Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan

Kennedy, Robert E.; Yang, Zhiqiang; Cohen, Warren B.; Pfaff, Eric; Braaten, Justin; Nelson, Peter O.

doi:10.1016/j.rse.2011.09.024

Cited by 238 publications

(185 citation statements)

References 63 publications

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“…This permits the capture of most natural and managed disturbance events at regional and global scales (Wilson & Sader 2002;Tucker et al 2004;Koutsias et al 2009;Kennedy et al 2012). In future studies, the monitoring of postfire forest recovery can also be conducted based on these images, as well as with a combination of data from other sensors, including Quickbird, MODIS and SAR (Synthetic Aperture Radar), etc.…”

Section: Discussionmentioning

confidence: 99%

“…The application of optical remote sensing technology is the most common and traditional approach to extracting forest characteristics and monitoring forest change. Optical imageries, particularly the extensive Landsat records, are well suited to the detection of forest disturbances (Masek et al 2008;Kennedy et al 2012;Townshend et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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Mapping a burned forest area from Landsat TM data by multiple methods

Chen

Moriya

Sakai

et al. 2014

Geomatics, Natural Hazards and Risk

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Forest fire is one of the dominant disturbances in boreal forests. It is the primary process responsible for organizing the physical and biological attributes of the boreal biome, shaping landscape diversity and influencing biogeochemical cycles. The Greater Hinggan Mountain of China is rich in forest resources while suffers from a high incidence of forest fires simultaneously. In this study, focusing on the most serious forest fire in the history of P. R. China which occurred in this region, we made use of two Landsat-5 TM (Thematic Mapper) images, and proposed to map the overall burned area and burned forest area by multiple methods. During the mapping, the fire perimeter, as well as rivers, roads and urban areas were first extracted and masked visually, and then four indices of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Vegetation Fractional Cover and Disturbance Index were calculated. For each index, the optimal threshold for separating burned from unburned forest area was determined using their histograms. For comparison, threshold segmentation using single-band reflectance was performed, in addition to a Maximum Likelihood Classifier (MLC) based supervised classification of all features and forest area alone; their accuracies were also evaluated and analysed. Among all the methods compared here, mapping by EVI threshold segmentation proved to be optimal by the comparisons of overall accuracy (99.78%) and the kappa coefficient (0.9946). Finally, the calculated burned area and burned forest area were compared with the values from official statistics. Compared with the classical methods used to report official statistics on burned areas, the remote sensing-based mapping is more objective and efficient, less labour-and time-consuming, and more repeatable.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping a burned forest area from Landsat TM data by multiple methods

Chen

Moriya

Sakai

et al. 2014

Geomatics, Natural Hazards and Risk

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“…These approaches for multi-temporal change detection can be applied, for example, to monitor forest fires, insect-related mortality, or post-disturbance regrowth at an annual time scale (e.g., Coops et al 2010, Schroeder et al 2011, Kennedy et al 2012. However, the spatial resolution of Landsat is limited (28.5 m pixels), and thus these data fail to allow for reconstructing small-scale disturbance processes.…”

Section: Introductionmentioning

confidence: 99%

Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing

et al. 2015

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Abstract. Little is known about the gap pattern of primeval beech forests, since large-scale studies with continuous coverage are lacking. Analyses of forest structural patterns have benefitted from advances in remote sensing, especially with the launch of satellites providing data of submetric ground resolution. These developments can strongly advance our knowledge of natural forest dynamics and disturbance regimes. The Uholka-Shyrokyi Luh forest in the Ukrainian Carpathians, the largest remnant of primeval European beech (Fagus sylvatica L.) covering 102.8 km 2 , is an outstanding object to analyze the frequency distribution of gap sizes and to infer processes of forest dynamics. A stereo pair of very high-resolution WorldView-2 satellite images was used to characterize the forest's gap pattern. Canopy gaps were first digitized stereoscopically based on the image pair. In a second step, spectral properties in the red and yellow frequency bands were used to distinguish the stereoscopically mapped gap areas from non-gap areas, which enabled gap mapping over the entire study area. To validate the spectral gap mapping 338 randomly distributed samples were assigned manually to gap and non-gap areas based on the orthoimages. We found excellent agreement except for an overestimation of gaps close to clouds due to diffuse image areas. The frequency distribution of gap size revealed the forest to be structured by a small-scale mosaic of gaps mainly ,200 m 2 (98% of the gaps). Only a few large, stand-replacing events were detected, most probably caused by a wind storm in March 2007 and a heavy wet snow fall in October 2009. The small canopy gaps reflect fine-scale processes shaping forest structure, i.e., the death of single trees or groups of a few trees and is in line with the findings of the terrestrial forest inventory. We conclude that remote sensing approaches based on very high-resolution satellite images are highly useful to characterize even small-scale forest disturbance regimes and to study long-term gap dynamics. Stereo satellite images provide two viewing angles of the study area, thus allowing for a highly accurate mapping of canopy gaps in forests with a complex topography.

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“…Kennedy [22] built a trajectory-based change detection method for automated characterization of forest disturbance dynamics. Then LandTrendr [23] and TimeSync [24] were further developed to detect trends in forest disturbance and recovery using yearly Landsat time series and applied widely [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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Abstract:Forests are one of the most important components of the global biosphere and have critical influences on the Earth's ecological balance. Regularly updated forest cover information is necessary for various forest management applications as well as climate modeling studies. However, map products are not widely updated at continental or national scales because the current land cover products have overly coarse spatial resolution or insufficiently large training data sets. This study presents the results of forests distribution and variation information over China using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with the first layer of MODIS Land Cover Type product (MODIS LC-1). The NDVI time series histogram characteristic curves for forestland were estimated from MODIS LC-1 and MODIS NDVI time series data.

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Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan

Cited by 238 publications

References 63 publications

Mapping a burned forest area from Landsat TM data by multiple methods

Mapping a burned forest area from Landsat TM data by multiple methods

Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

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