Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery
Maps of forest biomass are important tools for managing natural resources and reporting terrestrial carbon stocks. Using the San Juan National Forest in Southwest Colorado as a case study, we evaluate regional biomass maps created using physical variables, spectral vegetation indices, and image text...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2014-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/6/7/6407 |
| _version_ | 1818320605155426304 |
|---|---|
| author | Katharine C. Kelsey Jason C. Neff |
| author_facet | Katharine C. Kelsey Jason C. Neff |
| author_sort | Katharine C. Kelsey |
| collection | DOAJ |
| description | Maps of forest biomass are important tools for managing natural resources and reporting terrestrial carbon stocks. Using the San Juan National Forest in Southwest Colorado as a case study, we evaluate regional biomass maps created using physical variables, spectral vegetation indices, and image textural analysis on Landsat TM imagery. We investigate eight gray level co-occurrence matrix based texture measures (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) on four window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9) at four offsets ([1,0], [1,1], [0,1], [1,−1]) on four Landsat TM bands (2, 3, 4, and 5). The map with the highest prediction quality was created using three texture metrics calculated from Landsat Band 2 on a 3 × 3 window and an offset of [0,1]: entropy, mean and correlation; and one physical variable: slope. The correlation of predicted versus observed biomass values for our texture-based biomass map is r = 0.86, the Root Mean Square Error is 45.6 Mg∙ha−1, and the Coefficient of Variation of the Root Mean Square Error is 0.31. We find that models including image texture variables are more strongly correlated with biomass than models using only physical and spectral variables. Additionally, we suggest that the use of texture appears to better capture the magnitude and direction of biomass change following disturbance compared to spectral approaches. The biomass mapping methods we present here are widely applicable throughout the US, as they are based on publically available datasets and utilize relatively simple analytical routines. |
| first_indexed | 2024-12-13T10:27:40Z |
| format | Article |
| id | doaj.art-1afb826a7ede4113bee9fda4ffe400bc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:27:40Z |
| publishDate | 2014-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1afb826a7ede4113bee9fda4ffe400bc2022-12-21T23:50:57ZengMDPI AGRemote Sensing2072-42922014-07-01676407642210.3390/rs6076407rs6076407Estimates of Aboveground Biomass from Texture Analysis of Landsat ImageryKatharine C. Kelsey0Jason C. Neff1Environmental Studies Program, University of Colorado, 2200 Colorado Ave., Boulder, CO 80309, USAEnvironmental Studies Program, University of Colorado, 2200 Colorado Ave., Boulder, CO 80309, USAMaps of forest biomass are important tools for managing natural resources and reporting terrestrial carbon stocks. Using the San Juan National Forest in Southwest Colorado as a case study, we evaluate regional biomass maps created using physical variables, spectral vegetation indices, and image textural analysis on Landsat TM imagery. We investigate eight gray level co-occurrence matrix based texture measures (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) on four window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9) at four offsets ([1,0], [1,1], [0,1], [1,−1]) on four Landsat TM bands (2, 3, 4, and 5). The map with the highest prediction quality was created using three texture metrics calculated from Landsat Band 2 on a 3 × 3 window and an offset of [0,1]: entropy, mean and correlation; and one physical variable: slope. The correlation of predicted versus observed biomass values for our texture-based biomass map is r = 0.86, the Root Mean Square Error is 45.6 Mg∙ha−1, and the Coefficient of Variation of the Root Mean Square Error is 0.31. We find that models including image texture variables are more strongly correlated with biomass than models using only physical and spectral variables. Additionally, we suggest that the use of texture appears to better capture the magnitude and direction of biomass change following disturbance compared to spectral approaches. The biomass mapping methods we present here are widely applicable throughout the US, as they are based on publically available datasets and utilize relatively simple analytical routines.http://www.mdpi.com/2072-4292/6/7/6407forestcarbonerrorregional biomass mapSan Juan National Forest |
| spellingShingle | Katharine C. Kelsey Jason C. Neff Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery Remote Sensing forest carbon error regional biomass map San Juan National Forest |
| title | Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery |
| title_full | Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery |
| title_fullStr | Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery |
| title_full_unstemmed | Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery |
| title_short | Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery |
| title_sort | estimates of aboveground biomass from texture analysis of landsat imagery |
| topic | forest carbon error regional biomass map San Juan National Forest |
| url | http://www.mdpi.com/2072-4292/6/7/6407 |
| work_keys_str_mv | AT katharineckelsey estimatesofabovegroundbiomassfromtextureanalysisoflandsatimagery AT jasoncneff estimatesofabovegroundbiomassfromtextureanalysisoflandsatimagery |