C-CAP, Guam, 2011-2016 Land Cover Change Analysis

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NOAA OCM | Source Geospatial Form: remote-sensing image | Type of Source Media: DVD/CD-ROM

Early Date Classification: This data set was created by Photo Science (www.photoscience.com). This version of the classification is the High Resolution Land Cover (2011-era) for the Territory of Guam.

This section outlines the classification procedure for the Guam High Resolution C-CAP. WorldView2 imagery used in producing this land cover product was also utilized in producing an associated impervious surfaces layer for the island. The mapping approach utilized a boundary summary, outlier change detection process and refinement procedure developed by Photo Science to leverage the 2011 high resolution C-CAP data set to create the 2016 high resolution C-CAP. A calibration visit was not conducted though NOAA had access to local resources for validation. Non impervious features were mapped using a 0.25 acre minimum mapping unit (MMU) and impervious features were mapped using a 0.1 acre MMU.

Pre-processing steps:

The WorldView2 mosaic utilized for this project was provided from Digital Globe as an orthorectified, georeferenced product. Multiple image primitives and indicies such as texture and NDVI were derived from the 8-band satellite data. The imagery was re-sampled from it's native 2m spatial resolution to 2.4m. The 2011 C-CAP classification and the base Quickbird imagery (used in the original mapping) were geometrically corrected to co-register to the WorldView2 data which has a higher geolocation accuracy.

Impervious Update:

The 2011 impervious was over-laid with semi-transparency on the 2016 satellite data. It was panned at a scale of 1:3,500 and manually updated to match the 2016 data. Analysts zoomed in to a larger scale when necessary to perform edits. The product went through a QC procedure to ensure features were accurately captured.

Segmentation and Outlier Detection Process:

The 2016 impervious was combined with the 2011 land cover to create a hybrid data set. Image segmentation, done in Trimble's eCognition software, was completed at multiple scales using the multispectral (2.4 m) imagery in order to group like spectral and textural objects within the imagery. For consistency, the associated hybrid data set was incorporated into the segmentation layer as a boundary delimiter. Segments contained image attributes and a label from the 2011 classification. These data were inputs to a custom multi-variate outlier detection tool that identified objects of potential change. These areas created the change mask.

Training data was photo interpreted and collected within the change mask and was super-sampled from features outside of the mask. The referenced data set went through a QA/QC procedure to ensure it met accuracy standards.

Classification:

Automated Classification - Image segments were classified using a decision tree classifier in See5 software based on the training data and image attributes.

Automated Classification Refinement - Models are built to refine or reclassify land cover areas by utilizing the wealth of attribute information linked to each segment within eCognition.

Classification Edits - As with any automated or semi-automated land cover classification there are often inconsistencies in the land cover map. The final step before map finalization was to remove inaccuracies through manual segment labeling as interpreted by an analyst.

Map Finalization - Photo Science used independent reviewer degrees's comments to further refine the land cover map.

Attributes for this product are as follows:

0 Background

1 Unclassified

2 Impervious

3

4

5 Developed, Open Space

6 Cultivated Crops

7 Pasture/Hay

8 Grassland/Herbaceous

9 Deciduous Forest

10 Evergreen Forest

11 Mixed Forest

12 Scrub/Shrub

13 Palustrine Forested Wetland

14 Palustrine Scrub/Shrub Wetland

15 Palustrine Emergent Wetland

16 Estuarine Forested Wetland

17 Estuarine Scrub/Shrub Wetland

18 Estuarine Emergent Wetland

19 Unconsolidated Shore

20 Bare Land

21 Open Water

22 Palustrine Acquatic Bed

This data set was created by Quantum Spatial, Inc. (https://quantumspatial.com/). This version of the classification is the High Resolution Land Cover (2016-era) for the Territory of Guam.

This section outlines the classification procedure for the Guam High Resolution C-CAP. WorldView3 imagery used in producing this land cover product was also utilized in producing an associated impervious surfaces layer for the island. The mapping approach utilized a boundary summary, automated image segmentation, and manual extraction of change and land cover using the 2011 high resolution C-CAP data set, the 2011 WorldView2 imagery, the 2015-2016 WorldView3 imagery, and ancillary data sources to create the 2016 high resolution C-CAP. A calibration visit was not conducted though NOAA had access to local resources for validation. Non impervious features were mapped using a 0.25 acre minimum mapping unit (MMU) and impervious features were mapped using a 0.1 acre MMU.

Pre-processing steps:

The WorldView3 mosaic utilized for this project was provided NOAA as an orthorectified, georeferenced product. The imagery was re-sampled from its native 1.2m spatial resolution to 2.4m to match the spatial resolution of the 2011 C-CAP Land Cover product.

Impervious Update:

The 2011 impervious was over-laid with semi-transparency on the 2015-2016 WorldView3 satellite data. It was panned at a scale of 1:4,500 and manually updated to match the 2015-2016 data. Analysts zoomed in to a larger scale when necessary to perform edits. The product went through a QC procedure to ensure features were accurately captured.

Image Segmentation:

The 2016 impervious was combined with the 2011 land cover to create a hybrid data set. Image segmentation, done in Trimble's eCognition software, was performed at multiple scales using the WorldView3 imagery and existing land cover with updated impervious in order to group like spectral and textural objects within the imagery. The image segments were exported and then brought into ArcGIS. A manual interpretation process was used to collect segments as areas of change. These areas created the change mask.

Classification:

Classification - As segments were collected for change, a land cover class was also assigned to the segment to indicate its new 2016 land cover classification.

Map Finalization - Quantum Spatial used independent reviewer’s comments and NOAA's review comments to further refine the land cover map. Various models and processing tools were also used during this refinement process, such as creating and reviewing a 2011-2016 change matrix and identifying illogical change pixels based on to and from land cover combinations.

Attributes for this product are as follows:

0 Background

1 Unclassified

2 Impervious

3

4

5 Developed, Open Space

6 Cultivated Crops

7 Pasture/Hay

8 Grassland/Herbaceous

9 Deciduous Forest

10 Evergreen Forest

11 Mixed Forest

12 Scrub/Shrub

13 Palustrine Forested Wetland

14 Palustrine Scrub/Shrub Wetland

15 Palustrine Emergent Wetland

16 Estuarine Forested Wetland

17 Estuarine Scrub/Shrub Wetland

18 Estuarine Emergent Wetland

19 Unconsolidated Shore

20 Bare Land

21 Open Water

22 Palustrine Aquatic Bed

23 Estuarine Aquatic Bed

24 Tundra

25 Snow/Ice