Classification of Forest Dominate Types Using an Integrated Landsat and Ecological Model

Transcription

1 Classification of Forest Dominate Types Using an Integrated Landsat and Ecological Model Southern Region Existing Vegetation Mapping Pilot Project Test Supported By Region 8 Engineering/GeoSpatial 3 Year Effort

2 Southern Region Existing Vegetation Mapping Pilot Project Test Background of the Pilot Test Purpose and Goals of the Pilot Test Vegetation & Mapping Guide Method Results Discussion

3 Principal Cooperators Nantahala Forest Steven Simon, USDA-FS Ecologist Botanist Henry McNab, USDA-FS Research Forester Regional Office Renee Jacokes, USDA-FS Remote Sensing Specialist Washington Office Ron Brohman, USDA-FS WO Contact RSAC Ken Brewer, IAAA Program Leader John Gillham, Image Analyst Jess Clark, Image Analyst

4 Background Existing Vegetation Classification and Mapping Technical Guide The Northern Region Vegetation Mapping Project, R1-VMP Northern Idaho & NW Montana (Region 1) Vegetation Classification & Mapping Landsat Imagery & ecognition Software Completed in April 2004 Is the method applied by the R1-VMP and the Existing Vegetation Classification and Mapping Technical Guide applicable to the Southern Region?

5 Existing Vegetation Mid-level Mapping Standards Southern Region Pilot Test Project Objectives Provide an objective evaluation of the Draft Existing Vegetation Mid-level Mapping protocol and standards. Develop and demonstrate a process for mid-level existing vegetation mapping suitable application in the hardwood dominated forests of the Southern Region. Provide a mid-level existing vegetation map for the Nantahala National Forest to support the Forests, inventory, monitoring and Forest planning activities that will facilitate maintenance through change detection.

6 Investigator Concerns 1. Capability to map floristic and diameter classes in deciduous forests 2. Suitability of TM imagery to meet standards and field requirements 3. Can the Vegetation Guide be applied to the Southern Region

7 Existing Vegetation Classification and Mapping Technical Guide to the Mid-level Standards NVC Physiognomic Sub-class Total Vegetative Cover Total Tree Canopy Cover Overstory Tree Diameter Floristic Map Classes Minimum Map Feature Map Accuracy Goals 80% to 90% Map Accuracy Standard 65%

8 Existing Vegetation Mapping Physiognomic Classification Division Order Class Subclass Nonvegetated Vegetated No Dominant Lifeform Tree Dominated Shrub Dominated Herb-Nonvascular Dominated Sparse Vegetation Closed Tree Canopy Open Tree Canopy Shrubland Dwarf Shrubland Herbaceous Vegetation Nonvascular Vegetation Consolidated Rock sparse Vegetation Bolder Gravel Cobble sparse Vegetation Unconsolidated Sparse Vegetation Evergreen Closed Tree Canopy Deciduous Closed Tree Canopy Mixed Evergreen-Deciduous Closed Tree Canopy Evergreen Open Tree Canopy Deciduous Open Tree Canopy Mixed Evergreen-Deciduous Open Tree Canopy Evergreen Shrubland Deciduous Shrubland Mixed Evergreen/Deciduous Shrubland Evergreen Dwarf Shrubland Deciduous Dwarf Shrubland Mixed Dwarf Evergreen/Deciduous Shrubland Hydromorphic Rooted Vegetation Perennial Graminoid Vegetation Perrenial forb Vegetation Annaual Graminoid or Forb Vegetation Bryophyte Vegetation Lichen Vegetation Alga Vegetation

9 Existing Vegetation Map Feature Attributes NVC Physiognomic Classification to Class (Sub-class for Woody Vegetation) Total Vegetative Cover Tree Canopy Closure Floristic Description Overstory Tree Diameter Class Floristic Classification Category SAF/SRM Cover Type Dominance Type (Locally Defined) Alliance Association Mid-level R O O O Mid-level Map Units Overstory Tree Diameter Map Units 0 to 4.9 inches DBH 5 to 9.9 inches DBH 10 to 19.9 inches DBH 20 to 29.9 inches DBH 30+ inches DBH Total Vegetation Cover Map Units Total Tree Canopy Closure Map Units 0 to 9.9% 0 to 9.9% 10 to 29.9% 10 to 29.9% 30 to 59.9% 30 to 59.9% 60 to 79.9% 60 to 79.9% 80 to 100% 80 to 100%

10 What is Mid-level? Mapping Level/Units Smaller Resolution Larger Area Mapped National-Level (National) National Strategic Inventory Broad-Level (Regional) Bioregional Planning Mid-Level (Multi-Forest) Ecosystem Assessment by Watershed Base-Level (Forest) Project Planning, Monitoring and Evaluation Higher Resolution and Smaller Area Mapped

11 Nantahala National Forest Western North Carolina 35.5 N 83.5 W Elevation 1,000 to 6,600 Feet Central Appalachian Broadleaf-Coniferous Forest Meadow Province Blue Ridge Mountain Section Southern Blue Ridge Mountain Subsection Metasedimentary Mountains Subsection

12 Study Area: 1,672,675 Acres

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14 Vegetation Classification & Mapping Processes Terrain Derived Vegetation Distribution Model (Ecological Zones) ecognition Primary Data Sets Landsat TM ETM+ data Ecological Zones Based on Terrain Models Image Processing Lecia Imagine Analysis ecognition Software for Existing Vegetation Mid-level Mapping ArcGRID

15 Transition ETM+ (Landsat 7) April 2000 Leaf-off ETM+ (Landsat 7) October (Path 18) 2001/ December (Path 19) 2001 Leaf-on TM5 (Landsat 5) June (Path 18) 2003 / September (Path 19) 2002 Scene Path 19/Row 35 Scene Path 19/Row 36 Scene Path 18/Row 36

16 Ecological Zones of the Southern Appalachians Ecological Zone Units of land delineating the environment that support a specific plant community Initial modeling for the 5.6 million acre Southern Appalachians was based on the SAVD data set collected between 1976 and 1999 by 20 investigators Plots were located in undisturbed stands > 50 years Terrain variables for the initial models were based on 30 meter DEM s The investigators developing the initial model were: Steve Simon USDA-FS Ecologist Botanist Thomas Collins USDA-FS Geologist Gary Kaufman USDA-FS Botanist Henry McNab USDA-FS Research Forester Chris Ulrey USDI-NPS Vegetation Specialist Southern Appalachian Ecological Zones Forest Vegetation Distribution Model

17 Second Approximation Ecological Zones Terrain Models are reprocessed with 15 meter DEM s Area in and around the Nantahala National Forest The Primary Investigators is Steve Simon USDA-FS Ecologist Botanist Ecological Zone Units of land delineating the environment that support a specific plant community Ecological Zones Forest Vegetation Distribution Model

18 Ecological Zones Dominance Type There are 17 Ecological Zones in the Nantahala Study Area 32 Floristic Classes, referred to a Forest Dominance Types Were classified based on the integration of 15 Ecological Zones Mesic Oak-Hickory Rich Cove Acidic Cove Pine-Oak Heath High Elevation Red Oak Shortleaf Pine-Oak Northern Hardwood Cove Northern Hardwood Slope Oak/Rhododendron Dry-Mesic Oak Hickory Alluvial Forest Chestnut Oak/Mt. Laurel Spruce-Fir Shortleaf Pine-Oak Heath Dry Oak

19 ecognition Software - Image Segmentation

20 ecognition Software - Hierarchical Structure

21 Reference Data - Preprocessing Precipitation (annual average, January, April, July, October) Temperature (annual average, January, April, July, October) Elevation (meters from a 10m DEM)) Slope (in percent slope) Aspect Trishade (hybrid hillshade/aspect layer 3 bands) Landsat 7 ETM+ (bands 1-6 and panchromatic/ms merge for segmentation) 3 temporal dates (east half = , , ; west half = , , ) NDVI Normalized Difference Vegetation Index -- for all ETM + dates. PCA Principle Components Analysis (1st 3 bands) -- for all ETM+ dates Tassel Cap (TCAP) brightness, greenness, and wetness bands -- for all ETM+ dates Texture spectral variability from DOQs (3x3 low-pass filter done at 3m) Tone spectral tone from DOQs TNT Texture/Tone ratio Ecological Zones

22 ecognition Software - Dominance Type There are 11 Forest Dominance Types Tulip poplar Oak Spruce-fir Hemlock White pine Hemlock-white pine Yellow pine Mixed evergreen-deciduous tree Deciduous tree/evergreen shrub Mixed evergreen-deciduous tree/evergreen shrub Mixed deciduous tree.

23 Integration of Ecological & ecognition Models The class are compare using ArcGrid Classes defined by ecognition were evaluated within the context of Ecological Zones i.e., we summarized the different ecognition classes (Pine, Oak, White Pine, etc.) that occurred within an Ecological Zone (Shortleaf Pine, Mesic Oak-Hickory) and not the reverse. ecological matches, ecological mismatches, and ecognition defined types. existing vegetation types defined by ecognition that always took precedence over Ecological Zones included water, agricultural fields, native-grass-shrub, and rock outcrops ArcGRID

24 Accuracy Assessment A total of 330 accuracy assessment polygons on land administered by the U.S. Forest Service An equal number of accuracy samples, i.e. 10 samples, were randomly distributed across the 32 taxa Least 2% of the study area acres were assessed Random selection method was used Good distribution of samples in all polygon sizes that were well-distributed throughout the study area.

25 Method Canopy Cover Used ecognation to process the Canopy Cover based on the Vegatation Guide for Mid-Scale: 0-9.9% %, % % Canopy Cover for the Field Study was analyzed based on > 66%, 33-66%, and <33%. The overall canopy cover accuracy was 75.3%.

26 Results The overall accuracy of the mapping at the physiognomic subclass level was 92%. The overall accuracy at the forest dominance type level, an integration of image processing and ecological models, was 42%. The overall accuracy for tree canopy closure was 75%. The overall accuracy of Ecological Systems, a mid-level map product useful for Forest Planning, was 70%.

27 Discussion The Southern Region will incorporate the classifications and guidelines provided by the Technical Guide. The combination of ecognition and ecological vegetation modeling provided more accurate classification of existing vegetation than traditional automated processes. ecognition classification of rare vegetation types was poor while the environmental modeling of rare types was good. ecognition did well in mapping common types such as White Pine (about 60% accurate). Ecological vegetation modeling provides more accurate classification of larger areas of vegetation and non-vegetation types. As the Forest Service experiences decreasing budget and increasing need to map vegetation, ecological and ecognition models will provide an important cost saving benefit for the Agency.

28 Questions