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 Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

2020 ◽  
Vol 12 (4) ◽  
pp. 727 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Janik Deutscher ◽  
Carina Sobe ◽  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
...  
Keyword(s):  
Time Series ◽  
Patch Size ◽  
Forest Disturbance ◽  
Optical Data ◽  
Landsat 8 ◽  
Data Sets ◽  
Disturbed Forest ◽  
Optical Time ◽  
The Individual ◽  
Sentinel 2

Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7–17.5%, for Peru 80% as compared to 48.6–65.7%.

scholarly journals Characterizing over Four Decades of Forest Disturbance in Minnesota, USA

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 362 ◽  
Author(s):  
Jody Vogeler ◽  
Robert Slesak ◽  
Patrick Fekety ◽  
Michael Falkowski
Keyword(s):  
Time Series ◽  
Spatial Information ◽  
Forest Disturbance ◽  
Temporal Trends ◽  
Land Protection ◽  
Commission Errors ◽  
Protection Information ◽  
Disturbed Forest ◽  
Agent Of Change

Spatial information about disturbance driven patterns of forest structure and ages across landscapes provide a valuable resource for all land management efforts including cross-ownership collaborative forest treatments and restoration. While disturbance events in general are known to impact stand characteristics, the agent of change may also influence recovery and the supply of ecosystem services. Our study utilizes the full extent of the Landsat archive to identify the timing, extent, magnitude, and agent, of the most recent fast disturbance event for all forested lands within Minnesota, USA. To account for the differences in the Landsat sensors through time, specifically the coarser spatial, spectral, and radiometric resolutions of the early MSS sensors, we employed a two-step approach, first harmonizing spectral indices across the Landsat sensors, then applying a segmentation algorithm to fit temporal trends to the time series to identify abrupt forest disturbance events. We further incorporated spectral, topographic, and land protection information in our classification of the agent of change for all disturbance patches. After allowing two years for the time series to stabilize, we were able to identify the most recent fast disturbance events across Minnesota from 1974–2018 with a change versus no-change validation accuracy of 97.2% ± 1.9%, and higher omission (14.9% ± 9.3%) than commission errors (1.6% ± 1.9%) for the identification of change patches. Our classification of the agent of change exhibited an overall accuracy of 96.5% ± 1.9% with classes including non-disturbed forest, land conversion, fire, flooding, harvest, wind/weather, and other rare natural events. Individual class errors varied, but all class user and producer accuracies were above 78%. The unmatched nature of the Landsat archive for providing comparable forest attribute and change information across more than four decades highlights the value of the totality of the Landsat program to the larger geospatial, ecological research, and forest management communities.

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 OBJECT-ORIENTED ANALYSIS OF SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS FOR POST-EARTHQUAKE BUILDINGS CHANGE DETECTION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 139-144
Author(s):  
N. Khodaverdi zahraee ◽  
H. Rastiveis
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Feature Vector ◽  
Human Life ◽  
Textural Features ◽  
Image Objects ◽  
Input Feature ◽  
High Resolution Satellite Images ◽  
Before And After ◽  
Artificial Neural

Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre- and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre- and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.

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 Congo Basin forest loss dominated by increasing smallholder clearing

2018 ◽  
Vol 4 (11) ◽  
pp. eaat2993 ◽  
Author(s):  
Alexandra Tyukavina ◽  
Matthew C. Hansen ◽  
Peter Potapov ◽  
Diana Parker ◽  
Chima Okpa ◽  
...  
Keyword(s):  
Population Growth ◽  
Large Scale ◽  
Forest Cover ◽  
Forest Type ◽  
Forest Disturbance ◽  
Timber Harvesting ◽  
Selective Logging ◽  
Congo Basin ◽  
Small Scale ◽  
Forest Loss

A regional assessment of forest disturbance dynamics from 2000 to 2014 was performed for the Congo Basin countries using time-series satellite data. Area of forest loss was estimated and disaggregated by predisturbance forest type and direct disturbance driver. An estimated 84% of forest disturbance area in the region is due to small-scale, nonmechanized forest clearing for agriculture. Annual rates of small-scale clearing for agriculture in primary forests and woodlands doubled between 2000 and 2014, mirroring increasing population growth. Smallholder clearing in the Democratic Republic of the Congo alone accounted for nearly two-thirds of total forest loss in the basin. Selective logging is the second most significant disturbance driver, contributing roughly 10% of regional gross forest disturbance area and more than 60% of disturbance area in Gabon. Forest loss due to agro-industrial clearing along the Gulf of Guinea coast more than doubled in the last half of the study period. Maintaining natural forest cover in the Congo Basin into the future will be challenged by an expected fivefold population growth by 2100 and allocation of industrial timber harvesting and large-scale agricultural development inside remaining old-growth forests.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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