Land Use/ Land Cover Mapping Initiative for Kansas and the Kansas River Watershed

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1 Land Use/ Land Cover Mapping Initiative for Kansas and the Kansas River Watershed Kansas Biological Survey Kansas Applied Remote Sensing Program April 2008

2 Previous Kansas LULC Projects Kansas LULC Map (1990) 10 Level I classes Landsat TM data Single-date (summer) Three-year project Kansas GAP Map (1996) 43 classes; 40 are natural vegetation Landsat TM data Multi-seasonal (spring, summer, fall) ~5-year project

3 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.

4 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: 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

5 Land Cover Classification Methodology Data Acquisition 2004/2005 Terrain-corrected TM imagery 3-date: Spring, Summer, & Fall Image Processing: Layer Stack Clip Spatial Extents Mask Clouds Unsupervised Classification: ISODATA & Maximum Likelihood Classifiers Assign and recode spectral classes to LULC classes Cluster Busting Generalize to MMU Mosaic Processing Units Map Refinements/Editing Using existing KS GAP and USDA databases and aerial imagery interpretation. Accuracy Assessment Map & Deliverables

6 Map Refinement: Addition of Conservation Reserve lands Grass/Crop Image CLU data (CRP land in red) CLU data added 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

7 Generalization using CLU data Pre-generalized Traditional Generalization Techniques Generalized Using Common Land Unit

8 Land Cover Change: Nemaha County 1990 Land Cover Map 2005 Land Cover Map

9 Rural Land Cover Change: Nemaha County 1990 Land Cover Map 2005 Land Cover Map

10 Urban Land Cover Change: Johnson County 1990 Land Cover Map 2005 Land Cover Map

11 Phase 1 Land Use/Land Cover Map

12 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.

13 Getting the land cover data

14 Getting the land cover data

15 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)

16 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

17 Comparison of MODIS and Thematic Mapper imagery Landsat TM 30m resolution MODIS 250m resolution

18 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?

19 NDVI Jan. 1 Jan. 17 Feb. 2 Feb. 18 March 6 March 22 April 7 April 23 May 9 May 25 June 10 June 26 July 12 July 28 August 13 August 29 Sept. 14 Sept. 30 Oct. 16 Nov. 1 Nov. 17 Dec. 3 Dec. 19 General Crop Types Average multi-temporal NDVI profiles for Kansas in 2001 Alfalfa Fallow Summer Crops Winter Wheat

20 NDVI Jan. 1 Jan. 17 Feb. 2 Feb. 18 March 6 March 22 April 7 April 23 May 9 May 25 June 10 June 26 July 12 July 28 August 13 August 29 Sept. 14 Sept. 30 Oct. 16 Nov. 1 Nov. 17 Dec. 3 Dec. 19 Summer Crop Types Average multi-temporal NDVI profiles for Kansas in 2001 Corn Sorghum Soybeans

21 Irrigated and Non-Irrigated Crops Average multi-temporal NDVI profiles for Kansas in NDVI Jan. 1 Jan. 17 Feb. 2 Feb. 18 March 6 March 22 April 7 April 23 May 9 May 25 June 10 June 26 July 12 July 28 August 13 August 29 Sept. 14 Sept. 30 Oct. 16 Nov. 1 Nov. 17 Dec. 3 Dec. 19 Corn (Irrigated) Corn (Non-Irrigated) Winter Wheat (Irrigated) Winter Wheat (Non-Irrigated)

22 Double Cropping Average Multi-Temporal NDVI Profiles for Southeast Kansas NDVI Jan. 1 Jan. 17 Feb. 2 Feb. 18 March 6 March 22 April 7 April 23 May 9 May 25 June 10 June 26 July 12 July 28 August 13 August 29 Sept. 14 Sept. 30 Oct. 16 Nov. 1 Nov. 17 Dec. 3 Dec. 19 Alfalfa Double Crop Winter Wheat Summer Crop

23 Mapping Cool and Warm Season Grasslands Flint Hills Warm Season Grass Cool Season Grass Area

24 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

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