Uncovering forest dynamics using historical forest inventory data and Landsat time series

Myroniuk, Viktor; Bell, David M.; Gregory, Matthew J.; Vasylyshyn, Roman; Bilous, Andrii

doi:10.1016/j.foreco.2022.120184

Cited by 11 publications

(4 citation statements)

References 89 publications

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“…Table 1 shows a detailed description of the fundamental datasets used in this study. Landsat imagery provides a historically deep and data-rich archive on a terrestrial surface and is a valuable resource for change detection [36,37]. This study employs Landsat imagery for three primary purposes.…”

Section: Basic Datamentioning

confidence: 99%

“…Secondly, we utilized sufficient and effective samples extracted from both Landsat image chips and high-resolution imagery. Compared with the conventional method of selecting samples based on HGFC data [36], we constructed a higher-quality sample database which contributed to improving the reliability and accuracy of secondary classification data. Thirdly, we enhanced the LandTrendr algorithm by utilizing a multispectral ensemble and multiple time windows to better detect subtle signals and identify different types of change signals [45,52].…”

Section: Advantages and Reliability Of The Proposed Approachmentioning

confidence: 99%

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Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery

Ren,

Wang

et al. 2023

Remote Sensing

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Understanding accurate and continuous forest dynamics is of key importance for forest protection and management in the Greater Khingan Mountains (GKM). There has been a lack of finely captured and long-term information on forest disturbance and recovery since the mega-fire of 1987 which may limit the scientific assessment of the GKM’s vegetation conditions. Therefore, we proposed a rapid and robust approach to track the dynamics of forest disturbance and recovery from 1987 to 2021 using Landsat time series, LandTrendr, and random forests (RF) models. Furthermore, we qualified the spatial characteristics of forest changes in terms of burn severity, topography, and distances from roads and settlements. Our results revealed that the integrated method of LandTrendr and RF is well adapted to track forest dynamics in the GKM, with an overall accuracy of 0.86. From 1987 to 2021, forests in the GKM showed a recovery trend with a net increase of more than 4.72 × 104 ha. Over 90% of disturbances occurred between 1987 and 2010 and over 75% of recovery occurred between 1987 and 1988. Mildly burned areas accounted for 51% of forest disturbance and severely burned areas contributed to 45% of forest recovery. Forest changes tended to occur in zones with elevations of 400–650 m, slopes of less than 9°, and within 6 km of roads and 24 km of settlements. Temporal trends of forest disturbance and recovery were mainly explained by the implementation timelines of major forestry policies. Our results provide high-resolution and time-series information on forest disturbance and recovery in the GKM which could support scientific decisions on forest management and sustainable utilization.

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Section: Basic Datamentioning

confidence: 99%

Section: Advantages and Reliability Of The Proposed Approachmentioning

confidence: 99%

Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery

Ren,

Wang

et al. 2023

Remote Sensing

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“…J. White et al (2016), V. Myroniuk et al (2022) in their works, they noted that the use of satellite data in forestry is quite promising since such data cover large areas and can be widely used in forest inventory.…”

Section: Introductionmentioning

confidence: 99%

UAV data collection parameters impact on accuracy of Scots pine stand mensuration

Zadorozhniuk¹

2023

Ukr. j. for. wood sci.

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A wide range of UAV systems used for forest research requires unified approaches to data collection. The research aims to determine the optimal parameters for UAV data collection to obtain accurate information about stands, considering the cost of resources for its collection. The process of collecting remote sensing data consisted of nine combinations divided into three levels of overlap and three levels of spatial resolution (survey altitude) and changing the degree of filtering of a dense point cloud during image processing. Individual tree detectingin the stand was performed using the R programming language and the ForestTools package. The results of the assessment of the dependence of the radius of tree crowns on their height were used to set the parameters of the variable filter function for finding local maxima for Scots pine stands. Errors in the identification of treetops were estimated using the F-score. The identified heights were compared with the field data of the ground survey. The proportion of classified digital elevation model DEM in the dense point cloud was reduced from the total area of the test site using images of 4.1 cm/pix spatial resolution (150 m survey altitude). The study presents the results of assessing the impact of spatial resolution of optical images collected from UAVs and their overlap on the results of measurements of stands parameters. It is determined that a photogrammetric survey with input images with a longitudinal overlap of less than 90% is not appropriate for the study of forest areas due to the impossibility of aligning all images. The results of the assessment of tree accounting in the stand showed that it is most appropriate to use images with a spatial resolution of up to 3.3 cm/pix (120 m survey altitude), otherwise, the proportion of missed treetops increases. Reducing the spatial resolution of remote sensing data leads to an increase in errors in determining the height of individual trees, and the average heights of the experimental plots had the same trend. Given the combination of the assessed factors, it is not recommended to use images with a spatial resolution of more than 3.3 cm/pix for forestry research due to increased errors in the individual tree detection and tree height determination. The results obtained can be used to select data collection parameters for research on Scots pine stands to assess their growing stock and phytomass

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Section: Introductionmentioning

confidence: 99%

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2023

Ukr. j. for. wood sci.

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Доктор сільськогосподарських наук, професор, Національний університет біоресурсів і природокористування України, Україна Доктор сільськогосподарських наук, доцент, Національний університет біоресурсів і природокористування України, Україна Кандидат сільськогосподарських наук, доцент, Національний університет біоресурсів і природокористування України, Україна Доктор сільськогосподарських наук, професор, Національний університет біоресурсів і природокористування України, Україна Доктор сільськогосподарських наук, професор, Національний університет біоресурсів і природокористування України, Україна Доктор сільськогосподарських наук, професор, Національний університет біоресурсів і природокористування України, Україна Доктор сільськогосподарських наук, професор, Національний університет біоресурсів і природокористування України, Україна Кандидат технічних наук, доцент, Чернігівський національний технологічний університет, Україна Доктор технічних наук, професор, Національний університет біоресурсів і природокористування України, Україна Доктор технічних наук, професор, Національний університет біоресурсів і природокористування України, Україна Доктор біологічних наук, доцент, Національний університет біоресурсів і природокористування України, Україна Доктор біологічних наук, професор, Національний лісотехнічний університет України, Україна Кандидат сільськогосподарських наук, Український науково-дослідний інститут сільськогосподарської радіології, Україна Доктор біологічних наук, професор, Інститут агроекології і природокористування НААН, Україна Кандидат сільськогосподарських наук, доцент, Національний університет біоресурсів і природокористування України, Україна Доктор сільськогосподарських наук, професор, Національний університет біоресурсів і природокористування України, Україна

show abstract

Uncovering forest dynamics using historical forest inventory data and Landsat time series

Cited by 11 publications

References 89 publications

Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery

Continuous Tracking of Forest Disturbance and Recovery in the Greater Khingan Mountains from Annual Landsat Imagery

UAV data collection parameters impact on accuracy of Scots pine stand mensuration

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