natural resource management which utilizes Geographic Information System (GIS) technology. The GIS encompasses a continuum of spatial and relational data elements, and included functional capabilities such as: data collection, data entry, data base development, data analysis, data base management, display, and report generalization. In support of database development activities, BIA and BLM/TGS conducted a cooperative effort to investigate the potential of 1:100,000 scale Thematic Mapper (TM) False Color Composites (FCCs) for providing vegetation information suitable for input to the GIS and to later be incorporated as a generalized Bureau wide land cover map. Land cover information is critical as the majority of reservations currently have no land cover information in either map or digital form. This poster outlines an approach which includes the manual interpretation of land cover using TM FCCs, the digitizing of interpreted polygons, and the editing of digital data, used upon ground truthing exercises. An efficient and cost-effective methodology for generating large area land cover information is illustrated for the Mineral Strip area on the San Carlos Indian Reservation in Arizona. Techniques which capitalize on the knowledge of the local natural resources professionals, while minimizing machine processing requirements, are suggested.

Arizona↗
USGS2013

A comprehensive change detection method for updating the National Land Cover Database to circa 2011

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.

Remote Sensing of Environment↗
USGS2018

Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region

To improve understanding of the distribution of ecologically important, ephemeral wetland habitats across the Great Plains, the occurrence and distribution of surface water in playa wetland complexes were documented for four different years across the Great Plains Landscape Conservation Cooperative (GPLCC) region. This information is important because it informs land and wildlife managers about the timing and location of habitat availability. Data with an accurate timestamp that indicate the presence of water, the percent of the area inundated with water, and the spatial distribution of playa wetlands with water are needed for a host of resource inventory, monitoring, and research applications. For example, the distribution of inundated wetlands forms the spatial pattern of available habitat for resident shorebirds and water birds, stop-over habitats for migratory birds, connectivity and clustering of wetland habitats, and surface waters that recharge the Ogallala aquifer; there is considerable variability in the distribution of playa wetlands holding water through time. Documentation of these spatially and temporally intricate processes, here, provides data required to assess connections between inundation and multiple environmental drivers, such as climate, land use, soil, and topography. Climate drivers are understood to interact with land cover, land use and soil attributes in determining the amount of water that flows overland into playa wetlands. Results indicated significant spatial variability represented by differences in the percent of playas inundated among States within the GPLCC. Further, analysis-of-variance comparison of differences in inundation between years showed significant differences in all cases. Although some connections with seasonal moisture patterns may be observed, the complex spatial-temporal gradients of precipitation, temperature, soils, and land use need to be combined as covariates in multivariate models to effectively account for these patterns. We demonstrate the feasibility of using classification of Landsat satellite imagery to describe playa-wetland inundation across years and seasons. Evaluating classifications representing only 4 years of imagery, we found significant year-to-year and state-to-state differences in inundation rates.

Great Plains Landscape Conservation Cooperative↗
USGS2024

The interplay of future solar energy, land cover change, and their projected impacts on natural lands and croplands in the US

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by −16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %–2.4 % of croplands and 0.3 %–0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

conterminous United States↗
USGS2014

Land cover mapping of Greater Mesoamerica using MODIS data

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: ( i ) qualitative assessment provided good insight in identifying and correcting gross errors; ( ii ) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest ( r 2 &ensp;=&ensp;0.88), shrubland ( r 2 &ensp;=&ensp;0.75), and cropland ( r 2 &ensp;=&ensp;0.97) but weak positive association for grassland ( r 2 &ensp;=&ensp;0.26); and ( iii ) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Canadian Journal of Remote Sensing↗