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White Paper
State-wide Tree Canopy Extraction from NAIP
Using ERDAS IMAGINE® Spatial Modeler and Batch Processing to Extract Tree Canopy
Cover from State-wide NAIP Imagery
12 July 2019
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Contents Abstract ..................................................................................................................................... 3
Case Study................................................................................................................................. 3
NAIP and North Carolina Department of Agriculture and Consumer Services ......................... 3
NAIP Image Data Background ................................................................................................. 4
Classification Approach ........................................................................................................... 5
Batch Processing ..................................................................................................................... 9
Results ................................................................................................................................... 11
Conclusion ............................................................................................................................... 14
Acknowledgements and Disclaimers: ..................................................................................... 15
Contact us................................................................................................................................ 15
About Hexagon ........................................................................................................................ 15
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Abstract Tree canopy information plays a crucial role in understanding the geographic location and distribution of natural resources. High-detail tree canopy cover data at a parcel level can be difficult to achieve, especially using only satellite imagery over large areas. Because tree canopy calculations cover a large geographic area, obtaining timely, high-resolution imagery can be expensive and difficult.
To address this issue, the state of North Carolina Department of Agriculture and Consumer Services decided to cut costs by repurposing their existing 2016 National Agriculture Imagery Program (NAIP) imagery to extract tree canopy cover for the entire state. The Spatial Modeler in ERDAS IMAGINE® was used to process the NAIP tiles and generate state-wide tree canopy cover data. By using the Spatial Modeler, it was possible to customize and streamline the required steps in a sequence of batch processes to save time and keep project costs down.
Since the late 1970s, ERDAS IMAGINE from Hexagon’s Geospatial division has been the flagship product for remote sensing and image analysis projects. The world’s most widely-used remote sensing software, ERDAS IMAGINE processes satellite imagery, orthoimagery, UAS imagery, and even point cloud data. It contains a vast collection of processing tools and functions that have come to define the industry standard. The Spatial Modeler is the desktop drag-and-drop interface for creating and customizing processes, including building repeatable processes which can be executed in batch jobs and extended to cloud environments.
Case Study
NAIP and North Carolina Department of Agriculture and Consumer Services
The mission of the North Carolina Department of Agriculture and Consumer Services (NCDA&CS) is to provide services that promote and improve agriculture, agribusiness and forests; to protect consumers and businesses; and to conserve farmland and natural resources for the prosperity of all North Carolinians. Forest resource management is one of many aspects of the agency’s mission.
NCDA&CS is both a consumer and distributor of National Agriculture Imagery Program (NAIP) imagery. North Carolina has acquired state-wide NAIP imagery from the United States Department of Agriculture (USDA) since 2004. State-wide 4-band NAIP imagery was acquired for the first time in 2009. Since then, a variety of agencies and end users throughout North Carolina have used both NAIP imagery and the state-wide true-color leaf-off image data as a basemap for general GIS purposes. Recently, the North Carolina geospatial community has begun to explore how they can derive other value-added products from NAIP imagery in addition to using it as a simple basemap or web service.
To help meet the objective of forest resource management, tree canopy cover was extracted from the 2016 state-wide NAIP imagery collection. The tree canopy cover data will be used in a variety of applications. One of the main applications of the data will be to understand existing tree canopy resources and monitor canopy loss over time, especially for high value areas of North Carolina, such as the 33-county Sentinel Landscape in eastern North Carolina.
Another application will be to use the data to assess and monitor tree canopy cover for local municipalities and urban areas. The data will be supplied to all 100 counties in North Carolina. A couple of immediate beneficiaries of the tree canopy data, in addition to the NCDA&CS, are the City of Charlotte and the town of Fuquay Varina.
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Hexagon’s Geospatial division’s Spatial Modeler was used to apply a decision rule process to extract the tree canopy cover. The process used a variety of spectral indices, ratios, and texture processing to produce tree canopy data. This is the first time that NAIP imagery was used in an automated classification project for such a large area.
NAIP Image Data Background
NAIP imagery is a product of the USDA. There are 3,565 NAIP image tiles that cover the geographic extent of North Carolina. The image data has a 1-meter spatial resolution and consists of four spectral bands. The spectral bands include visible red, visible green, visible blue, and near infrared.
Band Wavelength (nm)
Blue 400-500
Green 500-600
Red 600-700
Near Infrared 800-900
The spectral bands provide the ability to map features such as tree canopy, agriculture areas, water bodies, and developed areas. The NAIP imagery used in this project was collected during May through September of the 2016 leaf-on season.
The NAIP image tiles are arranged to correspond to the USDA grid system. Each folder data delivery contains the NAIP tiles specific to each respective geographic section (Figure 1). Using the image acquisition date and pre-determined local geographic areas of interest, the imagery was divided into 34 sections across the state (Figure 2). Each section was processed in a sequence of separate batch routines.
Figure 1. North Carolina NAIP image data project folders arranged according to USDA Grid System naming convention.
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Figure 2. Thirty-four project sections determined by the Image Source Date and geographic proximity.
Classification Approach
There were several differences between this project and other vegetation-related projects. Satellite imagery usually involves using data that have very little difference in the date of image capture, which means that you can often mosaic all of the imagery together and then process it as a single file.
For large project areas such as state-wide North Carolina, NAIP imagery needs to be processed more attentively. The NAIP imagery has multiple collection dates throughout the leaf-on season, meaning that the pixels of similar tree canopies would have different pixel values due to different stages of the growing season. In addition, internal tonal variations due to seamline edits and photogrammetric processes related to the orthorectification of the imagery must be taken into account.
For these reasons, the imagery cannot simply be mosaicked into one larger image mosaic and then processed. That mosaicking approach would result in similar features (such as tree canopy) having significantly different pixel values, which results in more confusion pixels. In addition, large image mosaics can take a significant amount of unmanaged time to process.
To help address the internal artefacts of NAIP pixels and reduce pixel confusion while maintaining a timely process, a tile-based decision rule classification approach was designed and implemented. Running the same processes on each individual NAIP tile significantly reduced issues related to the large range of pixels values and classification confusion.
In addition, this project included an independent assessment of NAIP imagery to determine a fine-tuned approach to separate vegetation types so that tree canopy cover could be extracted with less pixel confusion.
To determine how to best classify the NAIP imagery, an evaluation of vegetation separability was performed at the beginning of the project. The evaluation included 19 spectral indices and 11 band ratios. Single band and synthetic band derivatives of NAIP were also tested for separability. From single-layer derivatives, layer stacks were created and then evaluated for individual layer contribution to classifying vegetation types. Points were plotted across candidate tiles and evaluated using their spectral profiles (Figures 3 and 4). Based on the profile separability, the best-suited layers were chosen for the extraction of tree canopy cover. A future publication may address the findings.
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Figure 3. Points plotted on layer stack and compared to Color Infrared. A spectral profile was generated to assess the separability of vegetation types.
Figure 4. An indices assessment for vegetation separability of Canopy, Grass, and Agriculture using the plot points from Figure 3 and the spectral profile tool in ERDAS IMAGINE. Each index was investigated for separability by zooming into each respective Y-axis.
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Texture analysis was also evaluated for vegetation separability. Several single-band inputs were tested using the spectral profile tool. The focus of the evaluation was to determine how well each texture layer identified tree pixels versus grass and agriculture pixels. Trees have a rougher texture compared to grass and agriculture fields (Figure 5). Using higher texture values yielded a better tree canopy result by eliminating grass and agriculture pixels. The texture result was further improved by incorporating kernel-based post-processing to reduce streamers from shadows and isolated clusters of high texture values that initially contributed to undesired results (Figure 6). When texture is combined with spectral information, grass and agricultural areas can more easily be distinguished from tree canopy pixels both visually and numerically (Figure 7).
Figure 5. Texture comparison between (a) and (b) synthesized broad-band layers and (c) CIR stack of the parent NAIP image. Synthesized broad-band derivatives visually and numerically perform well compared to texture generated from any of the individual red, green, blue, or NIR bands of NAIP imagery. Darker pixels correspond to smoother texture that are associated with grass and agriculture.
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Figure 6. Tree canopy cover color in green (a), can be improved (b) by kernel-based post-processing functions and thresholding to remove confusion with non-canopy features such as healthy grass and agricultural areas. Notice the reduced pixel confusion depicted by the yellow arrows in image (b) that are related to non-canopy features such as shadow edges, grass, and agricultural areas.
Figure 7. Pictured is the original NAIP CIR stack on the left and RGB layer stack composite on the right. The layer stack is a combination of texture and spectral inputs (indices and ratios). Water and moist areas trend towards orange-red color pixel values. Bare ground trends towards yellow color pixel values. Grass and agriculture areas trend towards green pixel values. Tree canopy pixels trend towards blue and purple color pixels representing values that can be used for threshold settings in the decision tree classification approach. Composite layer stacks were used to determine decision rule threshold values as well as a visual tool to see how well features could be separated.
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After the best-suited layers were determined to separate vegetation types from NAIP imagery, a decision rule classification approach was designed to output a thematic 2-class raster image that consisted of canopy and non-canopy pixels. These inputs were used for all 3,565 tiles. Each of the 34 sections were processed individually with varying threshold settings for each input.
To summarize, the entire workflow consisted of:
1. Pre-processing NAIP bands into derivative raster products, such as indices and ratios. 2. Calculating and post-processing texture derivative layers. 3. Applying a decision rule classification process to the derivative spectral and texture layers. 4. Outputting a 2-class thematic tree canopy raster to match the NAIP tile naming convention.
The Spatial Modeler is ideally suited for this workflow, since customizations were required for streamlining the processes.
Batch Processing
The Spatial Modeler was used to execute most of the batch processes for the project. Spatial models were created to perform the following processes:
• Generate derivative data layers.
• Implement the decision rule classification process.
• Post-process the thematic canopy data.
The spatial models were executed for all 3,565 NAIP tiles for each of the 34 project sections. A single spatial model was designed and used to accomplish the pre-processing steps (Figure 8). The pre-processing steps included calculating indices, synthetic broad band layers, and spectral ratios. Texture batch processing was performed from the ERDAS IMAGINE Raster interface, not within the Spatial Modeler. A decision rule process was designed using the Spatial Modeler Editor to extract tree canopy data (Figure 9). The resulting thematic tree canopy was then cleaned up in batch jobs using post-processing functions in the ERDAS IMAGINE Thematic menu. A final spatial model was created to rename each tree canopy file so that it matched with its parent input NAIP tile.
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Figure 8. The ERDAS IMAGINE Spatial Model used to generate the NAIP derivatives to be input into either the texture process or the decision rule classification.
Figure 9. A spatial model was used to implement the decision rule process. The decision rule used the most discriminant input layers to determine tree canopy cover based on the previously mentioned assessments. The Criteria Operator was used to manage the decision rule threshold values. Threshold values were slightly modified for each section based on a candidate test image.
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All the steps of the workflow could have been included in a single spatial model but breaking up the processes into several spatial models provided the ability to perform quality assessment checks along the way. As a result, a few sections were processed multiple times due to initial classification issues or in a rare instance when a file was missed in the batch list. Manual editing was not used at any point in the project, which provided a huge time and cost savings. The size of the original NAIP imagery was 770GB. Approximately 16TB of storage was used for intermediate file storage and processing. The final deliverable to the NCDA&CS was 3.7GB of TIF files that correspond to each parent NAIP image tile.
Results
The final product consisted of thematic 2-class TIFF files reporting the geographic location and extent of canopy versus non-canopy areas across the state of North Carolina. The output files matched the naming convention of each input NAIP tile with “_canopy.tif” appended to the filename. For example, input NAIP tile m_3407710_nw_1_20160620.tif corresponds to canopy file m_3407710_nw_1_20160620_canopy.tif.
The 10-week project did not include an accuracy assessment. Upon visual quality reviews for each project section, the canopy data matched tree features across each NAIP tile consistently from tile to tile (Figures 10, 11, and 12). Typical instances where there was noticeable pixel confusion occurred in three scenarios.
The first issue of missing canopy pixels occurred in very small areas where the look perspective of the image was straight down nadir and the sun’s position was directly overhead the sensor at the time of acquisition. This resulted in small patches of tree canopy having a very smooth texture and less shadowing. Therefore, the decision rule threshold for texture missed some small patches of tree canopy pixels. Thematic post-processing addressed most of these issues but could not resolve all issues without removing small isolated tree stands.
The second issue is the opposite, where high texture agriculture areas were classified as tree canopy. Applying a texture threshold worked well in separating agriculture lands from tree canopy. However, there were instances where the agriculture area texture was very similar to smooth tree canopy values, which resulted in canopy confusion pixels. Attempting to address this by raising the texture threshold would increase the number of canopy holes mentioned in the previous issue.
The third issue occurred in western portions of the state where conifer trees on slopes were missed due to the combination of darker-colored vegetation and increased shadows contributing to the pixel values. Lowering the spectral thresholds to include missed conifer pixels would increase the canopy pixels beyond deciduous cover to include more non-canopy features. In the western areas of the state where significant conifer exclusion occurred, the input data derivatives and decision rule thresholds are being modified to consider a conifer-focused approach to add to the existing canopy cover data.
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Figure 10. Example of tree canopy cover for an area northwest of New Bern, NC. The image is about 8.5 kilometers across. Tree canopy cover is colored in green overlaid on the color infrared NAIP imagery.
Figure 11. Tree canopy cover in the area of Camp Lejeune residential properties. Tree canopy cover is colored in green overlaid on the color infrared NAIP imagery.
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Figure 12. Tree canopy cover for the area of Asheville, NC. Tree canopy cover is colored in green overlaid on the color infrared NAIP imagery.
Combined, the total calculated area of tree canopy cover is 31,666.6 square miles of the total 53,819 square miles of the state of North Carolina. By this calculation, 58.8% of the state is covered by tree canopy. As a comparison, the USDA Forest Service Forest Inventory and Analysis (FIA) reports 59% of North Carolina is covered by forest according to Wikipedia.com. The results of this NAIP Canopy project compare nicely to the USDA analysis. The added benefit of using NAIP is the 1-meter spatial detail.
Compared to existing forest canopy cover data, this effort is impressive in terms of both scale of the project and detail of the resulting data. 3,565 NAIP tiles were processed to generate tree canopy cover at 1-meter spatial resolution (Figure 13). The canopy data is currently being distributed to North Carolina local governments for quality review.
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Figure 13. Tree canopy cover for the entire state of North Carolina. The total square miles of tree canopy cover resulting from the data is 31,666.6 square miles. This is 58.8% of the state of North Carolina. The no-data area in the middle contained no NAIP image tiles.
Conclusion North Carolina is a progressive state when it comes to geospatial technologies. NCDA&CS can confidently apply the canopy data to better understand the geographic location and distribution of tree canopy cover for their areas of interest. By using ERDAS IMAGINE and the Spatial Modeler, the process can be repeated for future NAIP image acquisitions. The same process can also be considered for previous NAIP acquisitions, which can then be used for change detection applications to better understand the loss of tree canopy cover for targeted areas of interest, such as the 33 counties covering the Sentinel Landscape and individual municipalities implementing urban tree canopy ordinances. Other states with NAIP imagery can deploy the same workflow using the classification and batch processing capabilities of ERDAS IMAGINE and the Spatial Modeler.
ERDAS IMAGINE and the Spatial Modeler were key to the success of this project. Significant time was saved by using spatial models and batch processing. The time savings resulted in significant cost savings as well. The project is repeatable for other states and future collections of NAIP imagery. This extends the cost benefit of NAIP Imagery and Hexagon Geospatial technology.
The processes applied in this project are being modified to address additional datasets such as Planet, Landsat and Sentinel-2 imagery. The spatial modeling component of this project can be extended to cloud processing analytics that can expose sophisticated processes to the masses and to end users who benefit from the value of remotely sensed image data.
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Acknowledgements and Disclaimers:
The State-wide Tree Canopy Extraction from NAIP project Using Spatial Modeler and Batch Processing to extract tree canopy cover from NAIP imagery was exclusively performed by Frank Obusek, who is an Independent Geospatial Solutions Consultant. At time of this project, Frank was a temporary employee of the State of North Carolina. This white paper was written by Frank Obusek in collaboration with employees from Hexagon Geospatial. Frank has 20 years of experience in ERDAS IMAGINE in the geospatial industry in a variety of professional roles. You can contact Frank Obusek at [email protected].
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