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Limin Yang

Publications and source records attributed to Limin Yang.

32 records · Page 2Linked to original sources
USGS2003

Effects of landscape characteristics on land-cover class accuracy

The effects of patch size and land-cover heterogeneity on classification accuracy were evaluated using reference data collected for the National Land-Cover Data (NLCD) set accuracy assessment. Logistic regression models quantified the relationship between classification accuracy and these landscape variables for each land-cover class at both the Anderson Levels I and II classification schemes employed in the NLCD. The general relationships were consistent, with the odds of correctly classifying a pixel increasing as patch size increased and decreasing as heterogeneity increased. Specific characteristics of these relationships, however, showed considerable diversity among the various classes. Odds ratios are reported to document these relationships. Interaction between the two landscape variables was not a significant influence on classification accuracy, indicating that the effect of heterogeneity was not impacted by the sample being in a small or large patch. Landscape variables remained significant predictors of class-specific accuracy even when adjusted for regional differences in the mapping and assessment processes or landscape characteristics. The land-cover class-specific analyses provide insight into sources of classification error and a capacity for predicting error based on a pixel's mapped land-cover class, patch size and surrounding land-cover heterogeneity.

Remote Sensing of Environment↗
USGS2003

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Photogrammetric Engineering and Remote Sensing↗
USGS2003

Regional forest land cover characterisation using medium spatial resolution satellite data

Increasing demands on forest resources require comprehensive, consistent and up-to-date information on those resources at spatial scales appropriate for management decision-making and for scientific analysis. While such information can be derived using coarse spatial resolution satellite data (e.g. Tucker et al. 1984; Zhu and Evans 1994; Cihlar et al. 1996; Cihlar et al., Chapter 12), many regional applications require more spatial and thematic details than can be derived by using coarse resolution imagery. High spatial resolution satellite data such as IKONOS and Quick Bird images (Aplin et al. 1997), though usable for deriving detailed forest information (Culvenor, Chapter 9), are currently not feasible for wall-to-wall regional applications because of extremely high data cost, huge data volume, and lack of contiguous coverage over large areas. Forest studies over large areas have often been accomplished using data acquired by intermediate spatial resolution sensor systems, including the Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) of Landsat, the High Resolution Visible (HRV) of the Systeme Pour l'Observation de la Terre (SPOT), and the Linear Image Self-Scanner (LISS) of the Indian Remote Sensing satellite. These sensor systems are more appropriate for regional applications because they can routinely produce spatially contiguous data over large areas at relatively low cost, and can be used to derive a host of forest attributes (e.g. Cohen et al. 1995; Kimes et al. 1999; Cohen et al. 2001; Huang et al. 2001; Sugumaran 2001). Of the above intermediate spatial resolution satellites, Landsat is perhaps the most widely used in various types of land remote sensing applications, in part because it has provided more extensive spatial and temporal coverage of the globe than any other intermediate resolution satellite. Spatially contiguous Landsat data have been developed for many regions of the globe (e.g. Lunetta and Sturdevant 1993; Fuller et al. 1994b; Skole et al. 1997), and a circa 1990 Landsat image data set covering the entire land area of the globe has also been developed recently (Jones and Smith 2001). An acquisition strategy aimed at acquiring at least one cloud free image per year for the entire land area of the globe has been initiated for Landsat-7 (Arvidson et al. 2001). This will probably ensure the continued dominance of Landsat in the near future.

Book chapter↗
USGS2002

Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping

FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for deciduous/evergreen/mixed and forest type group classifications were around 80 percent and 65 percent, respectively, and were consistent in the two regions. These results suggest that use of FIA plot data together with satellite imagery and relevant ancillary data may substantially improve the efficiency, accuracy, and consistency of large area forest land cover mapping.

Conterminous United States↗
USGS2002

Development of a circa 2000 land cover database for the United States

Multi-Resolution Land Characterization 2000 (MRLC 2000) is a second-generation federal consortium to create an updated pool of nation-wide Landsat 7 imagery, and derive a second-generation National Land Cover Database (NLCD 2000). This multi-layer, multisource database will include a suite of 30-meter resolution data that will serve as standardized ingredients for the production of land cover – both nationally and locally. This database will also provide the framework to allow flexibility in developing and applying suites of independent data layers. These nationally standardized independent data layers or components, will be useful not only within the land-cover classification but as data themes for other applications. This database will consist of the following components: (1) normalized tasseled cap (TC) transformations of Landsat 7 imagery for three time periods per scene (early, peak and late), (2) ancillary data layers, including 30m DEM derivatives of slope, aspect and elevation and three STATSCO soil derivatives, (4) image shape and texture information, (5) image derivatives of percent imperviousness and percent tree canopy per-pixel, (6) classified land-cover data derived from the Tassel Capped imagery, ancillary data and derivatives, (7) classification rules and metadata from the land cover classification, allowing future users the potential to modify rules to derive land cover products tailored to their specific local applications. In a pilot study application of the database concept, two mapping zones (Utah and Virginia) were selected for full generation of the above data components. Three derivative layers including, per-pixel imperviousness, per-pixel canopy and land cover were classified from the database. Cross validation accuracies for land cover ranged from 65-82%, and mean absolute error values of 10-15% were reported for percent tree canopy and imperviousness.

conterminous United States↗
USGS2001

Thematic accuracy of MRLC land cover for the eastern United States

One objective of the MultiResolution Land Characteristics (MRLC) consortium is to map general land-cover categories for the conterminous United States using Landsat Thematic Mapper (TM) data. Land-cover mapping and classification accuracy assessment are complete for the eastern United States. The accuracy assessment was based on photo-interpreted reference data obtained from a stratified probability sample of pixels. Agreement was defined as a match between primary or alternate reference land-cover labels assigned to each sample pixel and the mode (most common class) of the map's land-cover labels within a 3×3-pixel neighborhood surrounding the sampled point. At 30-m resolution, overall accuracy was 59.7% at an Anderson Level II thematic detail, and 80.5% at Anderson Level I.

Remote Sensing of Environment↗
USGS2001

A Landsat 7 scene selection strategy for a national land cover database

A strategy for selecting Landsat 7 ETM+ imagery for development of a new generation national land cover database of the United States has been developed. This strategy is formulated to target Landsat 7 ETM+ scenes based on land cover and land use, vegetation phenology and image quality (cloudiness, haze). Criteria based on phenology and scene quality provide a national baseline for acquiring Landsat 7 data. Optimal time periods for discriminating land cover types were identified for each Landsat 7 path-row footprint and each proposed land cover mapping zone (mosaic of several path-rows based on landscape and ecoregion), from which three Landsat scenes were selected. This database of selected scenes is used to guide Landsat 7 data purchasing. This methodology provides a consistent framework for populating Landsat 7 imagery to be used for a new national land cover characterization initiative.

Conference Paper↗
USGS2001

A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas

Forest cover is of great interest to a variety of scientific and land management applications, many of which require not only information on forest categories, but also tree canopy density. In previous studies, large area tree canopy density had been estimated at spatial resolutions of 1km or coarser using coarse resolution satellite images. In this study, a strategy is developed for estimating tree canopy density at a spatial resolution of 30 m. This strategy is based on empirical relationships between tree canopy density and Landsat data, established using linear regression and regression tree techniques. One-meter digital orthophoto quadrangles were used to derive reference tree canopy density data needed for calibrating the relationships between canopy density and Landsat spectral data. This strategy was tested over three areas of the United States. In general, models derived using both linear regression and regression tree techniques were statistically significant. The regression tree was found more robust than linear regression, primary due to its capability of approximating complex non-linear relationships using a set of linear equations. This strategy will be recommended for use in developing a nation wide tree canopy density data set at a 30 m resolution as part of the Multi-Resolution Land Characteristics 2000 project.

Conference Paper↗
USGS2000

Integration of a numerical model and remotely sensed data to study urban/rural land surface climate processes

Simulation of urban/rural land surface climate processes using boundary layer climate models requires accurate input data with regard to surface thermal and radiative properties. The research reported here resulted in development of a procedure to integrate the satellite-derived surface biophysical parameters with a boundary layer climate model for simulating spatial surface energy exchange. The procedure was tested through spatial surface energy balance simulation of an urban/rural landscape in eastern Nebraska. The modeled surface temperature and net radiation were compared to those derived from the concurrent satellite data. The errors of the modeled surface temperature were small, and were mainly attributed to uncertainties in the estimation of surface moisture availability and satellite-derived surface radiant temperature. Modeled net radiation was also in agreement with the values calculated from satellite data. Modeled turbulent heat fluxes were in general agreement as compared to those reported in the literature, but the model tended to overestimate the latent heat flux for most rural land cover types. It was concluded that by incorporation of satellite-derived surface physical parameters into a boundary layer model, simulation of spatial land surface climate processes was much improved. The method and procedures developed from this study can be utilized in other boundary layer climate models.

Nebraska↗
USGS2000

Accuracy assessment for the U.S. Geological Survey Regional Land-Cover Mapping Program: New York and New Jersey Region

The U.S. Geological Survey, in cooperation with other government and private organizations, is producing a conterminous U.S. land-cover map using Landsat Thematic Mapper 30-meter data for the Federal regions designated by the U.S. Environmental Protection Agency. Accuracy assessment is to be conducted for each Federal region to estimate overall and class-specific accuracies. In Region 2, consisting of New York and New Jersey, the accuracy assessment was completed for 15 land-cover and land-use classes, using interpreted 1:40,000-scale aerial photographs as reference data. The methodology used for Region 2 features a two-stage, geographically stratified approach, with a general sample of all classes (1,033 sample sites), and a separate sample for rare classes (294 sample sites). A confidence index was recorded for each land-cover interpretation on the 1:40,000-scale aerial photography The estimated overall accuracy for Region 2 was 63 percent (standard error 1.4 percent) using all sample sites, and 75.2 percent (standard error 1.5 percent) using only reference sites with a high-confidence index. User's and producer's accuracies for the general sample and user's accuracy for the sample of rare classes, as well as variance for the estimated accuracy parameters, were also reported. Narrowly defined land-use classes and heterogeneous conditions of land cover are the major causes of misclassification errors. Recommendations for modifying the accuracy assessment methodology for use in the other nine Federal regions are provided.

Photogrammetric Engineering and Remote Sensing↗
USGS1998

An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains

Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989–1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negative correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R 2 ) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models . The photosynthetic pathway is an important determinant of grassland performance with northern mixed prairie (mixture of C 3 and C 4 grassland) TI NDVI affected by both thermal and moisture conditions during the growing season while southern plains grasslands (primarily C 4 grassland) were predominantly influenced by spring and summer precipitation. Grassland land cover classes associated with sandy soils also demonstrated a strong relationship between TI NDVI and growing season rainfall. Significant impact of interannual climate variability on the TI NDVI–climate relationship was also observed. The study suggests an integrated approach involving numerical models, satellite remote sensing, and field observations to monitor grassland ecosystem dynamics on a regional scale.

Remote Sensing of Environment↗
USGS1997

Seasonal vegetation characteristics of the United States

The U.S. Geological Survey's EROS Data Center has created a prototype 1‐km resolution data base of vegetation seasonal characteristics. The characteristics are derived from time‐series NDVI data collected by the AVHRR satellite sensor. Information covering the 5 years 1989–1993 is included in the data base. Although quantitative validation of the seasonal characteristics cannot be made until several evaluation efforts are completed, general observations are possible by viewing images of the seasonal parameters. Figures 2 through 8 show several examples of the seasonal characteristics data base.

Geocarto International↗