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Cached_Layers/Rast_impervious_surface_3857 (MapServer)

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Service Description: Impervious surfaces for the State of New Jersey. Three classes of impervious surfaces were mapped: 1) Buildings, Roads, and Other Impervious Surfaces. Note that buildings do not exist for some areas due to the lack of LiDAR data. In these instances, buildings are assigned to the Other Impervious class. The overall accuracy of the mapping was 94%. The user's accuracies for Buildings, Roads, and Other Paved were 94%, 87%, and 84% respectively.The 2015 leaf-off New Jersey orthorectified imagery served as the primary data source, but the 1-meter leaf-on imagery acquired through the National Agricultural Imagery Program (NAIP) in the summer of 2015 was also used. Several additional datasets, that although dated and flawed, that still had value were used to supplement the process. This includes LiDAR data, which was collected at varying time periods in New Jersey, road centerlines, and the National Hydrography Dataset (NHD). Initial feature extraction was carried out in the coordinate system of the LiDAR data (most often UTM). LiDAR point clouds were normalized and classified to separate out buildings and trees. Raster surface models and intensity images were generated from the LiDAR point cloud data. Imagery data were mosaicked, and vector datasets were examined for completeness, accuracy, and topology.This project employed a semi-automated approach in which features were automatically extracted then manually reviewed and edited. The automated feature extraction workflow used an OBIA framework to automatically extract aforementioned impervious classes from the source datasets using a rule-based expert system. The expert system made use of segmentation, classification, and morphology algorithms to group pixels into objects, classify the objects based on the properties of the source datasets, and then refined the objects to enhance aesthetics and improve visual realism. The approach to mapping involved both automated feature extraction techniques and manual editing of impervious surfaces. A “bottom-up” approach to mapping was carried out in which impervious surface features that are under tree canopy but visible in the leaf-off imagery was mapped. The key advantage of the object-based approach is that the use of the spectral, textural, geometric, and contextual properties in the imagery reduce confusion between impervious land cover features and those that are similar in tone, such sand on the banks of streams.

Map Name: Rast_impervious_surface_3857

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Layers: Description: Impervious surfaces for the State of New Jersey. Three classes of impervious surfaces were mapped: 1) Buildings, Roads, and Other Impervious Surfaces. Note that buildings do not exist for some areas due to the lack of LiDAR data. In these instances, buildings are assigned to the Other Impervious class. The overall accuracy of the mapping was 94%. The user's accuracies for Buildings, Roads, and Other Paved were 94%, 87%, and 84% respectively.The 2015 leaf-off New Jersey orthorectified imagery served as the primary data source, but the 1-meter leaf-on imagery acquired through the National Agricultural Imagery Program (NAIP) in the summer of 2015 was also used. Several additional datasets, that although dated and flawed, that still had value were used to supplement the process. This includes LiDAR data, which was collected at varying time periods in New Jersey, road centerlines, and the National Hydrography Dataset (NHD). Initial feature extraction was carried out in the coordinate system of the LiDAR data (most often UTM). LiDAR point clouds were normalized and classified to separate out buildings and trees. Raster surface models and intensity images were generated from the LiDAR point cloud data. Imagery data were mosaicked, and vector datasets were examined for completeness, accuracy, and topology.This project employed a semi-automated approach in which features were automatically extracted then manually reviewed and edited. The automated feature extraction workflow used an OBIA framework to automatically extract aforementioned impervious classes from the source datasets using a rule-based expert system. The expert system made use of segmentation, classification, and morphology algorithms to group pixels into objects, classify the objects based on the properties of the source datasets, and then refined the objects to enhance aesthetics and improve visual realism. The approach to mapping involved both automated feature extraction techniques and manual editing of impervious surfaces. A “bottom-up” approach to mapping was carried out in which impervious surface features that are under tree canopy but visible in the leaf-off imagery was mapped. The key advantage of the object-based approach is that the use of the spectral, textural, geometric, and contextual properties in the imagery reduce confusion between impervious land cover features and those that are similar in tone, such sand on the banks of streams.

Copyright Text: NJDEP

Spatial Reference: 102100  (3857)


Single Fused Map Cache: true

Tile Info: Storage Info: Initial Extent: Full Extent: Units: esriMeters

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

Document Info: Supports Dynamic Layers: true

MaxRecordCount: 2000

MaxImageHeight: 4096

MaxImageWidth: 4096

Supported Query Formats: JSON, geoJSON, PBF

Supports Query Data Elements: true

Min Scale: 2311162.217155

Max Scale: 72223.819286

Supports Datum Transformation: true



Child Resources:   Info   Dynamic Layer

Supported Operations:   Export Map   Identify   QueryLegends   QueryDomains   Find   Return Updates