scholarly journals Multitemporal Analysis Using Landsat Thematic Mapper (TM) Bands for Forest Cover Classification in East Texas

2008 ◽  
Vol 32 (1) ◽  
pp. 21-27
Author(s):  
Jason C. Raines ◽  
Jason Grogan ◽  
I-Kuai Hung ◽  
James Kroll
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Forest Types ◽  
Significant Difference ◽  
Potential Benefits ◽  
Land Cover Maps ◽  
Band Combinations

Abstract Land cover maps have been produced using satellite imagery to monitor forest resources since the launch of Landsat 1. Research has shown that stacking leaf-on and leaf-off imagery (combining two separate images into one image for processing) may improve classification accuracy. It is assumed that the combination of data will aid in differentiation between forest types. In this study we explored potential benefits of using multidate imagery versus single-date imagery for operational forest cover classification as part of an annual remote sensing forest inventory system. Landsat Thematic Mapper (TM) imagery was used to classify land cover into four classes. Six band combinations were tested to determine differences in classification accuracy and if any were significant enough to justify the extra cost and increased difficulty of image acquisition. The effects of inclusion/exclusion of the moisture band (TM band 5) also were examined. Results show overall accuracy ranged from 72 to 79% with no significant difference between single and multidate classifications. We feel the minimal increase (3.06%) in overall accuracy, coupled with the operational difficulties of obtaining multiple (two), useable images per year, does not support the use of multidate stacked imagery. Additional research should focus on fully utilizing data from a single scene by improving classification methodologies.

The forest resource of Labrador: Landsat Thematic Mapper imagery provides a unique and current perspective

1993 ◽  
Vol 69 (6) ◽  
pp. 667-671 ◽  
Author(s):  
John A. Drieman
Keyword(s):  
Forest Cover ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Forest Resource ◽  
Visual Interpretation ◽  
Digital Format ◽  
Current Perspective ◽  
Colour Composite ◽  
Verification Process ◽  
Forest Cover Types

The need for a current, regional perspective of the forest of Labrador was identified. Mapping of forest cover types, peat-lands, recent burns and clearcut disturbances was accomplished through visual interpretation of 1:1,000,000 scale Landsat Thematic mapper colour composite transparencies and the transfer of interpreted polygons to a geographic information system. The mapping and verification process is described in this paper. The end product, a forest resource map, provides the most up-to-date and detailed information on Labrador's forest cover types and disturbances available on a single map. The digital format of the map facilities area summaries, viewing and printing.

scholarly journals A comparison of forest resource inventory, provincial land cover maps and field surveys for wildlife habitat analysis in the Great Lakes – St. Lawrence forest

2010 ◽  
Vol 86 (1) ◽  
pp. 77-86 ◽  
Author(s):  
Andrea J. Maxie ◽  
Karen F. Hussey ◽  
Stacey J. Lowe ◽  
Kevin R. Middel ◽  
Bruce A. Pond ◽  
...  
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Satellite Image ◽  
Wildlife Habitat ◽  
Forest Resource ◽  
Habitat Analysis ◽  
Resource Inventory ◽  
Forest Resource Inventory ◽  
Land Cover Maps ◽  
Forest Cover Types

In a portion of central Ontario, Canada we assessed the classification agreement between field-based estimates of forest stand composition and each of two mapped data sources used in wildlife habitat studies, the Forest Resource Inventory (FRI) and satellite-image derived Provincial Land Cover (PLC). At two study areas, Algonquin Provincial Park (APP) and Wildlife Management Unit 49 (WMU49), we surveyed 119 forest stands and 40 water and wetland stands. Correspondence levels between FRI and field classifications were 48% in APP and 44% in WMU49 when assessing six forest cover types. With only four simplified forest cover types, levels improved to 77% in APP and 63% in WMU49. Correspondence between PLC and field classifications for three forested stand types was approximately 63% in APP and 55% in WMU49. Because of the poor to moderate level of correspondence we detected between map and field classifications, we recommend that care be exercised when FRI or PLC maps are used in forest and wildlife research and management planning. Key words: forest resource inventory, FRI, provincial land cover, PLC, Landsat Thematic Mapper, map accuracy, map correspondence, map agreement, Ontario, wildlife habitat

Landsat thematic mapper studies of land cover spatial variability related to hydrology

1984 ◽  
Vol 4 (11) ◽  
pp. 217-226 ◽  
Author(s):  
S. Wharton ◽  
J. Ormsby ◽  
V. Salomonson ◽  
P. Mulligan
Keyword(s):  
Land Cover ◽  
Spatial Variability ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper

scholarly journals SCS+C Topographic Correction to Enhance SVM Classification Accuracy

2020 ◽  
Vol 2 ◽  
pp. 32-37
Author(s):  
Jwan AL-Doski ◽  
Shattri B. Mansor ◽  
H'ng Paik San ◽  
Zailani Khuzaimah
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Main Step ◽  
Support Vector ◽  
Landsat 8 ◽  
Mountainous Terrain ◽  
Topographic Correction ◽  
Corrected Image ◽  
Svm Algorithm ◽  
Land Cover Maps

The topographic impact may change the radiance values captured by the spacecraft sensors, resulting in distinct reflectance value for similar land cover classes and mischaracterization. The problem can be more clearly seen in rugged terrain landscapes than in flat terrains, such as the mountainous areas. In order to minimize topographic impacts, we suggested the implementation of Modified Sun-Canopy-Sensor Correction (SCS+C) technique to generate land cover maps in Gua Musang district which is located in a rugged mountainous terrain area in Kelantan state, Malaysia using an atmospherically corrected Landsat 8 imagery captured on 22 April 2014 by Support Vector Machine (SVM) algorithm. The results showed that the SCS+C method reduces the topographic effect particularly in such a steep and forested terrain with classification accuracy improvement about 4% which was statistically significantly with the McNemar test value Z and P measured 6.42 and 0.0001 on the corrected image classification90.1%accuracy compared to the uncorrected image86.2%for the test area. Thus, the topographic correction is suggested to be the main step of the data pre-processing stage in mountainous terrain before SVM image classification

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