Source: TENNESSEE STATE UNIVERSITY submitted to NRP
USING SPOT 7 AND LANDSAT 8 SATELLITE DATA IN MAPPING AND MONITORING SOFTWOOD FOREST VEGETATION AND CANOPY WATER CONTENT IN TENNESSEE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
MCINTIRE-STENNIS
Reporting Frequency
Annual
Accession No.
1021271
Grant No.
(N/A)
Cumulative Award Amt.
(N/A)
Proposal No.
(N/A)
Multistate No.
(N/A)
Project Start Date
Oct 7, 2019
Project End Date
Sep 30, 2022
Grant Year
(N/A)
Program Code
[(N/A)]- (N/A)
Recipient Organization
TENNESSEE STATE UNIVERSITY
3500 JOHN A. MERRITT BLVD
NASHVILLE,TN 37209
Performing Department
Agricultural and Environmental Sciences
Non Technical Summary
The mapping and monitoring of softwood forest vegetation and canopy water content is critical for forestry applications such as canopy stress analysis, fire susceptibility and forest productivity. However, there is not known knowledge of the changes in softwood forest vegetation and canopy moisture content in Tennessee. The objectives of this study are: 1) gather and compile Landsat 8 and spot 7 satellite data; 2) examine different data fusion techniques in mapping softwood forest vegetation using SPOT 7 panchromatic and Landsat 8 satellite data; and 3) map and monitor softwood forest vegetation and canopy water content using Landsat 8 and SPOT 7 satellite data in the last 5 years when satellites were launched. Machine learning random forest algorithm will be used to map and monitor softwood forest vegetation. Forest canopy water content will be mapped and monitored using Normalized Difference Water Index (NDWI). The study will improve forest vegetation mapping and will further enhance forest management and planning in Tennessee.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
12306112060100%
Knowledge Area
123 - Management and Sustainability of Forest Resources;

Subject Of Investigation
0611 - Conifer forests of the South;

Field Of Science
2060 - Geography;
Goals / Objectives
Research Objective 1: Gather and compile Landsat 8 and spot 7 satellite data.Research Objective 2: Examine different data fusion techniques in mapping softwood forest vegetation using SPOT 7 panchromatic and Landsat 8 satellite data.Research Objective 3: Map and monitor softwood forest vegetation and canopy water content using Landsat 8 and SPOT 7 satellite data in the last 5 years when satellites were launched.
Project Methods
Research Objective 1: Gather and compile Landsat 8 and SPOT 7 satellite data:Landsat 8 cloud free satellite images covering the Tennessee region will be downloaded from the United States Geological Society (USGS) website (https://earthexplorer.usgs.gov/). Landsat 8 satellite data with acquisition dates in the winter months of 2019 and 2014 will be downloaded because softwood forest vegetation are conifers that are evergreen in winter months when deciduous vegetation drop-off their leaves. SPOT 7 satellite data will be purchased from Airbus Defense and Space (https://www.intelligence-airbusds.com/en/8693-spot-67) via Harris Geospatial Solutions. Cloud free SPOT 7 satellite data with acquisition dates similar to the Landsat 8 satellite data will be acquired. The satellite images will be stored in a geospatial database for further image analysis.Research Objective 2: Examine different data fusion techniques in mapping softwood forest vegetation using SPOT 7 panchromatic and Landsat 8 satellite data:The Landsat 8 and SPOT 7 satellite data compiled in objective 1 with acquisition date of 2019 will be mosaiced and co-registered using ground control points. At-surface reflectance information will be generated from the satellite data by converting their digital numbers (DN) to at-surface reflectance.Landsat 8 reflectance data will be integrated with SPOT panchromatic band by using different data fusion techniques. The following data fusion techniques will be used and tested: Hue Saturation Intensity; Brovey Transform, High Pass Filter Additive; and Transparency Merge.The merged Landsat 8 and SPOT 7 images derived from the different data fusion techniques will be used to classify and map softwood forest vegetation. This will involve extracting spectral signatures of softwood forest vegetation (loblolly, short leaf and virginia pines) using digitized training polygons. Supervised classification such as machine learning random forest classifier will be used to classify and map the softwood forest vegetation types. The classified maps of southern yellow pines will be validated by using ancillary plot datasets of southern yellow pines and Google Earth.Research Objective 3: Map and monitor softwood forest vegetation and canopy water content using Landsat 8 and SPOT 7 satellite data in last 5 years when satellites were launched.Landsat 8 reflectance satellite data with acquisition dates of 2019 and 2014 will be used to generate Normalized Difference Water Index (NDWI) as indicator of vegetation canopy water content.The NDWI will be generated from Landsat 8 satellite data using eq. 1. NDWI = (NIR-MidIR)/(NIR+MidIR)-------eq.1.Landsat 8 reflectance data with acquisition dates of 2019 and 2014 will be integrated with SPOT panchromatic band with similar acquisition dates. The data fusion of Landsat 8 and SPOT panchromatic band will be carried out using the data fusion technique that best delineates softwood forest vegetation derived in Research Objective 2.The merged Landsat 8 and SPOT 7 reflectance images with acquisition dates of 2019 and 2014 will be used to classify, map and monitor softwood forest vegetation. This will involve extracting spectral signatures of softwood forest vegetation (loblolly, short leaf and virginia pines) using digitized training polygons. Supervised classification such as machine learning random forest classifier will be used to classify and map the softwood forest vegetation types. The classified maps of southern yellow pines will be validated by using ancillary plot datasets of southern yellow pines and Google Earth. The classified softwood forest vegetation maps will be overlaid to the generated NDWI maps to extract mean values of NDWI corresponding to the various softwood forest vegetation types. An analysis of vegetation type extent and canopy water content index in the years of 2019 and 2014 will be performed.

Progress 10/01/20 to 09/30/21

Outputs
Target Audience:TN Department of Agriculture Forestry companies Forestry county extension agents Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? The project has provided training for one undergraduate student and one graduate student related to satellite data acquisition and image processing How have the results been disseminated to communities of interest?Some of the project findings were presented to extension agents during the Tennessee Small Farm Expo What do you plan to do during the next reporting period to accomplish the goals?I plan to complete all accuracy assessments.

Impacts
What was accomplished under these goals? Objective 1 The gathering and compilation of Landsat 8 and Spot 7 satellite data have been conducted. Level 1 scenes of satellite images have been pre-processed for radiometric and geometric corrections. Objective 2 Landsat 8 and SPOT reflectance scenes have been fused with their panchromatic band using Brovey Transform, Hue Saturation Intensity, Smooth Filter Intensity Modulation, High Pass Filter. The classification of southern yellow pines has been performed. Accuracy assessments of classified maps are in progress. Objective 3 Multi-temporal satellite images have been downloaded and pre-processing has been performed. The classification and mapping of softwood forest vegetation (southern yellow pines) using multi-temporal images have been conducted. Normalized Difference Wetness Index has been generated as a measure to monitor canopy wetness of southern yellow pines. Accuracy assessments of multi-temporal classified maps are in progress.

Publications

  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Akumu, C.E. & Smith, R., Haile, S. (2021) Mapping and Monitoring the Canopy Cover and Greenness of Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines) in Central-Eastern Tennessee Using Multi-Temporal Landsat Satellite Data. Forests, 12 (499), https://doi.org/10.3390/f12040499
  • Type: Journal Articles Status: Accepted Year Published: 2021 Citation: Akumu, C.E. & Eze, A. (2021) Examining the Integration of Landsat Operational Land Imager (OLI) with Sentinel-1 and Vegetation Indices in Mapping of Southern Yellow Pines (Loblolly, Shortleaf and Virginia Pines). Photogrammetric Engineering and Remote Sensing, In Press


Progress 10/07/19 to 09/30/20

Outputs
Target Audience:TN Department of Agriculture Forestry companies Forestry county extension agents Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project has recruited and provided training and professional development opportunities to one graduate student to support project objective 3. How have the results been disseminated to communities of interest?Results have been disseminated through the Small Farmers Symposium at Tennessee State University What do you plan to do during the next reporting period to accomplish the goals?Carry out data fusion of Landsat 8 and Spot panchromatic bands

Impacts
What was accomplished under these goals? Research Objective 1: Gather and compile Landsat 8 and spot 7 satellite data. -The gathering and compilation of Landsat 8 and Spot 7 satellite data have been carried out. Pre-processing (radiometric and geometric correction) of Landsat and Spot satellite data acquired in 2019 have been carried out. Research Objective 2: Examine different data fusion techniques in mapping softwood forest vegetation using SPOT 7 panchromatic and Landsat 8 satellite data. -In progress Research Objective 3: Map and monitor softwood forest vegetation and canopy water content using Landsat 8 and SPOT 7 satellite data in the last 5 years when satellites were launched. -The mapping and monitoring of softwood vegetation (southern yellow pines) and canopy water content is in progress. The satellite images have been downloaded and pre-processing is in progress.

Publications

  • Type: Other Status: Other Year Published: 2020 Citation: Adeyinka, A. and Akumu, C.E. (2020). Mapping and monitoring softwood forest vegetation cover and canopy water content using Landsat TM and Landsat OLI satellite data in eastern Tennessee. Thesis Proposal, College of Agriculture, Tennessee State University, November, 5th, 2020