Land Use/ Land Cover Mapping Initiativefor Kansas and the Kansas River

Watershed

Kansas Biological SurveyKansas Applied Remote Sensing Program

April 2008

Previous Kansas LULC Projects

10 Level I classes1989-1991 Landsat TM dataSingle-date (summer)Three-year project

43 classes; 40 are natural vegetation

1992-1995 Landsat TM dataMulti-seasonal (spring, summer,

fall)~5-year project

Kansas LULC Map (1990)

Kansas GAP Map (1996)

Summary of LULC products for EPSCoR

– Phase 1:

• Modified Level I land use/land cover product derived from 30-meter Landsat Thematic Mapper data

– Phase 2:

• Change detection products created by analysis of the new LULC maps with past LULC maps;

• Cool-season/warm season grasslands mapping using multitemporal 250-meter MODIS data;

• Crop type identification and crop rotation products, also created from multitemporal MODIS data.

Level 1 Land Use/Land Cover Map Description

Phase 1: Modified Level I LULC mapping– Multitemporal Landsat TM (30-meter resolution)

• Seasonal data: Spring, summer, & fall

– Maximum separability of classes

• Image dates: 2004-05

– Classes Mapped• Forest, water, cropland, grassland, rural developed, and

urban commercial/industrial, urban residential, urban open, and urban woodland.

– Comparable to 1990 LULC map (classes & MMU)• Allows change detection

Data Acquisition

Generalize to MMU

Unsupervised Classification:

• Assign and recode spectral classes to LULC classes

• Cluster Busting

Image Processing:

• Layer Stack• Clip Spatial Extents• Mask Clouds

Mosaic Processing Units

Map Refinements/Editing

Accuracy Assessment

• 2004/2005 Terrain-corrected TM imagery

• 3-date: Spring, Summer, & Fall

ISODATA & Maximum Likelihood Classifiers

Using existing KS GAP and USDA databases and aerial imagery interpretation.

Map & Deliverables

Land Cover Classification Methodology

Map Refinement: Addition of Conservation Reserve lands

• From a mapping perspective: CRP land was accurately mapped as grasslands

• From an ecological perspective: A large number of grasslands are at risk of being converted back to cropland

Grass/Crop Image CLU data (CRP land in red) CLU data added

Generalization using CLU data

Pre-generalized Traditional Generalization Techniques

Generalized Using Common Land Unit

Land Cover Change: Nemaha County

1990 Land Cover Map 2005 Land Cover Map

Rural Land Cover Change: Nemaha County

1990 Land Cover Map 2005 Land Cover Map

Urban Land Cover Change: Johnson County

1990 Land Cover Map 2005 Land Cover Map

Phase 1 Land Use/Land Cover Map

Accuracy Assessment: In Progress

• Accuracy Levels reported: Overall, User’s, Producer’s, and the KAPPA statistic

• Stratified random sampling design– Sample size proportionate to the area mapped for each

land cover class

• Using existing databases as reference or ground-truth to assess accuracy levels for cropland, grassland, and woodland.– Kansas GAP database– USDA database

• Using aerial imagery (NAIP) interpretation techniques to assess accuracy levels for water and urban.

Getting the land cover data …

http://www.kars.ku.edu/

Getting the land cover data …

http://www.kars.ku.edu/projects/ecoforecasting/

Data contents

• Arc GRID files of watershed land cover (actual and buffered boundaries)

• Watershed boundary files: actual and buffered– Raster and vector formats

• County boundaries for watershed area• Ancillary data

– ArcMap .mxd file (Arc 9.2 document)– Arc layer (.lyr) file– Name files for land cover classes (.doc & .xls)

Level II Land Cover Mapping

Phase 2: Map Grasslands and Land Management

– LULC Mapping:• Crop type• Crop rotation practices• Warm season/cool season grasslands mapping• Irrigated vs. non-irrigated croplands*

– Data:• Time-series 250m MODIS imagery• 6 years MODIS data in KBS-KARS archive

– 2005 data as the target year– 16-day composites, 23/year

Comparison of MODIS and Thematic Mapper imagery

Landsat TM 30m resolution MODIS 250m resolution

Dense Time-Series Maximum Value Composites

• More is better: multi-date maximum-value image composites.– Select the “best” pixel, i.e., the pixel with the

highest green vegetation response, over a given time period (7, 10, 14, 16 days) – this tends to eliminate pixels contaminated by clouds, noise, etc.

– Create a new image consisting of the maximum values for the given time period.

– Thus, if bi-weekly (14-day) composites are used, you get 26 composite images per year → “dense time series” or “hyper-temporal” imagery.

• How does this look over the course of a year?

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Alfalfa Fallow Summer Crops Winter Wheat

General Crop TypesAverage multi-temporal NDVI profiles for Kansas in 2001

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NDVI

Corn Sorghum Soybeans

Summer Crop TypesAverage multi-temporal NDVI profiles for Kansas in 2001

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NDVI

Corn (Irrigated) Corn (Non-Irrigated) Winter Wheat (Irrigated) Winter Wheat (Non-Irrigated)

Irrigated and Non-Irrigated CropsAverage multi-temporal NDVI profiles for Kansas in 2001

Double CroppingAverage Multi-Temporal NDVI Profiles for Southeast Kansas

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NDVI

AlfalfaDouble Crop

Winter Wheat Summer Crop

Flint HillsWarm Season Grass

Cool SeasonGrass Area

Mapping Cool and Warm Season Grasslands

Acknowledgments

• Project Personnel– Ed Martinko– Josh Campbell– Kevin Dobbs– Steve Egbert– Mark Jakubauskas– Jude Kastens– John Lomas– Iwake Masialeti– Dana Peterson– Jerry Whistler

• Kansas GIS Policy Board– Funding for Land Cover

Mapping Outside the Kansas River Basin

– 2005 Landsat imagery database

• USGS AmericaView / KansasView Program– Imagery discount and

research support