National Land Cover Map Research Articles

Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture

Over 22 million hectares (ha) of U.S. croplands are irrigated. Irrigation is an intensified agricultural land use that increases crop yields and the practice affects water and energy cycles at, above, and below the land surface. Until recently, there has been a scarcity of geospatially detailed information about irrigation that is comprehensive, consistent, and timely to support studies tying agricultural land use change to aquifer water use and other factors. This study shows evidence for a recent overall net expansion of 522 thousand ha across the U.S. (2.33%) and 519 thousand ha (8.7%) in irrigated cropped area across the High Plains Aquifer (HPA) from 2002 to 2007. In fact, over 97% of the net national expansion in irrigated agriculture overlays the HPA. We employed a modeling approach implemented at two time intervals (2002 and 2007) for mapping irrigated agriculture across the conterminous U.S. (CONUS). We utilized U.S. Department of Agriculture (USDA) county statistics, satellite imagery, and a national land cover map in the model. The model output, called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the U.S. (MIrAD-US), was then used to reveal relatively detailed spatial patterns of irrigation change across the nation and the HPA. Causes for the irrigation increase in the HPA are complex, but factors include crop commodity price increases, the corn ethanol industry, and government policies related to water use. Impacts of more irrigation may include shifts in local and regional climate, further groundwater depletion, and increasing crop yields and farm income.

Object-based approach to national land cover mapping using HJ satellite imagery

To meet the carbon storage estimate in ecosystems for a national carbon strategy, we introduce a consistent database of China land cover. The Chinese Huan Jing (HJ) satellite is proven efficient in the cloud-free acquisition of seasonal image series in a monsoon region and in vegetation identification for mesoscale land cover mapping. Thirty-eight classes of level II land cover are generated based on the Land Cover Classification System of the United Nations Food and Agriculture Organization that follows a standard and quantitative definition. Twenty-four layers of derivative spectral, environmental, and spatial features compose the classification database. Object-based approach characterizing additional nonspectral features is conducted through mapping, and multiscale segmentations are applied on object boundary match to target real-world conditions. This method sufficiently employs spatial information, in addition to spectral characteristics, to improve classification accuracy. The algorithm of hierarchical classification is employed to follow step-by-step procedures that effectively control classification quality. This algorithm divides the dual structures of universal and local trees. Consistent universal trees suitable to most regions are performed first, followed by local trees that depend on specific features of nine climate stratifications. The independent validation indicates the overall accuracy reaches 86%.

Hyper‐temporal remote sensing helps in relating epiphyllous liverworts and evergreen forests

AbstractQuestionIs there, at the macro‐habitat scale, a relationship between the fraction of evergreen forests and the presence probability of epiphyllous liverworts? Can these two parameters be estimated and mapped using an NDVI‐based indicator that is derived from time‐series of SPOT‐VGT imagery?LocationSouthern China.MethodsApplying the ISODATA algorithm, we classified SPOT‐VGT NDVI time‐series imagery and produced an NDVI map at 1‐km2 resolution containing 128 NDVI classes. That map and the National Land Cover Map of China (2000) were used to prepare a scheme for field sampling. For 537 1‐km2 areas, located in 19 blocks of 100 km × 100 km, field data were collected. They represented 51 preselected NDVI classes that were assumed to contain evergreen forests. Data on the fractions of forest and evergreen forest, the fraction of evergreen forest in forest present and presence probability of epiphyllous liverworts were regressed against different NDVI class‐specific indicators by means of weighted least‐squares regression (WLSR).ResultsThe SPOT‐VGT NDVI for March was found to best explain the variation between 1‐km2 areas in the presence probability of epiphyllous liverworts (R2 = 0.64; linear relationship; RMSE = 0.38) as the area fraction (%) of evergreen forest (R2 = 0.90; exponential relationship; RMSE = 6.1%). Epiphyllous liverworts were only found within 1‐km2 areas when the SPOT‐VGT NDVI value for March was more than 0.50 (model estimate: 0.43 ± 0.20) and the fraction of evergreen forest in 1 km2 was above 14% (model estimate: 5 ± 35%). The estimation errors (with 95% confidence interval) of these two relationships were calculated using a bootstrap resampling method with 1000 replications; they were, respectively, 0.49–0.76 and 0.85–0.94 for R2, and 0.11–0.23 and 5.2–11.9% for RMSE. Other positive relationships were found between the presence probability of epiphyllous liverworts and the fraction of evergreen forest (R2 = 0.64, linear relationship; RMSE = 0.38) and between the fraction of evergreen forest and forest within 1‐km2 areas (R2 = 0.80, linear relationship; RMSE = 29%).ConclusionWe show that for southern China, the fraction of evergreen forest and the presence probability of epiphyllous liverworts can directly be inferred by making use of SPOT‐VGT NDVI imagery. The findings are fully consistent with earlier reported hotspots for epiphyllous liverworts. At the macro‐habitat scale, the presence probability of epiphyllous liverworts proved quantitatively related to the fraction of evergreen forest. This suggests causal relationships with patch size and/or rainfall patterns. We believe that the derived maps may serve as a foundation for a range of further studies.

A comparison between cluster busting technique and a classification tree algorithm of a moderate resolution imaging spectrometer (MODIS) land cover map of Honduras

A national land cover map derived from moderate resolution imaging spectrometer (MODIS) imagery products was developed for Honduras, Central America. We compared two methods of image classification: a cluster busting (CB) classification technique and a classification and regression tree (CART) algorithm. Field data samples were used to validate the resulting classifications. Inthe classification process, we used: a Google Earth™ sampling scheme, a time series of MODIS's Enhanced Vegetation Index (EVI) and digital elevation data(shuttle radar topography mission, SRTM). The CART classification method provided a more accurate classification (Kappa coefficient, K = 74%, overall model accuracy = 79.6%) while compared to the CB classification (Kappa coefficient, K = 9%, overall model accuracy = 25.1%). The findings are useful to design more accurate MODIS classification protocols in tropical countries.

National Land Cover Mapping by Remote Sensing Under the Control of Interpreted Data

National Land Cover Mapping by Remote Sensing Under the Control of Interpreted Data

Habitat analysis of North American sand flies near veterans returning from leishmania-endemic war zones

BackgroundNearly 1300 cases of leishmaniasis have been identified in American military personnel deployed to Iraq and Afghanistan. The symptoms of this disease can range from a mild, self-limiting cutaneous infection to a deadly visceral infection and are not prevented by chemoprophylaxis or immunization. Effective treatments, however, are available. The disease-causing parasite is spread through the bite of the female sand fly. Although the disease occurs in both the Old World and the New World, the parasite species differ between the hemispheres. The large number of cases in military veterans has caused some concern that Old World, temperate-adapted parasite species could be introduced into the native sand fly populations of American military facilities where veterans of the current conflicts return following their deployments. This paper reports part of a larger study to analyze the risk of such an accidental importation. Four potential habitats on two large Army facilities in the Southeast United States were surveyed to determine relative sand fly density. The National Land Cover Map was used to provide sand fly density prediction maps by habitat.ResultsSand fly density was significantly higher in deciduous forest and even higher at the interface between forest and open grassland. The evergreen forest and agricultural fields supported very low densities. On Fort Campbell, KY, the percentage of land covered by suitable habitat was very high. A sand fly density prediction map identified large tracts of land where infected individuals would be at higher risk of exposure to sand fly bites, resulting in an increased risk of introducing the parasite to a native insect population. On Fort Bragg, NC, however, commercial farming of long leaf pine reduced the percentage of the land covered in vegetation suitable for the support of sand flies. The risk of introducing an exotic Leishmania spp. on Fort Bragg, therefore, is considered to be much lower than on Fort Campbell.ConclusionA readily available land cover product can be used at the regional level to identify areas of sand fly habitat where human populations may be at higher risk of exposure. The sand fly density prediction maps can be used to direct further surveillance, insect control, or additional patient monitoring of potentially infected soldiers.

A Study on Advancing Strategy forNational Environmental Geographic Informations-Mainly focused on the National Environmental Assessment Map, Ecological Map and Land Cover Map-

A Study on Advancing Strategy forNational Environmental Geographic Informations-Mainly focused on the National Environmental Assessment Map, Ecological Map and Land Cover Map-

Mapping conifer understory within boreal mixedwoods from Landsat TM satellite imagery and forest inventory information

Information about conifer understory within deciduous-dominated mixed-wood stands would increase the possible range of management options for these complex communities, yet cost-effective and accurate inventory methods remain elusive. Maps of conifer understory produced from field-checked photo interpretation were compared with classified images created from Landsat Thematic Mapper data and two image classifiers. The highest accuracy achieved was 71% using an evidential reasoning classifier that integrated satellite remote sensing observations with stand inventory information. The image map did provide an advantage by capturing some of the spatial variability of conifer understory that is not captured by photo interpretation methods. Predicting the presence and spatial distribution of conifer understory is difficult because its establishment is influenced by many factors such as ecosite, available substrate, distance to seed source and mechanisms of recruitment that are not typically available in spatial formats. The image maps are considered estimates that may be suitable for broad strategic planning, and may serve as validation information for national satellite land cover mapping initiatives. Keywords: boreal mixedwood, conifer understory, image classification, Landsat TM, reflectance, forest inventory, vegetation index

National land cover mapping by remote sensing

Policy makers need current, accurate information upon which to base decisions. Viable land cover data, especially in developing countries, is often unavailable. Satellite remote sensing is an appropriate and effective source for national land cover mapping. The value of land cover information is enhanced by being one component within a Geographic Information System (GIS). The primary issues and procedures for land cover mapping using remote sensing including scale, sensor characteristics, image dates, appropriate land system classes, analysis strategies, end products, and GIS integration are reviewed. A case study for Afghanistan is provided as an example.