Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data
Information derived from high spatial resolution remotely sensed data is critical for the effective management of forested ecosystems. However, high spatial resolution data-sets are typically costly to acquire and process and usually provide limited geographic coverage. In contrast, moderate spatial...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-07-01
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| Series: | Diversity |
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| Online Access: | http://www.mdpi.com/1424-2818/6/3/396 |
| _version_ | 1817992569570721792 |
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| author | Trevor G. Jones Nicholas C. Coops Sarah E. Gergel Tara Sharma |
| author_facet | Trevor G. Jones Nicholas C. Coops Sarah E. Gergel Tara Sharma |
| author_sort | Trevor G. Jones |
| collection | DOAJ |
| description | Information derived from high spatial resolution remotely sensed data is critical for the effective management of forested ecosystems. However, high spatial resolution data-sets are typically costly to acquire and process and usually provide limited geographic coverage. In contrast, moderate spatial resolution remotely sensed data, while not able to provide the spectral or spatial detail required for certain types of products and applications, offer inexpensive, comprehensive landscape-level coverage. This study assessed using an object-based approach to extrapolate detailed tree species heterogeneity beyond the extent of hyperspectral/LiDAR flightlines to the broader area covered by a Landsat scene. Using image segments, regression trees established ecologically decipherable relationships between tree species heterogeneity and the spectral properties of Landsat segments. The spectral properties of Landsat bands 4 (i.e., NIR: 0.76–0.90 µm), 5 (i.e., SWIR: 1.55–1.75 µm) and 7 (SWIR: 2.08–2.35 µm) were consistently selected as predictor variables, explaining approximately 50% of variance in richness and diversity. Results have important ramifications for ongoing management initiatives in the study area and are applicable to wide range of applications. |
| first_indexed | 2024-04-14T01:27:33Z |
| format | Article |
| id | doaj.art-bcac2255a8ec4122964bf8557bcdfec5 |
| institution | Directory Open Access Journal |
| issn | 1424-2818 |
| language | English |
| last_indexed | 2024-04-14T01:27:33Z |
| publishDate | 2014-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diversity |
| spelling | doaj.art-bcac2255a8ec4122964bf8557bcdfec52022-12-22T02:20:21ZengMDPI AGDiversity1424-28182014-07-016339641410.3390/d6030396d6030396Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution DataTrevor G. Jones0Nicholas C. Coops1Sarah E. Gergel2Tara Sharma3Integrated Remote Sensing Studio, Department of Forest Resources Management, 2424 Main Mall, University of British Columbia, Vancouver BC V6T 1Z4, CanadaIntegrated Remote Sensing Studio, Department of Forest Resources Management, 2424 Main Mall, University of British Columbia, Vancouver BC V6T 1Z4, CanadaLandscape Ecology Lab, Centre for Applied Conservation Research, Department of Forest and Conservation Sciences, 2424 Main Mall, University of British Columbia, Vancouver BC V6T 1Z4, CanadaParks Canada, Gulf Islands National Park Reserve of Canada, 2220 Harbour Road, Sidney BC V8L 2P6, CanadaInformation derived from high spatial resolution remotely sensed data is critical for the effective management of forested ecosystems. However, high spatial resolution data-sets are typically costly to acquire and process and usually provide limited geographic coverage. In contrast, moderate spatial resolution remotely sensed data, while not able to provide the spectral or spatial detail required for certain types of products and applications, offer inexpensive, comprehensive landscape-level coverage. This study assessed using an object-based approach to extrapolate detailed tree species heterogeneity beyond the extent of hyperspectral/LiDAR flightlines to the broader area covered by a Landsat scene. Using image segments, regression trees established ecologically decipherable relationships between tree species heterogeneity and the spectral properties of Landsat segments. The spectral properties of Landsat bands 4 (i.e., NIR: 0.76–0.90 µm), 5 (i.e., SWIR: 1.55–1.75 µm) and 7 (SWIR: 2.08–2.35 µm) were consistently selected as predictor variables, explaining approximately 50% of variance in richness and diversity. Results have important ramifications for ongoing management initiatives in the study area and are applicable to wide range of applications.http://www.mdpi.com/1424-2818/6/3/396segmentationobject-basedLandsathyperspectralLiDARheterogeneityextrapolation |
| spellingShingle | Trevor G. Jones Nicholas C. Coops Sarah E. Gergel Tara Sharma Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data Diversity segmentation object-based Landsat hyperspectral LiDAR heterogeneity extrapolation |
| title | Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data |
| title_full | Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data |
| title_fullStr | Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data |
| title_full_unstemmed | Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data |
| title_short | Employing Measures of Heterogeneity and an Object-Based Approach to Extrapolate Tree Species Distribution Data |
| title_sort | employing measures of heterogeneity and an object based approach to extrapolate tree species distribution data |
| topic | segmentation object-based Landsat hyperspectral LiDAR heterogeneity extrapolation |
| url | http://www.mdpi.com/1424-2818/6/3/396 |
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