Land Cover Classification and Monitoring Case Studies: Twin Cities Metropolitan Area –Multi-temporal Landsat Image Classification and Change Analysis –Impervious Surface Classification and Mapping Marvin Bauer University of Minnesota

Multitemporal Landsat Image Classification and Change Analysis of Land Cover and Impervious Surface Area in the Twin Cities (Minnesota) Metropolitan Area Marvin Bauer University of Minnesota

Introduction There is growing concern by citizens and public agencies about the growth in the size of cities and accompanying –loss of agricultural lands, forests, and wetlands –escalating infrastructure costs for utilities, schools, etc. –increases in traffic congestion –degraded environment

Historically, aerial photography has been an important source of land cover and land use information, but costs are prohibitive for large geographic areas Statistical inventories are relatively economical, but do not include information on location of changes An alternative is satellite image classification with Landsat TM / ETM+ or SPOT data –Synoptic view, large area coverage –Digital format, efficient analysis and GIS-compatible –Economical Introduction, cont.

Study Area Twin Cities Metropolitan Area

Objectives Use multi-temporal Landsat imagery to classify land cover in the Twin Cities metropolitan area over the past years Quantify the change from rural land uses (agriculture, forest, wetland) to urban and developed uses

Multi-year Landsat Imagery Woodbury area, east of St. Paul

May Multitemporal Landsat Imagery for Land Cover Classification

September

Classification Scheme (Level I) Land Cover ClassDescription AgricultureCrop fields, pasture, and bare fields Cultivated GrassGolf courses, lawns, and sod fields ExtractionQuarries, sand and gravel pits Forest Deciduous forest land, evergreen forest land, mixed forest land, orchards, groves, vineyards, and nurseries Urban Residential, commercial services, industrial, transportation, communications, industrial and commercial, mixed urban or build-up land, other urban or built-up land Water Permanent open water, lakes, reservoirs, streams, bays and estuaries WetlandNon-forested wetland

Agriculture Urban Water Forest Grass Wetland Extraction 1986 Overall accuracy: 95.5% Kappa: 94.4%

Agriculture Urban Water Forest Grass Wetland Extraction 1991 Overall accuracy: 94.6% Kappa: 93.2%

Agriculture Urban Water Forest Grass Wetland Extraction 1998 Overall accuracy: 92.6% Kappa: 90.9%

Agriculture Urban Water Forest Grass Wetland Extraction 2002 Overall accuracy: 93.2% Kappa: 91.6%

Classification Accuracy (%) Land Cover Class Prod.UserProd.UserProd.UserProd.User Agriculture Forest Grass Urban Water Wetland Overall Kappa

Urban (unchanged) Urban growth 1986 – 1991 Urban growth 1991 – 1998 Urban growth 1998 – 2002 Rural Water 2000 MUSA boundary Highway Change Map

Land Cover Changes from 1986 to 2002 Urban increased 38.5% Agriculture, Forest and Wetland decreased 35.3% Land Cover Class Relative Change, 1986 – 2002 (%) Area (000 ha)% Area (000 ha)% Area (000 ha)% Area (000 ha)% Agriculture Urban Forest Wetland Water Grass Extraction

Matrix of Changes (“from – to” information) in Land Cover, 1986 to 2002 (000 ha) Net rural area converted to urban = 70,000 ha Total AgricultureUrbanForestWetlandWaterGrassExtraction Agriculture Urban Forest Wetland Water Grass Extraction Total

Discussion Multi-temporal data increases classification accuracy Highly accurate classifications are necessary for change detection Even with accurate classifications, there are anomalies (errors) associated with registration and classification errors Nevertheless, maps and statistics with sufficient accuracy for many users can be derived from Landsat TM data classifications

Discussion, cont. Change maps provide geographic location and “from-to” information Accurate regional level statistics and maps can be generated in months, as opposed to years for other surveys

Land Cover Classification and Change Detection: Summary and Conclusions Need and demand for accurate and timely landscape change data is increasing Multi-date Landsat classifications have the potential to cost effectively produce change maps and statistics over county to regional size areas

Minneapolis St. Paul Impervious Surface Mapping and Change Monitoring using Landsat Remote Sensing An example of using digital multispectral data to map a continuous variable

What are Impervious Surfaces? Any surface not penetrable by water Includes streets, parking lots, side- walks and building roof tops Transportation elements contribute the most to impervious surface area Image: © Regents of the University of Minnesota. Used with the permission of Metropolitan Design Center.

Why is it important to map extent and spatial distribution of impervious surfaces? Water runoff –Flooding –Erosion –Storm water storage Water quality Urban heat island effects Ascetics Impervious impacts on water quality and stream degradation Degraded Impacted Protected Stream Degradation Watershed % Imperviousness

Approaches to mapping impervious surface area (1) Interpret land use from aerial photography and assign average % impervious of the class to each polygon, or (2) interpret and measure % impervious, and digitize boundaries Alternative: Mapping and estimation of % impervious surface area using digital satellite imagery –More economical, readily compatible with GIS and potentially more accurate

Objectives Estimate and map impervious surface area for the seven-county TCMA (186 cities and townships) for 1986 – 2002 in support of hydrological modeling and monitoring by the Minnesota Pollution Control Agency Followed by classification and mapping of selected cities and areas, statewide

Basic Theory for Satellite Mapping of ISA Greenness is sensitive to amount of green vegetation and inversely related to amount of impervious surface % Impervious LowHigh Landsat Greenness Low High 100% Impervious 50% Impervious 50% Vegetation 100% Vegetation

DOQ with model calibration sites Landsat image Impervious (%) Greenness Y = G (G ) 2 Greenness – Impervious Model

Impervious surface map “Greenness” image Impervious (%) Greenness Y ISA = a - b 1 G + b 2 G 2 Greenness – Impervious Model

Comparison of aerial photo and Landsat classification 0 % Impervious 100

Y = G (G ) 2 Relationship of Landsat Greenness and Percent Impervious Surface Area Path 28/row 28 August 10, 2000 R 2 = 0.91 Std. Error = 10.71

Comparison of Measured and Landsat Estimates of Impervious Surface Area Y = x Path 28/row 28 August 10, 2000 R 2 = 0.91 Std. Error = 9.39

7-County TCMA, 2002 Impervious Area 247,081 acres % 1000 Percent Impervious

Change in percent impervious surface area, 1986 – 2002, by County

Woodbury ,949 acres, 8.5 % ,573 acres, 11.2 % ,169 acres, 18.2 % ,438 acres, 19.4% 0 Percent Impervious Percent Impervious ,949 acres, 8.5 % ,573 acres, 11.2 % ,169 acres, 18.2 % ,438 acres, 19.4%

Change in Percent Impervious Surface Area, 1986 – 2002, for Selected Areas

Impervious: Summary and Conclusions A strong relationship between impervious area and Landsat greenness enables mapping percent impervious surface area at the pixel level. Landsat classification provides GIS-ready, accurate and consistent maps and estimates at 30-meter resolution over city to county to regional size areas.

100% Impervious 0% Impervious Urban/Developed Forest Water Grass Wetland Shrubland Agriculture Classification of Land Cover and Impervious Surface Area

Summary and Conclusions Landsat classifications provide a means to economically obtain land cover and change information and impervious surface maps for inputs to GIS, environmental, hydrological, and urban planning models and decision making. For more information, see: land.umn.edu