scholarly journals Value Chain of Charcoal Production and Implications for Forest Degradation: Case Study of Bié Province, Angola

Environments ◽  
2018 ◽  
Vol 5 (11) ◽  
pp. 113 ◽  
Author(s):  
Vasco Chiteculo ◽  
Bohdan Lojka ◽  
Peter Surový ◽  
Vladimir Verner ◽  
Dimitrios Panagiotidis ◽  
...  
Keyword(s):  
Value Chain ◽  
Forest Cover ◽  
Forest Degradation ◽  
Primary Data ◽  
Natural Forests ◽  
Forest Cover Change ◽  
Charcoal Production ◽  
Order Of Magnitude ◽  
Alternative Sources ◽  
Urban Consumption

Forest degradation and forest loss threaten the survival of many species and reduce the ability of forests to provide vital services. Clearing for agriculture in Angola is an important driver of forest degradation and deforestation. Charcoal production for urban consumption as a driver of forest degradation has had alarming impacts on natural forests, as well as on the social and economic livelihood of the rural population. The charcoal impact on forest cover change is in the same order of magnitude as deforestation caused by agricultural expansion. However, there is a need to monitor the linkage between charcoal production and forest degradation. The aim of this paper is to investigate the sequence of the charcoal value chain as a systematic key to identify policies to reduce forest degradation in the province of Bié. It is a detailed study of the charcoal value chain that does not stop on the production and the consumption side. The primary data of this study came from 330 respondents obtained through different methods (semi-structured questionnaire survey and market observation conducted in June to September 2013–2014). A logistic regression (logit) model in IBM SPSS Statistics 24 (IBM Corp, Armonk, NY, USA) was used to analyze the factors influencing the decision of the households to use charcoal for domestic purposes. The finding indicates that 21 to 27 thousand hectares were degraded due to charcoal production. By describing the chain of charcoal, it was possible to access the driving factors for charcoal production and to obtain the first-time overview flow of charcoal from producers to consumers in Bié province. The demand for charcoal in this province is more likely to remain strong if government policies do not aim to employ alternative sources of domestic energy.

scholarly journals Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1016
Author(s):  
Mohammad Emran Hasan ◽  
Biswajit Nath ◽  
A.H.M. Raihan Sarker ◽  
Zhihua Wang ◽  
Li Zhang ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Forest Degradation ◽  
Primary Data ◽  
Forest Cover Change ◽  
The Past ◽  
The Future ◽  
Reserve Forest ◽  
Health Quality

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.

Opportunities for Reaping the Benefits of REDD+ in Sikkim Himalaya for Conservation and Enhancement of Carbon Stock

2017 ◽  
Vol 40 (3) ◽  
pp. 209-215
Author(s):  
Mohommad Shahid ◽  
◽  
L.K. Rai ◽  
Keyword(s):  
Climate Change ◽  
Forest Cover ◽  
Carbon Sink ◽  
Forest Degradation ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Carbon Sequestration Potential ◽  
Sequestration Potential ◽  
Article 5

Paris Agreement recognized the role of forests as carbon sink for mitigation of climate change, under Article 5 as REDD+, i.e., reducing emissions from deforestation and forest degradation and role of conservation, sustainable management of forests and enhancement of forest carbon stocks. Forest cover change analysis was done between two time periods 2005 and 2015 to assess the forest degradation. Carbon sequestration potential of the forests of Sikkim for mitigating climate change is also estimated. Benefits of implementing of REDD+ in Sikkim involving local communities as stakeholder to conserve and sustainably manage the forest is assessed. Gaps and challenges faced by the stakeholder in implementing REDD+ at project level are also highlighted.

Forest cover change patterns in Myanmar (Burma) 1990–2000

2005 ◽  
Vol 32 (4) ◽  
pp. 356-364 ◽  
Author(s):  
PETER LEIMGRUBER ◽  
DANIEL S. KELLY ◽  
MARC K. STEININGER ◽  
JAKE BRUNNER ◽  
THOMAS MÜLLER ◽  
...  
Keyword(s):  
Forest Cover ◽  
Mangrove Forests ◽  
Forest Cover Change ◽  
Charcoal Production ◽  
Important Species ◽  
Agricultural Conversion ◽  
Deforestation Rates ◽  
Commercial Logging ◽  
Closed Canopy ◽  
Landsat Satellite

Myanmar is one of the most forested countries in mainland South-east Asia. These forests support a large number of important species and endemics and have great value for global efforts in biodiversity conservation. Landsat satellite imagery from the 1990s and 2000s was used to develop a countrywide forest map and estimate deforestation. The country has retained much of its forest cover, but forests have declined by 0.3% annually. Deforestation varied considerably among administrative units, with central and more populated states and divisions showing the highest losses. Ten deforestation hotspots had annual deforestation rates well above the countrywide average. Major reasons for forest losses in these hotspots stemmed from increased agricultural conversion, fuelwood consumption, charcoal production, commercial logging and plantation development. While Myanmar continues to be a stronghold for closed canopy forests, several areas have been experiencing serious deforestation. Most notable are the mangrove forests in the Ayeyarwady delta region and the remaining dry forests at the northern edge of the central dry zone.

Estimating carbon losses from disturbances in tropical moist forests (deforestation and forest degradation) since 2011

2020 ◽  
Author(s):  
Frederic Achard ◽  
Christelle Vancutsem ◽  
Valerio Avitabile ◽  
Andreas Langner
Keyword(s):  
Forest Cover ◽  
Moving Average ◽  
Forest Degradation ◽  
Tree Cover ◽  
Accurate Information ◽  
Natural Forests ◽  
Carbon Loss ◽  
Average Value ◽  
Large Trees ◽  
Carbon Losses

<p>The need for accurate information to characterize the evolution of forest cover at the tropical scale is widely recognized, particularly to assess carbon losses from processes of disturbances such as deforestation and forest degradation<sup>1</sup>. In fact, the contribution of degradation is a key element for REDD+ activities and is presently mostly ignored in national reporting due to the lack of reliable information at such scale.<br>Recently Vancutsem et al.<sup>2</sup> produced a dataset at 30m resolution which delineates the tropical moist forest (TMF) cover changes from 1990 to 2019. The use of the Landsat historical time-series at high temporal and spatial resolution allows accurate monitoring of deforestation and degradation, from which the carbon losses from disturbances in TMFs can be estimated. A degradation event is defined here as temporary absence of tree cover (visible within a Landsat pixel during a maximum of three years duration) and includes impacts of fires and logging activities.<br>We quantify the annual losses in above-ground carbon stock associated to degradation and deforestation in TMF over the period 2011-2019 by combining the annual disturbances in forest cover derived from the Landsat archive the pan-tropical map of aboveground live woody biomass density (AGB) from Santoro et al.<sup>3</sup> at 100 m. To reduce the local variability within the estimation of AGB values, we apply a moving average filter under the TMF cover for the year 2010. <br>The carbon loss due to degradation is accounted as full carbon loss within a pixel (like a deforestation). The reason is that logging activities usually remove large trees with higher biomass densities than the average value of the disturbed pixel indicated by the pan-tropical maps. To avoid double counting of carbon removal, deforestation happening after degradation is not accounted as carbon loss.<br>Our results are compared with estimates of previous studies that cover different periods and forest domains: (i) Tyukavina et al.<sup>4</sup> provide estimates of carbon loss from deforestation for the period 2000-2012 for all forests (evergreen and deciduous) discriminating natural forests from managed forests, and (ii) Baccini et al.<sup>5 </sup>provide estimates of carbon loss from deforestation and degradation for the period 2003-2014 for both evergreen and deciduous forests.</p><p>In a further step, we will analyze the sensitivity of the results to the input AGB values by applying the same approach to other AGB maps (e.g. Baccini et al. 2012<sup>6</sup>).<br>Finally we intend to use Sentinel-2 data (10 m) for monitoring the location and extent of logging activities and burnt areas and further improve the estimates of carbon losses from forest degradation. </p><p>1. Achard F, House JI 2015 doi 10.1088/1748-9326/10/10/101002<br>2. Vancutsem C. et al. 2019 Submitted to Nat. Geoscience<br>3. Santoro M et al. 2018 doi 10.1594/PANGAEA.894711<br>4. Tuykavina A et al 2018 http://iopscience.iop.org/1748-9326/10/7/074002<br>5. Baccini A et al. 2017 doi 10.1126/science.aam5962<br>6. Baccini A et al. 2012 doi 10.1038/nclimate1354</p>

scholarly journals Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar

2017 ◽  
Author(s):  
Ghislain Vieilledent ◽  
Clovis Grinand ◽  
Fety A. Rakotomalala ◽  
Rija Ranaivosoa ◽  
Jean-Roger Rakotoarijaona ◽  
...  
Keyword(s):  
Forest Fragmentation ◽  
Forest Cover ◽  
National Level ◽  
Tree Cover ◽  
National Forest ◽  
Area Network ◽  
Natural Forests ◽  
Forest Cover Change ◽  
Current Conservation ◽  
Forest Maps

AbstractThe island of Madagascar has a unique biodiversity, mainly located in the tropical forests of the island. This biodiversity is highly threatened by anthropogenic deforestation. Existing historical forest maps at national level are scattered and have substantial gaps which prevent an exhaustive assessment of long-term deforestation trends in Madagascar. In this study, we combined historical national forest cover maps (covering the period 1953-2000) with a recent global annual tree cover loss dataset (2001-2014) to look at six decades of deforestation and forest fragmentation in Madagascar (from 1953 to 2014). We produced new forest cover maps at 30 m resolution for the year 1990 and annually from 2000 to 2014 over the full territory of Madagascar. We estimated that Madagascar has lost 44% of its natural forest cover over the period 1953-2014 (including 37% over the period 1973-2014). Natural forests cover 8.9 Mha in 2014 (15% of the national territory) and include 4.4 Mha (50%) of moist forests, 2.6 Mha (29%) of dry forests, 1.7 Mha of spiny forests (19%) and 177,000 ha (2%) of mangroves. Since 2005, the annual deforestation rate has progressively increased in Madagascar to reach 99,000 ha/yr during 2010-2014 (corresponding to a rate of 1.1%/yr). Around half of the forest (46%) is now located at less than 100 m from the forest edge. Our approach could be replicated to other developing countries with tropical forest. Accurate forest cover change maps can be used to assess the effectiveness of past and current conservation programs and implement new strategies for the future. In particular, forest maps and estimates can be used in the REDD+ framework which aims at “Reducing Emissions from Deforestation and forest Degradation” and for optimizing the current protected area network.

scholarly journals Appraisal of Deforestation and Forest Degradation in District Swat, Pakistan

2019 ◽  
Vol 10 (3) ◽  
pp. 7-15
Author(s):  
Syed Muhammad Owais ◽  
Saima Siddiqui
Keyword(s):  
Forest Cover ◽  
Forest Degradation ◽  
Temporal Analysis ◽  
Primary Data ◽  
Landsat 8 ◽  
Forest Reserves ◽  
Deforestation Rate ◽  
Geospatial Tools ◽  
Analysis System ◽  
The Right

14 September, 2019 Accepted: 07 October, 2019Abstract: Deforestation and forest degradation are not only a problem of north western mountainous region of Pakistanbut it is one of the main global environmental issues. To find out deforestation rate and its extent in Swat, KhyberPakhtunkhwa, Landsat 5 (October 2, 2011) and Landsat 8 OLI (October 15, 2016) data were processed in CarnegieLandsat Analysis System (CLASlite v3.3). Primary data related to deforestation in Swat were also obtained from localpeople through a structured questionnaire. Primary data were analyzed in Statistical Package for the Social Sciences(SPSS). Changes in land cover can be clearly identified during image analysis. The temporal analysis of forest coverbetween 2011 and 2016 showed a significant change in forest cover. About 11 km² area is converted from forest tobarren land, while approximately 9,985 km² area of forest cover was degraded. The perceived causes of deforestation inthe study area are unsustainable use and mismanagement of forest resources, population growth, plantation ofeucalyptus and lack of basic facilities and awareness. However, community ignorance is the main factor responsible fordeforestation and forest degradation. One of the major consequences of deforestation can be related to the totaldisappearance of Charchur waterfall in Talang Kota lower Swat in September 2016. Therefore, it is the right time tomove toward sustainable management, detection and monitoring of national forest reserves by using geospatial tools,and by the involvement of local communities to participate in decision making about the conservation of forestresources.

scholarly journals Appraisal of Deforestation and Forest Degradation in District Swat, Pakistan

2019 ◽  
Vol 10 (3) ◽  
pp. 7-15
Author(s):  
Syed Muhammad Owais ◽  
Saima Siddiqui
Keyword(s):  
Forest Cover ◽  
Forest Degradation ◽  
Temporal Analysis ◽  
Primary Data ◽  
Landsat 8 ◽  
Forest Reserves ◽  
Deforestation Rate ◽  
Geospatial Tools ◽  
Analysis System ◽  
The Right

14 September, 2019 Accepted: 07 October, 2019Abstract: Deforestation and forest degradation are not only a problem of north western mountainous region of Pakistanbut it is one of the main global environmental issues. To find out deforestation rate and its extent in Swat, KhyberPakhtunkhwa, Landsat 5 (October 2, 2011) and Landsat 8 OLI (October 15, 2016) data were processed in CarnegieLandsat Analysis System (CLASlite v3.3). Primary data related to deforestation in Swat were also obtained from localpeople through a structured questionnaire. Primary data were analyzed in Statistical Package for the Social Sciences(SPSS). Changes in land cover can be clearly identified during image analysis. The temporal analysis of forest coverbetween 2011 and 2016 showed a significant change in forest cover. About 11 km² area is converted from forest tobarren land, while approximately 9,985 km² area of forest cover was degraded. The perceived causes of deforestation inthe study area are unsustainable use and mismanagement of forest resources, population growth, plantation ofeucalyptus and lack of basic facilities and awareness. However, community ignorance is the main factor responsible fordeforestation and forest degradation. One of the major consequences of deforestation can be related to the totaldisappearance of Charchur waterfall in Talang Kota lower Swat in September 2016. Therefore, it is the right time tomove toward sustainable management, detection and monitoring of national forest reserves by using geospatial tools,and by the involvement of local communities to participate in decision making about the conservation of forestresources.

scholarly journals Forest Cover Change and the Effectiveness of Protected Areas in the Himalaya since 1998

2020 ◽  
Vol 12 (15) ◽  
pp. 6123
Author(s):  
Changjun Gu ◽  
Pei Zhao ◽  
Qiong Chen ◽  
Shicheng Li ◽  
Lanhui Li ◽  
...  
Keyword(s):  
Protected Areas ◽  
Forest Cover ◽  
Forest Degradation ◽  
Google Earth ◽  
Forest Cover Change ◽  
Natural Conditions ◽  
Positive Role ◽  
Forest Cover Changes ◽  
Matched Samples ◽  
The Himalaya

Himalaya, a global biodiversity hotspot, has undergone considerable forest cover fluctuation in recent decades, and numerous protected areas (PAs) have been established to prohibit forest degradation there. However, the spatiotemporal characteristics of this forest cover change across the whole region are still unknown, as are the effectiveness of its PAs. Therefore, here, we first mapped the forest cover of Himalaya in 1998, 2008, and 2018 with high accuracy (>90%) using a random forest (RF) algorithm based on Google Earth Engine (GEE) platform. The propensity score matching (PSM) method was applied with eight control variables to balance the heterogeneity of land characteristics inside and outside PAs. The effectiveness of PAs in Himalaya was quantified based on matched samples. The results showed that the forest cover in Himalaya increased by 4983.65 km2 from 1998 to 2008, but decreased by 4732.71 km2 from 2008 to 2018. Further analysis revealed that deforestation and reforestation mainly occurred at the edge of forest tracts, with over 55% of forest fluctuation occurring below a 2000 m elevation. Forest cover changes in PAs of Himalaya were analyzed; these results indicated that about 56% of PAs had a decreasing trend from 1998 to 2018, including the Torsa (Ia PA), an area representative of the most natural conditions, which is strictly protected. Even so, as a whole, PAs in Himalaya played a positive role in halting deforestation.

scholarly journals Change in tropical forest cover of Southeast Asia from 1990 to 2010

2014 ◽  
Vol 11 (2) ◽  
pp. 247-258 ◽  
Author(s):  
H.-J. Stibig ◽  
F. Achard ◽  
S. Carboni ◽  
R. Raši ◽  
J. Miettinen
Keyword(s):  
Southeast Asia ◽  
Satellite Imagery ◽  
Forest Cover ◽  
Satellite Image ◽  
Image Interpretation ◽  
Important Change ◽  
Automated Classification ◽  
Natural Forests ◽  
Forest Cover Change ◽  
Forest Change

Abstract. The study assesses the extent and trends of forest cover in Southeast Asia for the periods 1990–2000 and 2000–2010 and provides an overview on the main causes of forest cover change. A systematic sample of 418 sites (10 km × 10 km size) located at the one-degree geographical confluence points and covered with satellite imagery of 30 m resolution is used for the assessment. Techniques of image segmentation and automated classification are combined with visual satellite image interpretation and quality control, involving forestry experts from Southeast Asian countries. The accuracy of our results is assessed through an independent consistency assessment, performed from a subsample of 1572 mapping units and resulting in an overall agreement of >85% for the general differentiation of forest cover versus non-forest cover. The total forest cover of Southeast Asia is estimated at 268 Mha in 1990, dropping to 236 Mha in 2010, with annual change rates of 1.75 Mha (∼0.67%) and 1.45 Mha (∼0.59%) for the periods 1990–2000 and 2000–2010, respectively. The vast majority of forest cover loss (∼2 / 3 for 2000–2010) occurred in insular Southeast Asia. Complementing our quantitative results by indicative information on patterns and on processes of forest change, obtained from the screening of satellite imagery and through expert consultation, respectively, confirms the conversion of forest to cash crops plantations (including oil palm) as the main cause of forest loss in Southeast Asia. Logging and the replacement of natural forests by forest plantations are two further important change processes in the region.

scholarly journals 30-year changes of natural forests under human activities in the Indochina peninsula - case studies in Cambodia, Laos and Vietnam

2021 ◽  
Vol 43 (3) ◽  
Author(s):  
Duong Nguyen Dinh ◽  
Cam Lai Vinh
Keyword(s):  
Forest Cover ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Google Earth ◽  
Natural Forest ◽  
Infrastructure Development ◽  
Natural Forests ◽  
Forest Cover Change ◽  
Remotely Sensed Data ◽  
Automated Method

Natural forests are a basic component of the earth's ecology. It is essential for biodiversity, hydrological cycle regulation, and environmental protection. Natural forests are gradually degraded and reduced due to timber logging, conversion to cropland, production forests, commodity trees, and infrastructure development. Decreasing natural forests results in loss of valuable habitats, land degradation, soil erosion, and imbalance of water cycle on the regional scale. Thus, operational monitoring of natural forest cover change has been in the interest of scientists for a long time. Current forest mapping methods using remotely sensed data provide limited capability to separate natural forests and planted forests. Natural forest statistics are often generated using official forestry national reports that have different bias levels due to different methodologies applied in different countries in forest inventory. Over the last couple of decades, natural forests have been over-exploited for various reasons. This led to forest cover degradation and water regulation capability, which results in extreme floods and drought of a watershed in general. This situation demands an urgent need to develop a fast, reliable, and automated method for mapping natural forests. In this study, by applying a new method for mapping natural forests by Landsat time series, the authors succeeded in mapping changes of natural forests of Cambodia, Laos, and Vietnam from 1989 to 2018. As a focused study area, three provinces: Ratanakiri of Cambodia, Attapeu of Laos, and Kon Tum of Vietnam were selected. The study reveals that after 30 years, 51.3% of natural forests in Ratanakiri, 27.8% of natural forests in Attapeu, and 50% of natural forests in Kon Tum were lost. Classification results were validated using high spatial resolution imagery of Google Earth. The overall accuracy of 99.3% for the year 2018 was achieved.

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