scholarly journals DETECTION OF FOREST DISTURBANCE USING TANDEM-X POL-INSAR COHERENCE

2018 ◽  
Vol XLII-5 ◽  
pp. 295-297 ◽  
Author(s):  
M. Musthafa ◽  
G. Singh ◽  
U. Khati
Keyword(s):  
Time Series ◽  
Forest Structure ◽  
Forest Disturbance ◽  
High Reliability ◽  
Selective Logging ◽  
Forest Change ◽  
Teak Plantation

<p><strong>Abstract.</strong> This current study shows the potential of TanDEM-X pol-InSAR coherence to identify progressive selective logging of Teak plantation in Uttarakhand, India. Pol-InSAR data were acquired over four months with 11 days interval with perpendicular baseline varying from 111 to 689<span class="thinspace"></span>m. Progressive selective logging of mature teak plantation from January to February was analyzed using time-series pol-InSAR coherences. The results shows the baseline selection critical for forest change studies. TanDEM-X derived pol-InSAR coherence would enable us to detect the change in forest structure with high reliability.</p>

scholarly journals Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes

2018 ◽  
Vol 15 (11) ◽  
pp. 3377-3390 ◽  
Author(s):  
Victoria Meyer ◽  
Sassan Saatchi ◽  
David B. Clark ◽  
Michael Keller ◽  
Grégoire Vincent ◽  
...  
Keyword(s):  
Forest Structure ◽  
Aboveground Biomass ◽  
Tree Mortality ◽  
Forest Disturbance ◽  
Basal Area ◽  
Selective Logging ◽  
Tree Canopy ◽  
Crown Area ◽  
Large Trees ◽  
Canopy Area

Abstract. Large tropical trees store significant amounts of carbon in woody components and their distribution plays an important role in forest carbon stocks and dynamics. Here, we explore the properties of a new lidar-derived index, the large tree canopy area (LCA) defined as the area occupied by canopy above a reference height. We hypothesize that this simple measure of forest structure representing the crown area of large canopy trees could consistently explain the landscape variations in forest volume and aboveground biomass (AGB) across a range of climate and edaphic conditions. To test this hypothesis, we assembled a unique dataset of high-resolution airborne light detection and ranging (lidar) and ground inventory data in nine undisturbed old-growth Neotropical forests, of which four had plots large enough (1 ha) to calibrate our model. We found that the LCA for trees greater than 27 m (∼ 25–30 m) in height and at least 100 m2 crown size in a unit area (1 ha), explains more than 75 % of total forest volume variations, irrespective of the forest biogeographic conditions. When weighted by average wood density of the stand, LCA can be used as an unbiased estimator of AGB across sites (R2 = 0.78, RMSE = 46.02 Mg ha−1, bias = −0.63 Mg ha−1). Unlike other lidar-derived metrics with complex nonlinear relations to biomass, the relationship between LCA and AGB is linear and remains unique across forest types. A comparison with tree inventories across the study sites indicates that LCA correlates best with the crown area (or basal area) of trees with diameter greater than 50 cm. The spatial invariance of the LCA–AGB relationship across the Neotropics suggests a remarkable regularity of forest structure across the landscape and a new technique for systematic monitoring of large trees for their contribution to AGB and changes associated with selective logging, tree mortality and other types of tropical forest disturbance and dynamics.

scholarly journals Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series

2021 ◽  
Vol 13 (4) ◽  
pp. 740
Author(s):  
Oleg Antropov ◽  
Yrjö Rauste ◽  
Jaan Praks ◽  
Frank Martin Seifert ◽  
Tuomas Häme
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Forest Disturbance ◽  
Selective Logging ◽  
Congo Basin ◽  
Optical Data ◽  
Textural Features ◽  
Before And After ◽  
Disturbed Forest ◽  
Log Ratio

Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.

scholarly journals Using Landsat time series imagery to detect forest disturbance in selectively logged tropical forests in Myanmar

2017 ◽  
Vol 47 (3) ◽  
pp. 289-296 ◽  
Author(s):  
Katsuto Shimizu ◽  
Raul Ponce-Hernandez ◽  
Oumer S. Ahmed ◽  
Tetsuji Ota ◽  
Zar Chi Win ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Disturbance ◽  
Landsat Imagery ◽  
Forest Degradation ◽  
Selective Logging ◽  
Mitigation Strategies ◽  
Forest Disturbances ◽  
The Tropics ◽  
Degradation Indicator ◽  
Disturbance Detection

Detecting forest disturbances is an important task in formulating mitigation strategies for deforestation and forest degradation in the tropics. Our study investigated the use of Landsat time series imagery combined with a trajectory-based analysis for detecting forest disturbances resulting exclusively from selective logging in Myanmar. Selective logging was the only forest disturbance and degradation indicator used in this study as a causative force, and the results showed that the overall accuracy for forest disturbance detection based on selective logging was 83.0% in the study area. The areas affected by selective logging and other factors accounted for 4.7% and 5.4%, respectively, of the study area from 2000 to 2014. The detected disturbance areas were underestimated according to error assessments; however, a significant correlation between areas of disturbance and numbers of harvested trees during the logging year was observed, indicating the utility of a trajectory-based, annual Landsat imagery time series analysis for selective logging detection in the tropics. A major constraint of this study was the lack of available data for disturbances other than selective logging. Further studies should focus on identifying other types of disturbances and their impacts on future forest conditions.

scholarly journals Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi–Source Land Cover Products

2020 ◽  
Vol 12 (1) ◽  
pp. 129 ◽  
Author(s):  
Yang Hu ◽  
Yunfeng Hu
Keyword(s):  
Time Series ◽  
Forest Disturbance ◽  
Far East ◽  
Google Earth ◽  
Forest Recovery ◽  
Dynamic Monitoring ◽  
Forest Change ◽  
Primorsky Krai ◽  
Forest Classification ◽  
Classification Probability

The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time–series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 106 ha, of which the cumulative forest disturbance area reached 1.12 × 106 ha, and the cumulative forest recovery area reached 0.55 × 106 ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large–scale and high–precision dynamic monitoring of the forests in Russia’s Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested.

scholarly journals Canopy Area of Large Trees Explains Aboveground Biomass Variations across Nine Neotropical Forest Landscapes

2018 ◽  
Author(s):  
Victoria Meyer ◽  
Sassan Saatchi ◽  
David B. Clark ◽  
Michael Keller ◽  
Grégoire Vincent ◽  
...  
Keyword(s):  
Forest Structure ◽  
Aboveground Biomass ◽  
Tree Mortality ◽  
Forest Disturbance ◽  
Basal Area ◽  
Selective Logging ◽  
Tree Canopy ◽  
Crown Area ◽  
Large Trees ◽  
Canopy Area

Abstract. Large tropical trees store significant amounts of carbon in woody components and their distribution plays an important role in forest carbon stocks and dynamics. Here, we explore the properties of a new Lidar derived index, large tree canopy area (LCA) defined as the area occupied by canopy above a reference height. We hypothesize that this simple measure of forest structure representing the crown area of large canopy trees could consistently explain the landscape variations of forest volume and aboveground biomass (AGB) across a range of climate and edaphic conditions. To test this hypothesis, we assembled a unique dataset of high-resolution airborne Light Detection and Ranging (Lidar) and ground inventory data in nine undisturbed old growth Neotropical forests. We found that the LCA for trees greater than 27 m (~ 25–30 m) in height and at least 100 m2 crown size in a unit area (1 ha), explains more than 75 % of total forest volume variations, irrespective of the forest biogeographic conditions. When weighted by average wood density of the stand, LCA can be used as an unbiased estimator of AGB across all sites (R2 = 0.78, RMSE = 46.02 Mg ha−1, bias = 0.76 Mg ha−1). Unlike other Lidar derived metrics with complex nonlinear relations to biomass, the relationship between LCA and AGB is linear. A comparison with tree inventories across the study sites indicates that LCA correlates best with the crown area (or basal area) of trees with diameter > 50 cm. The spatial invariance of the LCA–AGB relationship across the Neotropics suggests a remarkable regularity of forest structure across the landscape and a new technique for systematic monitoring of large trees for their contribution to AGB and changes associated with selective logging, tree mortality, and other types of forest disturbance and dynamics.

scholarly journals Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

2021 ◽  
Vol 13 (9) ◽  
pp. 1743
Author(s):  
Daniel Paluba ◽  
Josef Laštovička ◽  
Antonios Mouratidis ◽  
Přemysl Štych
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Type ◽  
Incidence Angle ◽  
Correction Method ◽  
Google Earth ◽  
Forest Change ◽  
Corine Land Cover ◽  
Angle Correction ◽  
Wide Range

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: a case study in northern Ontario

2016 ◽  
Vol 8 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Oumer S. Ahmed ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
Txomin Hermosilla ◽  
Nicholas C. Coops ◽  
...  
Keyword(s):  
Time Series ◽  
Boreal Forest ◽  
Forest Change ◽  
Northern Ontario

scholarly journals STABILIZATION OF SENTINEL-1 SAR TIME-SERIES USING CLIMATE AND FOREST STRUCTURE DATA FOR EARLY TROPICAL DEFORESTATION DETECTION

2020 ◽  
Vol V-3-2020 ◽  
pp. 89-96
Author(s):  
J. Doblas ◽  
A. Carneiro ◽  
Y. Shimabukuro ◽  
S. Sant’Anna ◽  
L. Aragão ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Structure ◽  
Detection Algorithm ◽  
Structure Information ◽  
Detection Algorithms ◽  
Alternative State ◽  
Level Data ◽  
Relevant Variables ◽  
Long Time ◽  
Omission Errors

Abstract. In this study we analyse the factors of variability of Sentinel-1 C-band radar backscattering over tropical rainforests, and propose a method to reduce the effects of this variability on deforestation detection algorithms. To do so, we developed a random forest regression model that relates Sentinel-1 gamma nought values with local climatological data and forest structure information. The model was trained using long time-series of 26 relevant variables, sampled over 6 undisturbed tropical forests areas. The resulting model explained 71.64% and 73.28% of the SAR signal variability for VV and VH polarizations, respectively. Once the best model for every polarization was selected, it was used to stabilize extracted pixel-level data of forested and non-deforested areas, which resulted on a 10 to 14% reduction of time-series variability, in terms of standard deviation. Then a statistically robust deforestation detection algorithm was applied to the stabilized time-series. The results show that the proposed method reduced the rate of false positives on both polarizations, especially on VV (from 21% to 2%, α=0.01). Meanwhile, the omission errors increased on both polarizations (from 27% to 37% in VV and from 27% to 33% on VV, α=0.01). The proposed method yielded slightly better results when compared with an alternative state-of-the-art approach (spatial normalization).

scholarly journals An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States

2018 ◽  
Author(s):  
Ian Housman ◽  
Robert Chastain ◽  
Mark Finco
Keyword(s):  
United States ◽  
Remote Sensing ◽  
New England ◽  
Forest Disturbance ◽  
Forest Health ◽  
Rio Grande ◽  
National Forest ◽  
Forest Change ◽  
Southern New England ◽  
Disease Survey

The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 77.64% to 93.51% (kappa 0.09&ndash;0.59) in the Southern New England study area and 59.57% to 79.57% (kappa 0.09&ndash;0.45) in the Rio Grande National Forest study area. In general, ORS products met or exceeded the overall accuracy and kappa of IDS and RTFD products. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data.

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