scholarly journals Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1

2020 ◽  
Vol 12 (19) ◽  
pp. 3263
Author(s):  
Dirk Hoekman ◽  
Boris Kooij ◽  
Marcela Quiñones ◽  
Sam Vellekoop ◽  
Ita Carolita ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Real Time ◽  
Forest Degradation ◽  
Canopy Gaps ◽  
Selective Logging ◽  
Optical Systems ◽  
Wide Area ◽  
Optical Data ◽  
Small Scale ◽  
Forest Change

The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge.

scholarly journals Tropical Forest Remote Sensing Services for the Democratic Republic of Congo inside the EU FP7 ReCover Project (Final Results 2000-2012)

2015 ◽  
Vol XL-7/W3 ◽  
pp. 397-402 ◽  
Author(s):  
J. Haarpaintner ◽  
D. de la Fuente Blanco ◽  
F. Enßle ◽  
P. Datta ◽  
A. Mazinga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Service Delivery ◽  
Tropical Forest ◽  
Method Development ◽  
Service Providers ◽  
Forest Degradation ◽  
Tropical Region ◽  
Forest Change ◽  
Alos Palsar ◽  
Forest Maps

‘ReCover’ was a 3-year EU-FP7 project (Nov. 2010 – Dec. 2013), aiming to develop and improve science based remote sensing services to support tropical forest management and activities to reduce emission from deforestation and forest degradation (REDD) in the tropical region (Häme et al., 2012). This is an overview of the final ReCover service delivery of 2000-2012 single-year optical (Landsat, ALOS AVNIR-2, RapidEye) and C-and L-band SAR (Envisat ASAR and ALOS Palsar, respectively) image mosaics, their derived forest/non-forest maps, a multi-sensor forest change map (2000-2010) and a biomass map (based on 2003-2009 ICESat GLAS) o he user of he De ocr ic Repub ic of Congo DRC), he Observatoir Satellitale des Forê s d’Afrique Cen r e OSFAC). The results are an improvement from a first iteration service delivery in 2012 after a critical review and validation process by both, the user and service providers, further method development and research, like a prior statistical data analysis considering temporal/seasonal variability, improved data pre-processing, and through the use of ground reference data collected in March 2013 for classification training. Validation with Kompsat-2 VHR data for the 2010 forest/non-forest maps revealed accuracies of 87% and 88% for optical and radar sensors, respectively.

Near-real-time identification of the drivers of deforestation in French Guiana

2021 ◽  
Author(s):  
Marie Ballere ◽  
Stephane Mermoz ◽  
Alexandre Bouvet ◽  
Thierry Koleck ◽  
Thuy Le Toan
Keyword(s):  
Real Time ◽  
Tropical Forests ◽  
Spatial Data ◽  
Cloud Cover ◽  
Gold Mining ◽  
French Guiana ◽  
Detection Methods ◽  
Optical Data ◽  
Small Scale ◽  
Sar Images

<p>Tropical forests account more than 50% of recorded terrestrial biodiversity and play an important role in carbon storage and the water cycle. The degradation of tropical forests presents an immediate danger for the global environment and biodiversity. Monitoring of deforestation and understanding its drivers are challenging tasks that are essential to measures of reduction of deforestation.</p><p>Many researches have been carried out on the detection of deforestation using remote sensing data, and there are several operational systems that work. Those systems are mostly based on optical data, but they show big delays in detections due to the persistent cloud cover in the tropics. Since 2014, Sentinel-1 provides SAR images every 6 to 12 days, insensitive to cloud cover. Deforestation detection methods based on SAR images have increased and start to be operational (Bouvet et al. 2018, Reiche et al. 2021). They allow for faster and more accurate mapping. For example, Ballere et al. 2021 shows that 80% of gold-mining-related deforestation in French Guiana is first detected by a SAR-based method, before the optical method, most often offset by several months.</p><p>However, the detection of disturbances in itself is not sufficient for measures to halt deforestation. Finer et al. 2017 defined a 5 steps protocol in order to help the near-real-time monitoring to be effective, the first step being the detection. Then comes the prioritization of data: this can be done by integrating spatial data such as protected areas or specific areas of interest. The third step is the identification of the drivers. This usually involves human-work.</p><p>We present here an automatic method for the identification of the drivers of deforestation in French Guiana (gold mining, urbanization, small-scale agriculture and forest exploitation), and show its results. It is based on geographical and morphological indicators, and makes it possible not to wait for another image after the detection step. The method has the potential to be integrated into an operational system for French Guiana.</p>

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

2010 ◽  
Vol 114 (5) ◽  
pp. 1117-1129 ◽  
Author(s):  
Eraldo A.T. Matricardi ◽  
David L. Skole ◽  
Marcos A. Pedlowski ◽  
Walter Chomentowski ◽  
Luis Claudio Fernandes
Keyword(s):  
Tropical Forest ◽  
Landsat Imagery ◽  
Forest Degradation ◽  
Selective Logging

scholarly journals Combining Sentinel-1 and Landsat 8 Does Not Improve Classification Accuracy of Tropical Selective Logging

2022 ◽  
Vol 14 (1) ◽  
pp. 179
Author(s):  
Matthew G. Hethcoat ◽  
João M. B. Carreiras ◽  
Robert G. Bryant ◽  
Shaun Quegan ◽  
David P. Edwards
Keyword(s):  
Tropical Forests ◽  
Classification Accuracy ◽  
Biological Diversity ◽  
Forest Degradation ◽  
Climate Impacts ◽  
Selective Logging ◽  
Wood Products ◽  
Optical Data ◽  
Landsat 8 ◽  
Sar Data

Tropical forests play a key role in the global carbon and hydrological cycles, maintaining biological diversity, slowing climate change, and supporting the global economy and local livelihoods. Yet, rapidly growing populations are driving continued degradation of tropical forests to supply wood products. The United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme to mitigate climate impacts and biodiversity losses through improved forest management. Consistent and reliable systems are still needed to monitor tropical forests at large scales, however, degradation has largely been left out of most REDD+ reporting given the lack of effective monitoring and countries mainly focus on deforestation. Recent advances in combining optical data and Synthetic Aperture Radar (SAR) data have shown promise for improved ability to monitor forest losses, but it remains unclear if similar improvements could be made in detecting and mapping forest degradation. We used detailed selective logging records from three lowland tropical forest regions in the Brazilian Amazon to test the effectiveness of combining Landsat 8 and Sentinel-1 for selective logging detection. We built Random Forest models to classify pixel-based differences in logged and unlogged regions to understand if combining optical and SAR improved the detection capabilities over optical data alone. We found that the classification accuracy of models utilizing optical data from Landsat 8 alone were slightly higher than models that combined Sentinel-1 and Landsat 8. In general, detection of selective logging was high with both optical only and optical-SAR combined models, but our results show that the optical data was dominating the predictive performance and adding SAR data introduced noise, lowering the detection of selective logging. While we have shown limited capabilities with C-band SAR, the anticipated opening of the ALOS-PALSAR archives and the anticipated launch of NISAR and BIOMASS in 2023 should stimulate research investigating similar methods to understand if longer wavelength SAR might improve classification of areas affected by selective logging when combined with optical data.

scholarly journals Selective logging reduces body size in omnivorous and frugivorous tropical forest birds

2021 ◽  
Vol 256 ◽  
pp. 109036
Author(s):  
Simone Messina ◽  
David Costantini ◽  
Suzanne Tomassi ◽  
Cindy C.P. Cosset ◽  
Suzan Benedick ◽  
...  
Keyword(s):  
Body Size ◽  
Tropical Forest ◽  
Selective Logging ◽  
Forest Birds

scholarly journals Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1723
Author(s):  
Félix Dubuisson ◽  
Miloud Rezkallah ◽  
Hussein Ibrahim ◽  
Ambrish Chandra
Keyword(s):  
Real Time ◽  
Power Management ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Euler Method ◽  
Small Scale ◽  
Diesel Generator ◽  
Bacterial Foraging Optimization ◽  
Time Model ◽  
Bacterial Foraging

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

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