Spatial Data Catalog

Internal feature number.

Sequential unique whole numbers that are automatically generated.

Feature geometry.

Coordinates defining the features.

Length of feature in internal units.

Positive real numbers that are automatically generated.

Area of feature in internal units squared.

Positive real numbers that are automatically generated.

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Landsat image selection and preprocessing. For the change analysis, a two-date pair of Landsat scenes was selected for each path/row restricting temporal range to reduce the impact of seasonal and phenological variation. A pre-processing step was performed to convert the digital number to top of atmosphere reflectance using procedures similar to those established for the NLCD 2001 mapping effort (Homer et al., 2004). Reflectance derivatives, including a tasseled-cap transformation and a 3-ratio index, were generated for each scene to use in the modeling process as independent variables. Where present, clouds and cloud shadows were digitized for masking. NLCD 2006 Percent Developed Imperviousness (Final Product) and Percent Developed Imperviousness Change Analysis. Because the four NLCD developed classes are derived from a percent imperviousness mapping product, an overview of steps required to update the NLCD 2001 imperviousness to reflect urban growth captured in 2006 era Landsat imagery is provided here (Xian, et al., 2010). First, 2001 nighttime lights imagery from the NOAA Defense Meteorological Satellite Program (DMSP) was imposed on the NLCD 2001 impervious surface product to exclude low density imperviousness outside urban and suburban centers so that only imperviousness in urban core areas would be used in the training dataset. Two training datasets, one having a relatively larger urban extent and one having a smaller extent, were produced through imposing two different thresholds on city light imagery. Second, each of the two training datasets combined with 2001 Landsat imagery was separately applied using a regression tree (RT) algorithm to build up RT models. Two sets of RT models were then used to estimate percent imperviousness and to produce two 2001 synthetic impervious surfaces. Similarly, the same two training datasets were used with 2006 Landsat imagery to create two sets of RT models that produce two 2006 synthetic impervious surfaces. Third, the 2001 and 2006 synthetic impervious surface pairs were compared using both 2001 impervious surface products to retain 2001 impervious surface area (ISA) in the unchanged areas. The 2006 DMSP nighttime lights imagery was then employed to ensure that non-imperviousness areas were not included and that new impervious surfaces emerged in the city light extent. After this step, two 2006 intermediate impervious surfaces were produced. Finally, the two intermediate products and 2001 imperviousness were compared to remove false estimates in non-urban areas and generate a 2006 impervious surface estimate. Imperviousness threshold values used to derive the NLCD developed classes are: (1) developed open space (imperviousness < 20%), (2) low-intensity developed (imperviousness from 20 - 49%), (3) medium intensity developed (imperviousness from 50 -79%), and (4) high-intensity developed (imperviousness > 79%). During this process, inconsistencies in the NLCD 2001 Percent Developed Imperviousness product were corrected with the new product, NLCD 2001 Percent Developed Imperviousness Version 2.0, included as part of the NLCD 2006 product release. Land Cover Change Analysis. For the NLCD 2006 Land Cover Update, a new change detection method, Multi-Index Integrated Change (MIIC), was developed to capture a full range of land cover disturbance and potential land cover change patterns for updating the National Land Cover Database (Jin, et al., In Preparation). Recognizing the potential complementary nature of multiple spectral indices in detection of different land cover changes, we integrated four indices into one model to more accurately detect true land cover changes between two time periods. Within the model, normalized burn ratio (NBR), change vector (CV, Xian, et al., 2009), relative change vector (RCV), and normalized difference vegetation index (NDVI) are calculated separately for the early date (circa 2001) and late date (circa 2006) scenes. The four pairs of indices for the two dates are differenced and then evaluated in a final model conditional statement that categorizes each pixel as either biomass increase, biomass decrease, or no change. Individual path/row raw results from this change analysis process are assembled into a seamless national product to form the NLCD 2001/2006 Maximum Potential Change map. The integrated change result is clumped and sieved to produce a refined change/no-change mask used below. NLCD 2006 Land Cover Classification. Land cover mapping protocols used during NLCD 2006 processing are similar to those used to label the NLCD 2001 product (Homer, et al., 2004), but applied on a path/row basis instead of multiple path/row MRLC zones (Xian, et al., 2009). Classification was achieved using decision tree modeling that employed a combination of Landsat imagery, reflectance derivatives, and ancillary data (independent variables) with training data points (dependent variable) collected from a refined version of the NLCD 2001 land cover product. Training points were randomly sampled and limited to those areas that were determined to be unchanged between 2001 and 2006 during the MIIC spectral change analysis process. Training data for pixels changed to developed land cover were not collected since the four classes in urban and sub-urban areas were mapped separately using a regression tree modeling method (described in the Imperviousness Change Analysis process steps above). Post classification modeling and hand-editing were used to further refine the decision tree output. Following classification, the 2006 land cover was masked with the change/no-change result (captured during the MIIC change analysis modeling) to extract a label for spectrally changed pixels. Labeled change pixels were then compared to the NLCD 2001 land cover base to exclude those pixels identified as spectral change, but classified with the same label as the corresponding 2001 pixel. NLCD 2006 percent developed impervious pixels, identified as changed, were extracted to NLCD developed class codes using NLCD 2001 legend thresholds for developed classes and added to the change pixel map. This intermediate change pixel product was generalized using the NLCD Smart Eliminate tool with the following minimum mapping units (mmu) applied: 1 acre (approximately 5 ETM+ 30 m pixel patch) for developed classes (class codes 21, 22, 23, and 24); 7.12 acres (approximately 32 ETM+ pixel patch) for agricultural classes (class codes 81 and 82); and 2.67 acres (approximately 12 ETM+ pixel patch) for all other classes (class codes 11, 12, 31, 41, 42, 43, 52, 71, 90, and 95). The smart eliminate aggregation program subsumes pixels from the single pixel level to the mmu pixel patch using a queens algorithm at doubling intervals. The algorithm consults a weighting matrix to guide merging of cover types by similarity, resulting in a product that preserves land cover logic as much as possible. During the NLCD 2006 analysis and modeling process, inconsistencies in the NLCD 2001 Land cover product were corrected with the new product, NLCD 2001 Land Cover Version 2.0, included as part of the NLCD 2006 product release. NLCD 2006 Land Cover (Final Product). Additional processing steps were designed to create the final NLCD 2006 land cover map. Individual path/row change pixel results were assembled to form an intermediate seamless national product. This seamless change pixel map was reviewed and edited to remove regional inconsistencies. Refined NLCD 2006 change pixels were then combined with the re-issued NLCD 2001 Land Cover Version 2.0, and the resulting image was smart-eliminated to a 5-pixel mmu. This final step eliminated single pixels and patches less than 5 pixels in extent that appeared as a result of combining the separate images. NLCD 2006 Change Pixels (Final Product). A comparison of the NLCD 2001 re-issued base and the NLCD 2006 Land Cover was necessary to extract a final version of the NLCD 2006 Change Pixels. In a model, pixels that were labeled with the same land cover class code were removed and only those pixels that did not agree in the two classifications were retained as final NLCD 2006 Change Pixels. NLCD 2001/2006 Percent Developed Imperviousness Change (Supplementary Raster Layer). The NLCD 2001 Percent Developed Imperviousness Version 2.0 and the NLCD 2006 Percent Developed Imperviousness were compared in a model to provide the user community with a layer that highlights imperviousness change between 2001 and 2006. NLCD 2006 Maximum Potential Spectral Change (Supplementary Raster Layer). A raster layer containing all pixels identified in the raw change detection process and additional pixels identified as changed in NOAA C-CAP 2001-2006 change products. Raw change includes areas of biomass increase (value 1) and biomass decrease (value 2) with background (127) and clouds (value 250) identified separately. Only a portion of these pixels were ultimately selected as real change during our final protocols. This product was assembled from individual path/row MIIC raw change results. NLCD 2006 From-To Change Pixels (Supplementary Raster Layer). Although similar to the NLCD 2006 change pixel map, the from-to change pixel image was derived from a direct comparison between the re-issued seamless NLCD 2001 Land Cover Version 2.0 Map and the seamless NLCD 2006 Land Cover Map. An index value for each possible change combination was assigned using a from-to change matrix with sequentially numbered cells (see matrix and index values in entity and attribute section). Pixels are labeled with an index value created from a matrix of every possible change combination (see entity and attribute information for details). NLCD 2006 Path/Row Index (Supplementary Vector Layer). To create seamless national layers from individually processed path/rows required assembly of components. The path/row index identifies each Landsat scene pair footprint and includes a Landsat acquisition date attribute and scene identification number attribute for each scene pair used during the NLCD 2006 change analysis and land cover modeling process. The mosaic was made using a model to code each footprint with the appropriate path/row value using a <path>0<row> scheme. For example, all pixels in the footprint for path 29/row 30 would be value 29030 in the path/row index vector file. Landsat data and ancillary data used for the land cover prediction - For a list of Landsat scenes and scene dates by path/row used in this project, please see: appendix1_nlcd2006_scene_list_by_path_row.txt Data Type of DEM composed of 1 band of Continuous Variable Type. Data Type of Slope composed of 1 band of Continuous Variable Type. Data Type of Aspect composed of 1 band of Categorical Variable Type. Data type of Position Index composed of 1 band of Continuous Variable Type. Data type of 3-ratio index composed of 3 bands of Continuous Variable Type.