RMRS Forest Inventory and Analysis User Group Meeting 2010 April 13

Why is tree canopy cover important? Percent tree canopy cover is an integral part of both international forest land definitions and U.S. definitions of forest. Percent tree canopy cover is an important variable both within forest land areas and in areas not traditionally considered forest. Knowing where trees are is an important first step in quantifying carbon and managing tree resources.

What is NLCD? National Land Cover Database (NLCD) NLCD 1992: Originated from Multi-Resolution Land Characteristics (MRLC) consortium, a multi-agency program formed to acquire Landsat data across the conterminous U.S. and generate 30m-pixel land cover map. NLCD 2001: 2 nd generation land cover map. In addition, 30m- pixel maps of imperviousness and % tree canopy. NLCD 2011: 3 rd generation land cover, imperviousness, and %tree canopy maps.

NLCD 2001 Percent Tree Canopy Cover From FY07 through FY09 the canopy cover layer was downloaded from MRLC 400 times per month on average (not including ftp downloads). Input for Landfire modeling. Potential source for updating the 2000 assessment of urban tree cover as part of the Resource Planning Act Assessment. Definition of Tree Canopy: A layer or multiple layers of branches and foliage at the top or crown of a forest’s tree.

Example of the NLCD 2001 Percent Tree Canopy Layer

2001 General Modeling Approach = Response developed by an automated classification of tree crown cover on 1-m DOQs (with extensive post-processing hand editing). Model developed using regression tree (random forest) algorithm Fig. from Homer et al. 2007

2001 NLCD Zone Map

Preliminary Accuracy Assessments NLCD 2001 % Tree Canopy Cover Homer et al reported mean absolute error rates of 9.9%, 14.1%, and 8.4% in zones 16, 41, and 60 respectively. Greenfield et al reported under estimation of percent tree canopy cover. An external review of the Landfire canopy cover data suggests that pixel level values are too high (

Rationale for using FIA data FIA is a fundamental component of Forest Service research. FIA is a data rich program: % tree canopy cover estimates will be made on all FIA plots (~340,000 in conterminous US).. including sampling locations that are not consider forest land (e.g. urban, agricultural). Consistency between map based and plot based estimates: Adopting the FIA survey design and using FIA data for developing % tree canopy models will likely provide more consistent estimates of percent canopy cover. The FIA survey design is easily intensified: FIA will use NAIP photography to estimate percent tree canopy on all non-visited plots as well as a portion of visited plots. The photo interpretation techniques can be used on an intensified FIA sample.

Rationale for using photo interpretation National Agriculture Imagery Program (NAIP) 1-m resolution, ortho-rectified, natural color, or near Infrared imagery for the conterminous U.S... and free. Can use directly in models, with no post-processing editing. Can test the robustness of the models using different data intensities. Can test other photo-based responses using tree/no tree classifications, such as: classification trees or segmentation. Can leverage information that will be collected as part of the FIA program.

General Approach: Proof of Concept In general, the proof of concept research will rely on many of the same explanatory variables used in the 2001 product (e.g. leaf-on and leaf-off Landsat imagery, DEM derivatives). The response variable will be developed from manual photo interpretation, modeled NAIP Imagery, and FIA plot modeling (e.g. Toney et al. 2009). The type of model will be determined by examining various quality assessments. There are 5 proof of concept study areas.

2011 General Modelling Approach = Response developed by photo interpreting tree crown cover on NAIP Imagery, modeled NAIP imagery, and FIA plot modeling Random forest algorithm K nearest neighbor imputation Support vector machines... Example modelling techniques Fig. from Homer et al. 2007

Key Research Questions Alternative pixel-level modeling techniques. Alternative stratification/grouping strategies (i.e. alternatives to mapping zones). Using ordinal data for developing model. Model stability under different sampling intensification levels. Impact of scale of response on tree canopy cover estimates (30m vs 90m). Relationship among plot-based, PI-based, and modeled estimates (Toney et al. 2009).

Proof of Concept Study Areas

Study Area Characteristics 1.Approximately one Landsat scene in area 2.Cover multiple scenes 3.Cover multiple gradients I.urban area II.different vegetation types

FIA Grid and Photo Intensification Standard Intensity FIA grid (6000 acres): Modeled % canopy cover estimates Photo interpreted estimates Intensified 4x grid (1500 acres): Photo interpreted % canopy cover 105 photo points per sampling location within a 90m 2 area Modeled canopy cover from field data based on stem mapping and crown diameter models from Coulston et al. 2010

Photo Sample

Repeatability Component Between 2 and 5 interpreters are working in each study area. 5% of the sample locations are interpreted by all interpreters Comparison will be conducted among interpreters, and by different landscapes (e.g. urban, heavily forested, agricultural).

Compare and Contrast: 2001 to 2011 Approaches Response data developed from data with similar grain size (1m) 2001 product developed from fewer (3-4) large (1-4 km 2 ) sample locations proof of concept approach relies on many (~4100) small (0.81 ha) sample locations. Sample locations were purposive for Sample locations drawn randomly Explanatory variables are similar. Modelling approaches will be similar although additional approaches will be examined as part of the 2011 proof of concept. Cost is likely higher using 2011 approach but some of the work is part of the core FIA program.

Status Proof of concept work funded Photo interpretation 90% completed Explanatory data stacks in place Main research components to be completed FY10 Internal Forest Service funding proposal to be submitted March 2010 Prototyping work 2011 (dependent upon additional funding) Production work (dependent upon additional funding)

Team Members 1 Ken Brewer, Quantitative Sciences Staff WO 1,2 Warren Cohen, Pacific Northwest Research Station 1,2 John Coulston, Southern Research Station 1,2 Mark Finco, Remote Sensing Applications Center 1 Everett Hinkley, Remote Sensing National Coordinator 1,2 Gretchen Moisen, Rocky Mountain Research Station 1 Frank Sapio, Forest Health Technology Enterprise Team 1 Brian Schwind, Remote Sensing Applications Center 1,2 Ty Wilson, Northern Research Station 1 Leadership Team 2 Science Team

Questions? 4x Intensity Photo-based Sample Locations 105 photo points to estimate % tree canopy cover for model development E.G. NLCD 2001 % Tree Canopy cover

References Homer, C., Haung, C., Yang, L., Wylie, B., Coan, M Development of a 2001 national land-cover database for the United States. Photogrammetric Engineering and Remote Sensing 70, Greenfield E.J., Nowak D.J., Walton J.T Assessment of 2001 NLCD Percent Tree and Impervious Cover Estimates. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING. 75(11) Toney, C., Shaw, J.D., Nelson, M.D A stem-map model for predicting tree canopy cover of forest inventory and analysis (FIA) plots. In: McWilliams, W., Moisen, G., Czaplewski, R. comps Forest Inventory and Analysis (FIA) symposium. October 21-23, Park City, UT. Proc. RMRS- P-56CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 1 CD. Coulston, J.W., Oswalt, S.N., Carraway, A.B., Smith, W.B Assessing forest land area based on canopy cover in a semi-arid region: a case study. Forestry, doi: /forestry/cpp039