Annual Deforestation Rate Research Articles

Carbon Emissions from Deforestation and Degradation in a Forest Reserve in Venezuela between 1990 and 2015

This study presents for the first time in Venezuela a joint analysis of deforestation and forest degradation processes, including its effects on carbon emissions. The Caparo Forest Reserve, located in the Western Plains ecoregion, in one of the national hot spots of deforestation, served as a case study using three different periods: 1990–2000, 2000–2010 and 2010–2015. In the context of the United Nations Framework Convention on Climate Change (UNFCCC) framework, the Practice Guidance for Land Use, Land-Use Change and Forestry from the Intergovernmental Panel on Climate Change (IPCC) was followed. These guidelines combine the activity data for the estimation of deforestation and degradation rates, in this case using open access Landsat imagery in conjunction with the TerraAmazon system with the emission factors, and these based on aboveground biomass (AGB) estimations using field data from permanent plots monitored during the study period. Deforestation was responsible of a net loss of −53,461 ha, while close to −3667 ha were classified as degraded forests during the 1990–2000 decade (−4.9% annual deforestation rate). An estimated area of −36,447 ha and −515 ha between 2000 and 2010 was affected by both processes (−4.3% annual forest loss), and −8111 ha and −737 ha between 2010 and 2015 (−3.2% per year). These processes were responsible for an estimated equivalent in carbon emissions of 2.21 ± 0.32 (SEM—Standard Error of the Mean) Mt CO2 year−1 (1990–2000), 1.56 ± 0.19 Mt CO2per year between 2000 and 2010, while 0.80 ± 0.11 Mt CO2 year−1 during the 2010–2015 period. Between 92.9% and 98.63% (mean 94.9%) of these emissions came from deforestation, and between 1.37% and 7.79% (mean 5.1%) from forest degradation. Using available data, at national scale, deforestation and forest degradation in Caparo represented, on average, 0.49% of the total CO2 emissions and about 1.79% of land use change related emissions for the same period in Venezuela. Finally, we briefly outline a set of elements so these results can serve as a baseline for the potential establishment of a Reducing Emissions from Deforestation and Forest Degradation (REDD+) strategy in the area.

Fire and edge effects in a fragmented tropical forest landscape in the southwestern Amazon

Amazonian forests are subject to both anthropogenic and climate impacts despite the recent decline of annual deforestation rates. Forests in the southwestern Amazon have been intensively fragmented due to deforestation and recently intense drought events have caused extensive forest fires in this region. However, the impacts of fires and fragmentation on forests and forest responses to disturbances in the southwestern region are still poorly studied. We examined impacts of forest fire and fragmentation on forests in a highly fragmented landscape in Acre state to characterize post-fire forest recovery from wildfires, in 2005 and 2010, and analyze edge effects on forest aboveground biomass (AGB) and stem density. Forest inventories were conducted in five areas that included forests burned in 2005 and 2010 and unburned forests. Thirty-two 500 m transects were established with diameter at breast height (DBH) of stems and species identifications collected for subplots every 100m from forest edge to forest interiors. AGB and stem density in different DBH classes were estimated for trees, with pioneer species (Cecropia sp.) used as a disturbance indicator in our analysis. We used generalized liner mixed models to examine the importance of forest fragmentation variables including edge age, edge distance, forest cover and edge density, in addition to elevation and slope as environmental variables. Compared with unburned forests, the impact of forest fire on AGB and stem density is still significant four years after the event. However, rapid recovery within nine years after a fire event made AGB levels statistically indistinguishable from unburned areas. Pioneer species, AGB and densities of small stems of Cecropia sp. in recently burned forests and larger stems in the 2005 burned forest plots were significantly higher than unburned forests. As for forest fragmentation, high spatial variability of AGB and stem density was observed across the studied sites and limited edge effects were observed on AGB and tree density. Among forest fragmentation variables, edge age was the most important predictor of AGB and structural changes in forests followed by edge density. Although AGB changes were poorly correlated with edge distance, relative changes of forest AGB at different edge distances to AGB at 500m, the deepest edge distance, indicates a substantial reduction of AGB near forest edges.

Earth observation data for assessment of nationwide land cover and long-term deforestation in Afghanistan

This study has generated a national level spatial database of land cover and changes in forest cover of Afghanistan for the 1975–1990, 1990–2005 and 2005–2014 periods. Using these results we have analysed the annual deforestation rates, spatial changes in forests, forest types and fragmentation classes over a period of 1975 to 2014 in Afghanistan. The land cover map of 2014 provides distribution of forest (dry evergreen, moist temperate, dry temperate, pine, sub alpine) and non-forest (grassland, scrub, agriculture, wetlands, barren land, snow and settlements) in Afghanistan. The largest land cover, barren land, contributes to 56% of geographical area of country. Forest is distributed mostly in eastern Afghanistan and constitutes an area of 1.02% of geographical area in 2014. The annual deforestation rate in Afghanistan's forests for the period from 1975 to 1990 estimated as 0.06% which was declined significantly from 2005 to 2014. The predominant forest type in Afghanistan is moist temperate which shows loss of 80km2 of area during the last four decades of the study period. At national level, the percentage of large core forest area was calculated as 52.20% in 2014.

Effect of differential forest management on land-use change (LUC) in a tropical hill forest of Malaysia

Forest ownership is considered as a vital aspect for sustainable management of forest and its associated biodiversity. The Global Forest Resources Assessment 2015 reported that privately owned forest area are increasing on a global scale, but deforestation was found very active in privately owned hill forest areas of Malaysia. Penang State was purposively chosen as it has been experiencing rapid and radical changes due to urban expansion over the last three decades. In this study, analyses of land-use changes were done by PCI Geomatica using Landsat images from 1991 to 2015, future trends of land-use change were assessed using EXCEL forecast function, and its impact on the surrounding environment were conducted by reviewing already published articles on changing environment of the study area. This study revealed an annual deforestation rate of 1.4% in Penang Island since 1991. Trend analysis forecasted a forest area smaller than the current forest reserves by the year 2039. Impact analysis revealed a rapid biodiversity loss with increasing landslides, mudflows, water pollution, flash flood, and health hazard. An immediate ban over hill-land development is crucial for overall environmental safety.

An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso, Brazilian Amazon

Forest cover disturbances due to processes such as logging and forest fires are a widespread issue especially in the tropics, and have heavily affected forest biomass and functioning in the Brazilian Amazon in the past decades. Satellite remote sensing has played a key role for assessing logging activities in this region; however, there are still remaining challenges regarding the quantification and monitoring of these processes affecting forested lands. In this study, we propose a new method for monitoring areas affected by selective logging in one of the hotspots of Mato Grosso state in the Brazilian Amazon, based on a combination of object-based and pixel-based classification approaches applied on remote sensing data. Logging intensity and changes over time are assessed within grid cells of 300m×300m spatial resolution. Our method encompassed three main steps: (1) mapping forest/non-forest areas through an object-based classification approach applied to a temporal series of Landsat images during the period 2000–2015, (2) mapping yearly logging activities from soil fraction images on the same Landsat data series, and (3) integrating information from previous steps within a regular grid-cell of 300m×300m in order to monitor disturbance intensities over this 15-years period. The overall accuracy of the baseline forest/non-forest mask (year 2000) and of the undisturbed vs disturbed forest (for selected years) were 93% and 84% respectively. Our results indicate that annual forest disturbance rates, mainly due to logging activities, were higher than annual deforestation rates during the whole period of study. The deforested areas correspond to circa 25% of the areas affected by forest disturbances. Deforestation rates were highest from 2001 to 2005 and then decreased considerably after 2006. In contrast, the annual forest disturbance rates show high temporal variability with a slow decrease over the 15-year period, resulting in a significant increase of the ratio between disturbed and deforested areas. Although the majority of the areas, which have been affected by selective logging during the period 2000–2014, were not deforested by 2015, more than 70% of the deforested areas in 2015 had been at least once identified as disturbed forest during that period.

Titling land to conserve forests: The case of Cuyabeno Reserve in Ecuador

Abstract We used a mixed-methods approach to assess the impact of a ‘forest-friendly’ titling program on previously untitled lands surrounding the Cuyabeno Reserve in Ecuador. Such programs are part of an increasing trend in tenure formalization intended to simultaneously strengthen tenure security, reduce deforestation, and open the door for more incentive-based conservation programs. We use quasi-experimental methods to estimate and compare the impact of titling on forest outcomes for lands that are titled with certain limitations on the ownership bundle of rights, alongside lands titled but without these restrictions. This quantitative analysis is paired with results from a series of focus group interviews with landowners to understand their experiences with the titling effort, particularly tied to the restrictions. Our results point to a statistically significant impact of titling with restrictions on reducing deforestation by 34%, whereas titling without such restrictions resulted in no significant effect. When we explore impacts according to annual deforestation rates, the results suggest that titled lands are buffered from the surges in deforestation that otherwise occurred on untitled lands and more broadly across the region. While ‘forest-friendly’ restrictions had more of an effect on forest outcomes than titled lands without, the insights shared by landowners suggest important concerns about equity and unjust burdens on current households that could risk livelihood options for future generations.

Predictive modelling of the spatial pattern of past and future forest cover changes in India

This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km2, which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880–2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multi-layer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km2 of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.

Expanding temporal resolution in landscape transformations: Insights from a landsat-based case study in Southern Chile

Understanding temporal and spatial dimensions of land cover dynamics is a critical factor to link ecosystem transformation to land and environmental management. The trajectory of land cover change is not a simple difference between two conditions, but a continuous process. Therefore, there is a need to integrate multiple time periods to identify slow and rapid transformations over time. We mapped land cover composition and configuration changes using time series of Landsat TM/ETM+ images (1985–2011) in Southern Chile to understand the transformation process of a temperate rainforest relict and biodiversity hotspot. Our analysis builds on 28 Landsat scenes from 1985 to 2011 that have been classified using a random forests approach. Base on the high temporal data set we quantify land cover change and fragmentation indices to fully understand landscape transformation in this area. Our results show a high deforestation process for old growth forest strongest at the beginning of the study period (1985–1986–1998–1999) followed by a progressive slowdown until 2011. Within different study periods deforestation rates were much larger than the average rate over the complete study period (0.65%), with the highest annual deforestation rate of 1.2% in 1998–1999. The deforestation resulted in a low connectivity between native forest patches. Old-growth forest was less fragmented, but was concentrated mainly in two large regions (the Andes and Coastal mountain range) with almost no connection in between. Secondary forest located in more intensively used areas was highly fragmented. Exotic forest plantation areas, one of the most important economic activities in the area, increased sevenfold (from 12,836 to 103,540ha), especially during the first periods at the expense of shrubland, secondary forest, grassland/arable land and old grown forest. Our analysis underlines the importance of expanding temporal resolution in land cover/use change studies to guide sustainable ecosystem management strategies as increase landscape connectivity and integrate landscape planning to economic activities. The study is highlighting the key role of remote sensing in the sustainable management of human influenced ecosystems.

Assessment and Monitoring of Deforestation and Land-Use Changes (1976-2014) in Andaman and Nicobar Islands, India Using Remote Sensing and GIS

Andaman and Nicobar Islands are part of Indo- Burma and Sundaland global biodiversity hotspots. This study provides spatial information on forest types, deforestation and associated land-use changes in Andaman and Nicobar Islands during 1976 to 2014. Satellite remote sensing and geographical information system (GIS) techniques have been used to analyse forest cover changes, rate of deforestation and to map patterns of forest cover distribution in Andaman and Nicobar Islands. Classified maps prepared for 1976, 1989, 1993, 2000, 2006 and 2014 indicate that the forest cover accounts for an area of 7086.1 (85.9%), 6969.2 (84.5%), 6941.1 (84.1%), 6934.6 (84.1%), 6617.8 (80.2%) and 6407.3 sq. km (77.7%) respectively. It was found that the area occupied by evergreen forests is very high, consisting of 3065.1 sq. km (32.2%) followed by semi-evergreen (1531.6 sq. km), moist deciduous (1133.4 sq. km) and mangrove forest (677.2 sq. km) in 2014. There is large-scale deforestation in Andaman and Nicobar Islands which has been estimated as 678.8 sq. km during the last four decades. The loss of forest cover is high in moist deciduous forests which has been estimated as 312.2 sq. km in Andaman Islands; whereas in Nicobar Islands, the highest loss was found in evergreen forests (244.6 sq. km). The rate of deforestation in Andaman and Nicobar Islands was high during 2000-2006 (0.78) indicating major influence of the tsunami of 26 December 2004. The annual rate of deforestation from 2006 to 2014 was 0.40. The geospatial analysis of areas of forest cover change provides baseline information for restoration and conservation planning.

Land-use/land-cover change and forest fragmentation in the Jigme Dorji National Park, Bhutan

Monitoring land-cover change and understanding the dynamics of forest cover is critically important for the management of forest ecosystems. This study assesses land cover change (1990–2015) in the Jigme Dorji National Park, one of the largest national parks in Bhutan, using Landsat imageries. Overall changes in forest cover, as well as the fragmentation of dense forests within the park, were analyzed using landscape metrics. The results show that total forest area, dense forest area, and moderately dense forest area decreased while open forest area and shrub area increased. During the 25-year study period, the annual deforestation rate was 821 ha year−1, equivalent to 0.63% year−1. The total number of patches of all type increased by 176%, from 250,353 to 691,811, while the mean patch size of forest patches decreased from 1.7 ha to 0.6 ha. The number of smaller patches (0–100 ha size class) in dense forest increased rapidly, indicating that a more fragmented landscape accrued over time, a trend that does not bode well for the maintenance of biodiversity in the national park.

Environmental Concerns of Deforestation in Myanmar 2001–2010

Deforestation in Myanmar has recently attracted much attention worldwide. This study examined spatio-temporal patterns of deforestation and forest carbon flux in Myanmar from 2001 to 2010 and environmental impacts at the regional scale using land products of the Moderate Resolution Imaging Spectroradiometer (MODIS). The results suggest that the total deforestation area in Myanmar was 21,178.8 km2, with an annual deforestation rate of 0.81%, and that the total forest carbon release was 20.06 million tons, with an annual rate of 0.37%. Mangrove forests had the highest deforestation and carbon release rates, and deciduous forests had both the largest deforestation area and largest amount of carbon release. During the study period, the south and southwestern regions of Myanmar, especially Ayeyarwady and Rakhine, were deforestation hotspots (i.e., the highest deforestation and carbon release rates occurred in these regions). Deforestation caused significant carbon release, reduced evapotranspiration (ET), and increased land surface temperatures (LSTs) in deforested areas in Myanmar during the study period. Constructive policy recommendations are put forward based on these research results.

SYNERGY OF OPTICAL AND SAR DATA FOR MAPPING AND MONITORING MANGROVES

Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.

SYNERGY OF OPTICAL AND SAR DATA FOR MAPPING AND MONITORING MANGROVES

Abstract. Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.

Long term changes in forest cover and land use of Similipal Biosphere Reserve of India using satellite remote sensing data

The spatial changes in forest cover of Similipal biosphere reserve, Odisha, India over eight decades (1930–2012) has been quantified by using multi-temporal data from different sources. Over the period, the forest cover reduced by 970.8 km2 (23.6% of the total forest), and most significantly during the period, 1930–1975. Human-induced activities like conversion of forest land for agriculture, construction of dams and mining activities have been identified as major drivers of deforestation. Spatial analysis indicates that 399 grids (1 grid = 1 × 1 km) have undergone large-scale changes in forest cover (>75 ha) during 1930–1975, while only 3 grids have shown >75 ha loss during 1975–1990. Annual net rate of deforestation was 0.58 during 1930–1975, which has been reduced substantially during 1975–1990 (0.04). Annual gross rate of deforestation in 2006–2012 is indeed low (0.01) as compared to the national and global average. This study highlights the impact and effectiveness of conservation practices in minimizing the rate of deforestation and protecting the Similipal Biosphere Reserve.

Deforestation and malaria incidence in the legal Amazon Region between 1996 and 2012

Abstract Malaria is a major public health problem, and Brazil is the largest contributor in America to the number of cases. In the country, the Amazon concentrates around 99.5% of the cases. This article aims to compare trends in deforestation rate and the parasite index of malaria in the Amazon region between 1996 and 2012. The annual crude rate of deforestation and the Annual Parasite Index (API) of malaria between 1996 and 2012 were estimated. The rate of deforestation has great variability among the states every year, and a similar situation is observed for malaria API over the studied period. There are important variations across the states within this period. The historical series of crude deforestation rate and malaria API have markedly similar trends; with a 1-2 years lag between them, suggesting that the occurrence of fluctuations in deforestation numbers impacts malaria numbers in a period immediately following. The mapping of deforested areas and the analysis of temporal series are therefore useful in the analysis of malaria.

Development of national database on long-term deforestation (1930–2014) in Bangladesh

The aim of the present study is to prepare a nation-wide spatial database on forest cover to assess and monitor the land use changes associated with deforestation in Bangladesh. The multi-source data were interpreted to get the forest cover map of 1930, 1975, 1985, 1995, 2006 and 2014. The spatial information generated on total area under forest cover, rate of deforestation and afforestation, changes across forest types, forest canopy density, replacement land use in deforested area and deforestation hotspots. This spatial analysis has indicated that forest cover is undergoing significant negative change in area and quality. We report that forests in Bangladesh covered an area of 23,140km2 in 1930 which has decreased to 14,086km2 in 2014, a net loss of 9054km2 (39.1%) in eight decades. Analysis of annual rate of gross deforestation for the recent period indicates 0.77% during 2006–2014. During the past eight decades, semi-evergreen forests show loss of 56.4% of forest cover followed by moist deciduous forests (51.5%), dry deciduous forests (43.1%) and mangroves (6.5%). The loss of 23.5% of dense forest cover was found from 1975 to 2014. Dense semi-evergreen forests shows more negative change (36.9%) followed by dense moist deciduous forest (32.7%) from 1975 to 2014. Annual rate of deforestation is higher in dense forests compared to open forests from 2006 to 2014 and indicates increased threat due to anthropogenic pressures. The spatial analysis of forest cover change in mangroves has shown a lower rate of deforestation. Most of the forest conversions have led to the degradation of forests to scrub and transition to agriculture and plantation. The study has identified the ‘deforestation hotspots’ can help in strategic planning for conservation and management of forest resources.

CAMBIOS DE COBERTURA Y FRAGMENTACIÓN DE MANGLARES EN LA REGIÓN DEL SOCONUSCO, CHIAPAS, MÉXICO, 1994-2011

<p>Se analizó la dinámica espacial de la cobertura de manglar en el humedal costero El Cabildo-Amatal-Gancho Murillo, Chiapas, México, determinando su impacto por procesos de fragmentación. El análisis de cambios demostró una pérdida de 509 ha de manglar en un período de 17 años y una tasa de deforestación anual del 1.05%. Las métricas estimadas indican que el sistema se conforma por 38 parches de manglar mayores a 0.36 ha en el 2011, presentando un aumento del 58% en su número con respecto a 1994. La dimensión fractal de 1.4 y la tasa de fragmentación indican un paisaje impactado. Los resultados muestran que debido a los cambios en el sistema es necesario implementar planes de manejo costero.</p><p> </p><p><span>LAND COVERAGE CHANGES AND FRAGMENTATION <span>OF MANGROVES IN THE SOCONUSCO REGION, <span>CHIAPAS, MEXICO, 1994-2011</span></span><br /></span></p><p><strong>ABSTRACT</strong><br /> We analyzed the spatial dynamics of the mangrove coverage of El Cabildo-Amatal-Gancho Murillo coastal wetland in Chiapas, Mexico by determining their impact by fragmentation processes. The analysis of changes showed a loss of 509 ha of mangrove in a 17-year period and an annual deforestation rate of 1.05 %. The metrics indicate that the system was composed of 38 largest mangrove patches greater than 0.36 ha in 2011, which represents an increase of 58% regarding its size in the year 1994. The fractal dimension of 1.4 and the fragmentation rate estimated suggest that the landscape has been impacted. Altogether, the results show that, due to changes in the system, it is necessary to implement coastal management plans.</p><p> </p><p> </p>

Análisis y modelación de los procesos de deforestación: un caso de estudio en la cuenca del río Coyuquilla, Guerrero, México

We analyzed land use / land cover changes (LUCC) in the basin of the river Coyuquilla in the state of Guerrero, Mexico during the periods 1986-2000 and 2000-2011. Land use / land cover maps for 1986, 2000 and 2011 were elaborated using Landsat and SPOT images and previous cartography from the National Institute of Geography and Statistics (INEGI) and the National Forest Inventory. The analysis of the satellite images was carried out through the method of interdependent interpretation of multidate satellite data in order to assure the consistency of the classification. Maps’ accuracy was assessed through the interpretation of a stratified random sample of verification sites. Census information from INEGI, which includes demographic and socioeconomic data, along with environmental data were gathered and integrated in a GIS database. The method of the weights of evidence was applied in order to identify the LUCC drivers and elaborate maps of change probability. These maps were evaluated using a receiver operating characteristic (ROC) analysis.Overall map accuracy was 71%, 73% and 75% for 1986, 2000 and 2011 respectively. During the period 1986-2000, a loss of 16% of the forest cover that mainly affected the dry tropical forest with an annual deforestation rate of -3.7% was observed. During the period 2000-2011, a loss of 22% of forest cover was observed and oak forest was the cover with the highest deforestation rate (-5.54%). These results are consistent with previous studies in the region. The increase of induced pasture was the main cause of the loss of forest cover. The elevation, distance to roads, distance to human settlements and the slope were important factors to explain deforestation in the three forest types and the two periods analyzed. The assessment of probabilistic models through the computing of the area under the ROC curve index gives index values between 0.61 and 0.92. These results are well above a random model and show that the models were able to identify the most likely to LUCC areas for the next period (2000-2011) for the eight analyzed transitions. Therefore, the model presents a reasonable performance in identifying the forest areas more likely to be cleared. However, in order to understand the processes of changes and their causes, the statistical approach must be complemented by a study based on a social science approach.

Extracting and analyzing forest and woodland cover change in Eritrea based on landsat data using supervised classification

Remote sensing images are suitable for quantifying and analyzing land-cover dynamics, particularly for forest-cover change. In this study, the methodology used the supervised classification technique to classify and analyze the total forest-cover change in Eritrea. The results indicated that the forest and woodland cover extracted with high overall accuracy and kappa coefficient of approximately 96% and 0.94, respectively. Generally, the forest cover declined from 2966km2 to 1401km2 from the 1970s to 2014, and the woodland forest cover was reduced from 14,879km2 to 13,677km2 in the same period. The annual rate of deforestation was very high, with approximately 0.35% (62km2) of the total forest cover lost each year for the last 44years. The study concluded that deforestation is one of the leading causes of environmental degradation in the country and it might be caused by human factors as well as due to climate change, i.e., by prolonged drought and inadequate and erratic rainfall. Thus, this paper may significantly help decision makers and researchers who are interested in remote sensing for forest management and monitoring, and for controlling and planning development at local, regional, and global [scales].

Evaluating the trade-off between food and timber resulting from the conversion of Miombo forests to agricultural land in Angola using multi-temporal Landsat data

The repopulation of abandoned areas in Angola after 27years of civil war led to a fast and extensive expansion of agricultural fields to meet the rising food demand. Yet, the increase in crop production at the expense of natural resources carries an inherent potential for conflicts since the demand for timber and wood extraction are also supposed to rise.We use the concept of ecosystem services to evaluate the trade-off between food and woody biomass. Our study area is located in central Angola, in the highlands of the upper Okavango catchment. We used Landsat data (spatial resolution: 30×30m) with a bi-temporal and multi-seasonal change detection approach for five time steps between 1989 and 2013 to estimate the conversion area from woodland to agriculture. Overall accuracy is 95%, user's accuracy varies from 89–95% and producer's accuracy ranges between 92–99%. To quantify the trade-off between woody biomass and the amount of food, this information was combined with indicator values and we furthermore assessed biomass regrowth on fallows.Our results reveal a constant rise in agricultural expansion from 1989–2013 with the mean annual deforestation rate increasing from roughly 5300ha up to about 12,000ha. Overall, 5.6% of the forested areas were converted to agriculture, whereas the FAO states a national deforestation rate for Angola of 5% from 1990–2010 (FAO, 2010). In the last time step 961,000t per year of woodland were cleared to potentially produce 1240t per year of maize. Current global agro-economical projections forecast increasing pressure on tropical dry forests from large-scale agriculture schemes (Gasparri et al., 2015; Searchinger and Heimlich, 2015). Our study underlines the importance of considering subsistence-related change processes, which may contribute significantly to negative effects associated with deforestation and degradation of these forest ecosystems.