Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery

2017 ◽  
Vol 196 ◽  
pp. 193-204 ◽  
Author(s):  
David Gudex-Cross ◽  
Jennifer Pontius ◽  
Alison Adams
Keyword(s):  
Forest Cover ◽  
Landsat Imagery ◽  
Spectral Unmixing ◽  
Object Based ◽  
Multi Temporal ◽  
Forest Cover Mapping

scholarly journals Change assessment of forest cover in Ghodaghodi lake area in Kailali district of Nepal

1970 ◽  
Vol 19 (2) ◽  
pp. 15-19 ◽  
Author(s):  
S Khanal
Keyword(s):  
Forest Cover ◽  
Landsat Imagery ◽  
Forest Degradation ◽  
Lake Area ◽  
Forest Cover Change ◽  
Comparison Approach ◽  
Multi Temporal ◽  
One Year

Ghodaghodi Lake in Far-West Nepal has been listed as a Ramsar Site due to its significance as a habitat for several endangered species of flora and fauna. The wetland and its surrounding area is facing deforestation, forest degradation and encroachment. In this case study, unsupervised and finally supervised classification of multi-temporal Landsat imagery covering the wetland area was applied. A post-classification comparison approach was used to derive forest cover change maps. The results depicted the loss of forest cover over a thirty- one year period, in three time slices. The highest rate of loss was observed in the 1990 to1999 time slice. Keywords: Change detection; forest cover; Ghodaghodi lake; Landsat DOI: 10.3126/banko.v19i2.2980 Banko Janakari, Vol. 19, No.2 2009 pp.15-19

Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data

2017 ◽  
Vol 43 (5) ◽  
pp. 432-450 ◽  
Author(s):  
Sahel Mahdavi ◽  
Bahram Salehi ◽  
Meisam Amani ◽  
Jean Elizabeth Granger ◽  
Brian Brisco ◽  
...  
Keyword(s):  
Newfoundland And Labrador ◽  
Object Based ◽  
Multi Temporal

scholarly journals Forest Type and Tree Species Classification of Nemoral Forests With Sentinel-1 and 2 Time Series Data

2021 ◽  
Author(s):  
Kristian Skau Bjerreskov ◽  
Thomas Nord-Larsen ◽  
Rasmus Fensholt
Keyword(s):  
Tree Species ◽  
Forest Cover ◽  
Forest Type ◽  
Error Rates ◽  
Sensor Data ◽  
Series Data ◽  
Forest Types ◽  
Multi Temporal ◽  
Species Groups

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-censor data will have a specific strength in further classification of nemoral forest landscapes owing to the distinct seasonal patterns of the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and machine learning (random forest) trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90 %, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34-74%. Species groups with coniferous species were the least confused whereas the broadleaf groups, especially Oak, had higher error rates. The results are applied in Danish National accounting of greenhouse gas emissions from forests, resource assessment and assessment of forest biodiversity potentials.

scholarly journals Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset

2020 ◽  
Vol 12 (15) ◽  
pp. 2475 ◽  
Author(s):  
Daniel S. W. Katz ◽  
Stuart A. Batterman ◽  
Shannon J. Brines
Keyword(s):  
Classification Accuracy ◽  
World View ◽  
Urban Trees ◽  
Aerial Images ◽  
Aerial Image ◽  
Urban Tree ◽  
Object Based ◽  
Street Tree ◽  
Multi Temporal

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.

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