GPS Located Accuracy Assessment Plots on the Modoc National Forest

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1 This file was created by scanning the printed publication. Errors identified by the software have been corrected; however, some errors may remain. GPS Located Accuracy Assessment Plots on the Modoc National Forest Kevin Casey Abstract. A random grid of field plots were evaluated in a blind test to measure the accuracy of remotely sensed vegetation polygons on the Modoc National Forest. Field crews gathered integrated inventory data on all vegetation, as well as collecting accuracy assessment data. GPS positions were collected at plot centers and later post-processed to achieve 2-5 meter horizontal accuracy. Plots were not rotated into condition and field crews had no knowledge of the location of mapped polygons or the label attributes of the polygons. This methodology allows for collection of accuracy assessment data at the same time that inventory data is collected, thereby eliminating the need for separate crews and multiple visits to the field. INTRODUCTION Forest vegetation inventories are often based upon a polygon map derived from remote sensing tools. In Region 5 of the U.S. Forest Service (California) vegetation maps are produced at ten year intervals using a combination of TM imagery, color aerial photography and field training sites. The inventory plots are located using a randomized grid of points. The standard grid used in the western U.S.A. has points at 5000 meter intervals. A densified grid is used for conifer forest, having points at 2500 meter intervals. The grid of plots is used to gather basic vegetation composition, structure and growth data. From these data a forest inventory can be compiled. In addition, data needed to perform a formal accuracy assessment of the vegetation map can be collected. This methodology saves time and expense by combining the data collection needs for both the inventory and the accuracy assessment into one project. MAPPING The vegetation map for the Modoc National Forest is based upon 1991 TM imagery which has been terrain corrected and georeferenced. Polygons are created using a clustering algorithm and labels are attached to polygons using a combination of supervised and unsupervised classification. The minimum mapping unit is approximately one hectare, or ten pixels. The CALVEG system of vegetation classification is used to label polygons. In addition, for conifer and Project Leader for integrated resource inventories, Region 5, U.S. Forest Senice Remote Sensing Lab, Sacramento, California.

2 hardwood types, a crown model is used to predict stand size class and density class. The mapping generally occurs prior to the inventory field work, but since the inventory grid is entirely independent of the map, field data collection can occur before or after the map is completed. PLOT LOCATION Oflice Procedures The 5000 meter (or 2500 meter) grid is imported into a GIs in the form of a text file containing UTM coordinate pairs (MOSS format). It is clipped with the forest administrative boundary and then viewed on-screen with a SPOT panchromatic image as a backdrop. Each point of the grid is manually transferred to a color aerial photograph. The photographs are pin-pricked and labeled on the back with a plot number. The plots are also transferred to 1:24,000 scale topographic maps to aid the field crews in locating the plots. UTM coordinates for each plot are printed, also to aid the field crew with the plot location on the ground. No information regarding the vegetation polygon map is put on any of the materials given to the field crew. Field Procedures Field crews can use any of the supplied materials to locate the plot center on the ground. The pin-pricked photo is the control, and a tolerance of 20 meters is allowed for error. Crews are equipped with a GPS receiver, and it can be used to navigate to the approximate plot center by entering the UTM coordinates of the plots into the receiver. Final navigation must be done using the aerial photograph. The Cluster Plot A five point cluster plot is installed, centered on the pin-pricked location. (See Figure 1.) The total size of the cluster plot is approximately one hectare. At each point of the cluster, vegetation data is measured and entered into a data recorder. A series of circular, nested fixed-area plots are used for collecting vegetation data. Trees are measured individually; shrubs, grasses and forbs are tallied by species and percent cover. The inventory also collects data on snags, down logs and woody debris of various sizes.

3 FIA 5 Point Cluster Plot f 1 H ctare Circle Figure 1. -Plot Configuration The CALVEG Accuracy Assessment Form An accuracy assessment form was developed to capture an independent assessment of the vegetative cover at each sub-plot of the cluster plot. (See Figure 2.) The crew uses a dichotomous key to select the Primary Cover Type and Best CALVEG Type for each of the five subplots. Cover Types are limited to a rather short list; examples being Conifer, Hardwood, Sagebrush shrub, Mixed Chaparral, Herbaceous, etc. Each Cover Type contains many different CALVEG Types. Some examples for the Conifer type are: Mixed Conifer-pine (MP), Mixed Conifer-fir (MF), East-side Ponderosa pine (EP), Western juniper (WJ), and so on. The plots occasionally fall on an ecotone, or boundary, between two or more distinctive types. Condition codes are used to capture this information. Condition A is used for the vegetation a point #1. If another distinctive type occurs elsewhere on the cluster, it becomes Condition B. For Conifer and Hardwood types, the crew is instructed to evaluate the best size class, and best density class for the stand of trees occupying the plot. In affect, we ask the crew to provide the "best fit" map label for the vegetation at each of the five subplots.

4 X POINT NO. 2 X CONDITION (A,B,C)!Q X PRIMARY X COVER 0 X TYPE X BEST CALVEG X TYPE - -- P X NEXT BEST X CALVEG TYPE F X BEST SIZE CLASS C" X SECOND BEST SIZE? -- - X BEST DENSITY CLASS P X SECOND BEST DENSITY 'D X SECONDARY X COVER A P X TYPE X BEST CALVEG A A X TYPE X NEXT BEST -A X CALVEG TYPE ru 2 X BEST SIZE CLASS 9' A X SECOND BEST SlZE A A X BEST DENSITY CLASS.a X SECOND BEST DENSITY

5 GPS OPERATIONS Field Procedures Crews collect GPS data at the plot center (Point #1) only. The specifications require a minimum of 180 positions be logged. The receiver is set up to record 3- D positions only. The elevation mask is set at 15 degrees, the PDOP mask is set at six and the Signal Level mask is set at six. These parameters were selected to Plot Diagram Plot 285 ~GPS file: R071819A.SSF at Point #L ~ate:7/18/95 ( Figure 3. -Example of split condition plot. produce high quality positions that can be differentially corrected to yield horizontal accuracy of 2-5 meters. Occasionally it is not possible to collect GPS positions at the plot center due to overhanging trees or the terrain. Crews can collect GPS positions at one of the other four subplots, provided they note the location GPS data was collected on the plot diagram. Post Processing The U.S. Forest Service operates three permanent GPS base stations in northern California. A Susanville base station was used to perform differential corrections for the Modoc project. It is located 50 to 150 miles from the plot locations on the Modoc forest. Nearly 100% percent success was achieved in correcting the rover

6 positions. The mean corrected position for each plot was exported to MOSS format and then brought into a GIs. The actual GPS position of the plot replaces the "predicted" position stored in the database. The difference between the predicted location of the plot center and the GPS located plot center is usually meters horizontal distance. Figure 4 illustrates an example of how a plot falls within a vegetation polygon. Note that the "predicted" plot location can result in a plot falling into one polygon, while the GPS position falls into a different polygon. The predicted plot location would suggest that Veg Type B would best match the vegetation data collected in the field. However, the GPS plot location falls primarily into Veg Type A. Using the predicted plot location for an accuracy assessment would yield incongruous results. Figure 4. - Example of Predicted vs. GPS plot location. ACCURACY ASSESSMENT Analysis of Data ~ccurac~ assessment field data consists of completed forms as shown in Figure 5. Cover type, CaVEG type, size and density are compared to the polygon attributes of the polygon(s) that intersects with the GPS plot location. In the case of multiple-condition plots, the vegetation map is examined for conformity with the condition boundaries indicated on the Plot Diagram (see Figure 3.) An semiautomated approach has been developed for capturing the map label data fiom the

7 vegetation map. It involves buffering the GPS plot locations with a given radius, resulting in a polygon coverage of circles roughly one hectare in size. This polygon coverage is overlaid onto the vegetation coverage producing a frequency report listing all the vegetation map labels intersecting each buffered plot. The frequency report is the best indicator of how well the vegetation map compares with the actual vegetation as described by the field crew. - - VIOA - FOREST WRWCE M- I CALVEG ACCURACY ASSESSMENT RECORD Figure 5. -Example of completed CALVEG data entty form. CONCLUSIONS Spatial Accuracy There are several aspects of "accuracy" that can be defined when evaluating forest vegetation maps. Spatial accuracy - or how well a feature on the map matches with a feature on the ground is a key aspect that needs to be evaluated. In forest vegetation, often the best measure of spatial accuracy is how well the boundary between forest and non-forest is represented on the map. A classic example would be a small meadow within a stand of conifers. A perfectly registered vegetation map would depict the meadow in the same location as a GPS traverse of the meadow. Or, as in this case, the condition boundary described by the field crew would match with a polygon boundary in the vegetation map. Because maps derived from TM imagery have errors inherent with registration, as well as other factors, it is very important that the accuracy assessment plot be located with a minimum of error. Using corrected GPS data to properly locate the plots in the GIs is crucial to this effort. The less uncertainty there is in the location of the accuracy assessment plot, the better we can evaluate the accuracy of the map itself

8 Map Label Accuracy The accuracy of map labels in depicting the "right" vegetation type is another aspect to evaluate. This can be done at several levels. The vegetation maps contain a heiarchy of information about vegetation: Life Form/CALVEG/Size & Density. At the most basic level, we need to know how well the map represents Life Forms. Example: how many of the Conifer plots are actually mapped as Conifer? At the CALVEG level we need to know how many of the Red fir plots are mapped as Red fir? And at the next level we need to know how many of the Large Treemgh Density Red fir plots are mapped as Large Tree/High Density? The accuracy assessment plots installed on the Modoc National Forest provide the data to answer these questions. They also contain enough "firzziness7' information to allow for interpretation of wrong map labels. If map labels are wrong, we want to know if they are only slightly wrong, or completely wrong. For users of the vegetation map, these issues can be very important. Project Efficiency Combining the collection of accuracy assessment data with the standard inventory plot has improved the efficiency of the project. The accuracy data can be collected quickly, once on site. As with most field data collection projects, the largest cost item is getting to the plot. Provided that the plot work can be completed in one day by a two person crew, there is minimal additional expense incurred by collecting the accuracy data. Some additional training is necessary for crews to complete the accuracy assessment form, but the training can be combined with normal inventory procedure training. Using GPS to precisely locate plot centers is valuable from several standpoints. For the accuracy assessment, it provides control; a position accurate to only a few meters, that can be compared with a map product. If the map contains registration errors, the GPS data will show it conclusively. Secondly, having precise GPS coordinates of the plot location allow for independent evaluation of other map products that describe life form, cover or vegetation. Thirdly, the GPS coordinates provide a navigational aid in relocating the plots for hture remeasurement. REFERENCES Forest Inventory and Analysis User's Guide, 19%. Published by USDA Forest Service, Region 5 - Pacific Southwest..Integration of Inventory and Field Data for Automated Fuzzy Accuracy Assessment of Large Scale Remote-Sensing Derived Vegetation Maps in Region 5 National Forests, Jeff A. Milliken and Curtis E. Woodcock.