Forest recovery trends derived from Landsat time series for North American boreal forests-Reference-Cited by-同舟云学术

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Forest recovery trends derived from Landsat time series for North American boreal forests

Published:2015-12-16 Issue:1 Volume:37 Page:138-149
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Pickell Paul D.¹,Hermosilla Txomin¹,Frazier Ryan J.¹,Coops Nicholas C.¹,Wulder Michael A.²

Affiliation:

1. Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada

2. Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, Victoria, British Columbia, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2015.1126375

Reference42 articles.

1. Forest Monitoring Using Landsat Time Series Data: A Review

2. Using the Landsat record to detect forest-cover changes during and after the collapse of the Soviet Union in the temperate zone of European Russia

3. Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne lidar data

4. The extent of the North American boreal zone

5. Evaluating the utility and seasonality of NDVI values for assessing post-disturbance recovery in a subalpine forest

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1. Post-fire vegetation dynamics of a temperate mixed forest: An assessment based on the variability of Landsat spectral indices;Trees, Forests and People;2024-09

2. Revealing post-megafire spectral and compositional recovery in the Siberian boreal forest using Landsat time series and regression-based unmixing approach;Remote Sensing of Environment;2024-09

3. Ecosystems Disturbance Recovery: What It Was or What It Could Have Been?;Geophysical Research Letters;2024-08-30

4. Unmixing-based forest recovery indicators for predicting long-term recovery success;Remote Sensing of Environment;2024-07

5. Post-fire forest recovery trajectory characterized by a modified LandTrendr recovery detection method: A case study of Pinus yunnanensis forests;Agricultural and Forest Meteorology;2024-07

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