Title:

High resolution land cover for San Jose, CA (2011)

Author(s):

University of Vermont Spatial Analysis Laboratory

Publication Year:

2012

How to Cite:

If you use these data in a publication, presentation, or other research product please use the following citation:

University of Vermont Spatial Analysis Laboratory. 2012. High resolution land cover for San Jose, CA (2011). Burlington, VT: University of Vermont Spatial Analysis Laboratory. https://gis.w3.uvm.edu/utc/Landcover/SanJose_2011.zip

Abstract:

High-resolution land-cover dataset for San Jose, California. Nine land-cover classes were mapped: (1) tree canopy, (2) shrubs, (3) irrigated grass, (4) non-irrigated grass, (5) bare earth, (6) water, (7) buildings, (8) roads, and(9) other paved surfaces. The primary sources used to derive this land-cover layer were 2006 LiDAR, 2011 True Color Orthophotography, and 2010 imagery from the National Agricultural Imagery Program (NAIP). The 2006 LiDAR was used primarily to map tree canopy, but the age of this dataset necessitated extensive manual review of the draft tree canopy to remove trees lost or added since LiDAR acquistion. The reference imagery for manual review was the 2011 True Color Orthophotography. The other land-cover classes were based on a combination of the available multispectral imagery (2011 True Color Orthophotography and 2010 NAIP) and various ancillary data sources. The ancillary data sources included GIS datasets provided by the University of California-Davis (water, roads) and layers developed by the University of Vermont Spatial Analyis Laboratory (shrubs, bare soil) using manual interpretation of the 2011 True Color Orthophotography. This land-cover dataset is thus considered current as of 2011. Object-based image analysis techniques (OBIA) were used to develop a draft land-cover map from the available remote-sensing data and vector GIS layers. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product was both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to a thorough manual quality control in which thousands of corrections were incorporated into the final map.

Keywords:

environment; imageryBaseMapsEarthCover; planningCadastre; Environment and People; Urban natural resources management; Natural Resource Management & Use; urban tree canopy; UTC; land cover; California; San Jose

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