landcover

The USDA, NASS Cropland Data Layer (CDL) of the US for the growing seasons 1997 through 2013 are available using CropScape. Data for some states may not be available for one or more years.
 
 

Two of the most widely used land-cover data sets for the United States are the National Land-Cover Data (NLCD) at 30-m resolution and the Global Land- Cover Characteristics (GLCC) at 1-km nominal resolution. Both data sets were produced around 1992 and expected to provide similar land-cover information. This study investigated the spatial distribution of NLCD within major GLCC classes at 1-km unit over a total of 11 agricultural-related eco-regions across the continental United States. Our results exhibited that data agreement or relationship between the GLCC and NLCD was higher for the eco-regions located in the corn belt plains with homogeneous or less complicated land-cover distributions. The GLCC cropland primarily corresponded to NLCD row crops, pasture/hay and small grains, and was occasionally related to NLCD forest, grassland and shrubland in the remaining eco-regions due to high land-cover diversity. The unique GLCC classes of woody savanna and savanna were mainly related to the NLCDorchard and grassland, respectively, in the eco-region located in the Central Valley of California. The GLCC urban/built-up among vegetated areas strongly agreed to the NLCD urban for the eco-regions in the corn belt plains. A set of subclass land-cover information provided through this study is valuable to understand the degrees of spatial similarity for the major global vegetated classes. The subclass information from this study provides reference for substituting less-detailed global data sets for detailed NLCD to support national environment studies.

Many investigators need and use global land cover maps for a wide variety of purposes. Ironically, after many years of very limited availability, there are now multiple global land cover maps and it is not readily apparent (1) which is most useful for particular applications or (2) how to combine the different maps to provide an improved dataset. The existing global land cover maps at 1 km spatial resolution have arisen from different initiatives and are based on different remote sensing data and employed different methodologies. Perhaps more significantly, they have different legends. As a result, comparison of the different land cover maps is difficult and information about their relative utility is limited. In an attempt to compare the datasets and assess their strengths and weaknesses we harmonized the thematic legends of four available coarse-resolution global land cover maps (IGBP DISCover, UMD, MODIS 1-km, and GLC2000) using the LCCS-based land cover legend translation protocols. Analysis of the agreement among the global land cover maps and existing validation information highlights general patterns of agreement, inconsistencies and uncertainties. The thematic classes of Evergreen broadleaf trees, Snow and Ice, and Barren show high producer and user accuracy and good agreement among the datasets, while classes of mixed tree types show high commission errors. Overall, the results show a limited ability of the four global products to discriminate mixed classes characterized by a mosaic of trees, shrubs, and herbaceous vegetation. There is a strong relationship between class accuracy, spatial agreement among the datasets, and the heterogeneity of landscapes. Suggestions for future mapping projects include careful definition of mixed unit classes, and improvement in mapping heterogeneous landscapes.

The U.S. Geological Survey (USGS) 2001 National Land Cover Database (NLCD) was compared to the U.S. Department of Agriculture (USDA) 2002 Census of Agriculture. Wecompared areal estimates for cropland at the state and county level for 14 States in the Upper Midwest region of the United States. Absolute differences between the NLCD and Census cropland areal estimates at the state level ranged from 1.3% (Minnesota) to 37.0% (Wisconsin). The majority of counties (74.5%) had differences of less than 100 km2. 7.2% of the counties had differences of more than 200 km2. Regions where the largest areal differences occurred were in southern Illinois, North Dakota, South Dakota, and Wisconsin, and generally occurred in areas with the lowest proportions of cropland (i.e., dominated by forest or grassland). Before using the 2001 NLCD for agricultural applications, such as mapping of specific crop types, users should be aware of the potential for misclassification errors, especially where the proportion of cropland to other land cover types is fairly low.

Land-use change models are used by researchers and professionals to explore the dynamics and drivers of land-use/land-cover change and to inform policies affecting such change. A broad array of models and modeling methods are available to researchers, and each type has certain advantages and disadvantages depending on the objective of the research. This report presents a review of different types of models as a means of exploring the functionality and ability of different approaches. In this review, we try to explicitly incorporate human processes, because of their centrality in land-use/land-cover change. We present a framework to compare land-use change models in terms of scale (both spatial and temporal) and complexity, and how well they incorporate space, time, and human decisionmaking. Initially, we examined a summary set of 250 relevant citations and developed a bibliography of 136 papers. From these 136 papers a set of 19 land-use models were reviewed in detail as
representative of the broader set of models identified from the more comprehensive review of literature. Using a tabular approach, we summarize and discuss the 19 models in terms of dynamic (temporal) and spatial interactions, as well as human decisionmaking as defined by the earlier framework. To eliminate the general confusion surrounding the term scale, we evaluate each model with respect to pairs of analogous parameters of spatial, temporal, and decisionmaking scales: (1) spatial resolution and extent, (2) time step and duration, and (3) decisionmaking agent and domain. Although a wide range of spatial and temporal scales is
covered collectively by the models examined, we find most individual models occupy a much more limited spatio temporal niche. Many raster models we examined mirror the extent and resolution of common remote-sensing data. The broadest-scale models are, in general, not spatially explicit. We also find that models incorporating higher levels of human decision making are more centrally located with respect to spatial and temporal scales,
probably due to the lack of data availability at more extreme scales. Further, we examine the social drivers of land-use change and methodological trends exemplified in the models we reviewed. Finally, we conclude with some proposals for future directions in land-use modeling.

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.

This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

This study presents the results of comparing land use estimates between three different data sets for the Upper Mississippi River Basin (UMRB). The comparisons were performed between the U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) National Resource Inventory (NRI), the U.S. Geological Survey (USGS) National Land Cover Data (NLCD) database, and a combined USDA National Agricultural Statistics Service (NASS) Agricultural Census – NLCD dataset created to support applications of the Hydrologic Unit Model for the U.S. (HUMUS). The comparison was performed for 1992 versions of the datasets because that was the only consistent year available among all three data sources. The results show that differences in land use area estimates increased as comparisons shifted from the entire UMRB to smaller 4- and 8-digit watershed regions (as expected). However, the area estimates for the major land use categories remained generally consistent among all three data sets across each level of spatial comparison. Differences in specific crop and grass/forage land use categories were magnified with increasing refinement of the spatial unit of comparison, especially for close-grown crops, pasture, and alfalfa/hayland.

We quantify the emergence of biofuel markets and its impact on U.S. and world agriculture for the coming decade using the multi-market, multi-commodity international FAPRI (Food and Agricultural Policy Research Institute) model. The model incorporates the trade-offs between biofuel, feed, and food production and consumption and international feedback effects of the emergence through world commodity prices and trade. We examine land allocation by type of crop, and pasture use for countries growing feedstock for ethanol (corn, sorghum, wheat, sugarcane, and other grains) and major crops competing with feedstock for land resources such as oilseeds. We shock the model with exogenous changes in ethanol demand, first in the United States, then in Brazil, China, the European Union-25, and India, and compute shock multipliers for land allocation decisions for crops and countries of interest. The multipliers show at the margin how sensitive land allocation is to the growing demand for ethanol. Land moves away from major crops and pasture competing for resources with feedstock crops. Because of the high U.S. tariff on ethanol, higher U.S. demand for ethanol translates into a U.S. ethanol production expansion. The latter has global effects on land allocation as higher coarse grain prices transmit worldwide. Changes in U.S. coarse grain prices also affect U.S. wheat and oilseed prices, which are all transmitted to world markets. In contrast, expansion in Brazil ethanol use and production chiefly affects land used for sugarcane production in Brazil and to a lesser extent in other sugarproducing countries, but with small impacts on other land uses in most countries.