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Forest loss is an environmental issue that threatens ecosystems in the Dominican Republic (the DR). Although shifting agriculture by slash-and-burn methods is thought to be the main driver of forest loss in the DR, empirical evidence of this relationship is still lacking. Since remotely sensed data on fire occurrence is a suitable proxy for estimating the spread of shifting agriculture, here I explore the association between forest loss and fire during the first 18 years of the 21st Century using zonal statistics and spatial autoregressive models on different spatio-temporal layouts. First, I found that both forest loss and fire were spatially autocorrelated and statistically associated with each other at a country scale over the study period, particularly in the western and central part of the DR. However, no statistical association between forest loss and fire was found in the eastern portion, a region that hosts a large international tourism hub. Second, deforestation and fire showed a joint cyclical variation pattern of approximately four years up to 2013, and from 2014 onwards deforestation alone followed a worrying upward trend, while at the same time fire activity declined significantly. Third, I found no significant differences in forest loss patterns between the deforested area of small (1 ha) clearings of forest. I propose these findings hold potential to inform land management policies that help reduce forest loss, particularly in protected areas, mountain areas, and the vicinity of tourism hubs.

Achieving a global forest transition, that is, a shift from net deforestation to reforestation, is essential for climate change mitigation. However, both land-based climate change mitigation policy and research on forest transitions neglect key processes that relieve pressure from forests, but cause emissions elsewhere (‘hidden emissions’). Here, we identify three major causes of hidden emissions of forest transitions, that is, emissions from agricultural intensification, from woodfuel substitution, and from land displacement. Taken together, these emissions may compromise the climate change mitigation effect of national forest transitions. We propose to link analyses of hidden emissions of forest transitions with quantifications of full socio-ecological greenhouse-gas accounts and analyses of their politics. Such an integration allows for drawing lessons for effective and just climate change mitigation policies.

Protected areas (PAs) are a cornerstone for conservation biodiversity. Madagascar, as a hotspot for biodiversity, has a network of 114 terrestrial protected areas covering the main forest types occurring on the island. Deforestation continues unabated despite the network covering 11% of the island. Here we present a case study approach reporting on four PAs from the humid forests, dry western forests, and southwestern dry and spiny forests and thickets. To describe deforestation in and around the case sites, we have considered a time window of 30 years for analysis, focusing on six years with reliable data: 1990, 2000, 2010, 2015 (the year of latest PA network update), and 2017. We have considered forest versus other land covers within the PAs in "buffers" at a distance of 500 m, 2.5 km, 5 km, and 10 km from the border of the PA. These buffers were set from the border towards the center of the PA (inside the PAs) and from the border outside the PAs. The smallest PAs, Kasijy (IUCN IV), and Behara Tranomaro (no IUCN category), showed the least forest loss. Tsaratanana (IUCN I) had the highest deforestation rates within the last two years of analysis, with deforestation concentrated in the core area. Ranobe PK-32 (no IUCN category), originally with the largest forest extent, has lost most of its forest cover and showed the highest annual deforestation rate (3.5%) between 2015 and 2017. All four cases prove to be very challenging to manage. Future conservation activities require tailored interventions to account for site-specific current and potential future threats, as detailed in this contribution., Forests, 12 (9), ISSN:1999-4907

We assess the fulfillment of zero deforestation agreements at farm level, ALOS-1 and ALOS-2 PALSAR fine beam dual imagery for years 2010 and 2016 was processed with ad-hoc routines to estimate stable forest, deforestation, and stable non-forest extension for 2615 participant farms in five regions of Colombia. Farms associated with Meta Foothills regions showed zero deforestation during the period analyzed (2010–2016), while other regions showed low deforestation rates with the exception of the Cesar River Valley (75 ha). Results, suggests that topography and dry weather conditions have an effect on radar-based mapping accuracy. Nevertheless, overall ALOS Phased Array L-band SAR (PALSAR) data provided overall accurate, relevant, and consistent information for forest change analysis for local zero deforestation agreements assessment.

National monitoring of forestlands and the processes causing canopy cover loss, be they abrupt or gradual, partial or stand clearing, temporary (disturbance) or persisting (deforestation), are necessary at fine scales to inform management, science and policy. This study utilizes the Landsat archive and an ensemble of disturbance algorithms to produce maps attributing event type and timing to &gt, 258 million ha of contiguous Unites States forested ecosystems (1986&ndash, 2010). Nationally, 75.95 million forest ha (759,531 km2) experienced change, with 80.6% attributed to removals, 12.4% to wildfire, 4.7% to stress and 2.2% to conversion. Between regions, the relative amounts and rates of removals, wildfire, stress and conversion varied substantially. The removal class had 82.3% (0.01 S.E.) user&rsquo, s and 72.2% (0.02 S.E.) producer&rsquo, s accuracy. A survey of available national attribution datasets, from the data user&rsquo, s perspective, of scale, relevant processes and ecological depth suggests knowledge gaps remain.

We examine the effect of forest cover change on agricultural productivity using household panel data and remote sensing data on forest change. The focus of the study is rural Indonesia, where deforestation is causing intensive biodiversity loss while agriculture is the main industry. We estimate an agricultural production function and find that farmers in rural Indonesia experienced a reduction in agricultural productivity of 45% between 2001 and 2014 or US$2.63 billion in 2014. In addition, we explore the mechanisms underlying the productivity loss and find that biological pest control is the most plausible explanation., World Development, 114, pp.196-207; 2019

Massive deforestation in Indonesia drives the need for proper monitoring using appropriate technology and method. The continuing mission of Landsat sensor extends the observation to almost 30 years back, initiating the ability to monitor the dynamics of vegetation intensively. By taking the advantage of the Landsat archive, advanced semi-automatic classification method, namely ClasLite developed by Asner et al. (J Appl Remote Sens 3:33543–33543, 2009) and a new end-product of 30 m Global Forest Cover Change cover (GFC) datasets developed by (Hansen et al. in Science 342:850–853, 2013a), offered the ability to easily monitor deforestation and forest degradation with little or few knowledge of mapping. This study aims to assess the performance of these newly available products of GFC and the ClasLite method against the traditional pixel-based supervised classification of minimum distance to mean (MD), maximum likelihood (ML), spectral angle mapper (SAM), and random forest (RF). Visual image interpretation of pan-sharpened Landsat was carried out to measure the accuracy of each final map. Result demonstrated that GFC and CLaslite performance has 3 to 18% higher overall accuracy for mapping vegetation cover change compared with the conventional supervised analysis using MD, ML, SAM, and RF with ClasLite as the most accurate method with 78.14 ± 2%. Further adjustment of the cover change map of GFC by using forest extent from ClasLite was able to increase the accuracy of the original GFC data by 10%. Therefore, GFC and ClasLite ensure the ability to monitor vegetation cover change accurately in a simple manner.

Worldwide mining activities are one of the major anthropogenic activities that have caused high forest cover loss (FCL). In this study, we have quantified FCL in Odisha State due to mining activities analyzing Hansen Global Forest Change (HGFC) time series data for the period of 2001–2019 in Google Earth Engine platform. Our analysis suggests that Nabarangpur, Puri, Kendrapara, and Kalahandi districts lost more than 20% of their forest cover during this period. Rayagada and Koraput were the top two districts that recorded the highest FCL with mean change rates of 13.81 km2/year and 7.17 km2/year, respectively. The results point out that mining operations have grown in recent years in Odisha State, and the increase in these activities has contributed to the increase in FCL. This study offers a cost-effective methodology to monitor FCL in mining areas which will eventually contribute to the protection of forest biodiversity and forest dwelling tribal population.

Deforestation and forest degradation around the world endanger the functioning of ecosystems, climate stability, and conservation of biodiversity. We assessed the spatial and temporal dynamics of forest cover in Myanmar’s Hkakabo Razi Landscape (HRL) to determine its integrity based on forest change and fragmentation patterns from 1989 to 2016. Over 80% of the HRL was covered by natural areas, from which forest was the most prevalent (around 60%). Between 1989 and 2016, forest cover declined at an annual rate of 0.225%. Forest degradation occurred mainly around the larger plains of Putao and Naung Mung, areas with relatively high human activity. Although the rate of forest interior loss was approximately 2 to 3 times larger than the rate of total forest loss, forest interior was prevalent with little fragmentation. Physical and environmental variables were the main predictors of either remaining in the current land-cover class or transitioning to another class, although remaining in the current land cover was more likely than land conversion. The forests of the HRL have experienced low human impact and still constitute large tracts of contiguous forest interior. To ensure the protection of these large tracts of forest, sustainable forest policies and management should be implemented.

To combat global deforestation, monitoring forest disturbances at sub-annual scales is a key challenge. For this purpose, the new Planetscope nano-satellite constellation is a game changer, with a revisit time of 1 day and a pixel size of 3-m. We present a near-real time forest disturbance alert system based on PlanetScope imagery: the Thresholding Rewards and Penances algorithm (TRP). It produces a new forest change map as soon as a new PlanetScope image is acquired. To calibrate and validate TRP, a reference set was constructed as a complete census of five randomly selected study areas in Tuscany, Italy. We processed 572 PlanetScope images acquired between 1 May 2018 and 5 July 2019. TRP was used to construct forest change maps during the study period for which the final user’s accuracy was 86% and the final producer’s accuracy was 92%. In addition, we estimated the forest change area using an unbiased stratified estimator that can be used with a small sample of reference data. The 95% confidence interval for the sample-based estimate of 56.89 ha included the census-based area estimate of 56.19 ha.

Protected areas (PAs) are a foundational and essential strategy for reducing biodiversity loss. However, many PAs around the world exist on paper only; thus, while logging and habitat conversion may be banned in these areas, illegal activities often continue to cause alarming habitat destruction. In such cases, the presence of armed conflict may ultimately prevent incursions to a greater extent than the absence of conflict. Although there are several reports of habitat destruction following cessation of conflict, there has never been a systematic and quantitative “before-and-after-conflict” analysis of a large sample of PAs and surrounding areas. Here we report the results of such a study in Colombia, using an open-access global forest change dataset. By analysing 39 PAs over three years before and after Colombia’s peace agreement with the Revolutionary Armed Forces of Colombia (FARC), we found a dramatic and highly significant increase in the deforestation rate for the majority of these areas and their buffer zones. We discuss the reasons behind such findings from the Colombian case, and debate some general conservation lessons applicable to other countries undergoing post-conflict transitions.

Forests both support biodiversity and provide a wide range of benefits to people at multiple scales. Global and national remote sensing analyses of drivers of forest change generally focus on broad-scale influences on area (composition), ignoring arrangement (configuration). To explore meso-scale relationships, we compared forest composition and configuration to six indicators of economic growth over 23 years (1992-2015) of satellite data for 23 island nations. Based on global analyses, we expected to find clear relationships between economic growth and forest cover. Eleven islands lost 1 to 50% of forest cover, eight gained 1 to 28%, and four remained steady. Surprisingly, we found no clear relationship between economic growth trends and forest-cover change trajectories. These results differ from those of global land-cover change analyses and suggest that conservation-oriented policy and management approaches developed at both national and local scales are ignoring key meso-scale processes.

Tropical forests play a central role in reaching the climate change mitigation goals of the Paris agreement. Over the last decade, hundreds of subnational initiatives that aim to reduce deforestation related emissions (REDD+) have been implemented across the tropics which need to be evaluated and monitored. This makes it important to develop and compare different approaches to quantifying impacts on carbon emissions and associated socio-economic trade-offs and synergies. This thesis compared methods that assess deforestation before and after the start of REDD+, in- and outside REDD+ initiatives, and across different scales. These assessments require (spatial) data on forest area and changes therein, but these data contain uncertainties which influence the REDD+ evaluation results. These uncertainties need to be accounted for in REDD+ assessments. The causes of deforestation differ across and within the different study sites, thus calling for locally tailored approaches when designing REDD+ interventions. Apart from the outcomes of these interventions on forest change, well-being outcomes need to be addressed and assessed too in order not to harm forest-dependent livelihoods.

The world's forests are crucially important for both biodiversity conservation and climate mitigation. New forest status and forest change spatial layers using remotely sensed data have revolutionised forest monitoring globally, and provide fine-scale deforestation alerts that can be actioned in near-real time. However, existing products are restricted to representing tree cover and do not reflect the considerable spatial variation in the biological importance of forests. Here we link modelled biodiversity values to remotely sensed data on tree cover to develop global maps of forest biodiversity significance (based on the rarity-weighted richness of forest mammal, bird, amphibian and conifer species) and forest biodiversity intactness (based on the modelled relationship between anthropogenic pressures and community intactness). The strengths and weaknesses of these products for policy and local decision-making are reviewed and we map out future improvements and developments that are needed to enhance their usefulness.

Deforestation is recognized as a major driver of the loss of biodiversity and ecosystem services. It also disturbs natural processes such as biogeochemical, hydrological, and ecological cycles. In Malawi, deforestation is estimated to be responsible for the loss of 33,000 hectares per year, and is mainly attributed to agriculture expansion, tobacco growing, and excessive use of biomass. However, little research has been conducted at either the local level or that of forests located on customary land. This research aimed to identify and analyze the underlying driving factors associated with the proximate factors of agriculture expansion, tobacco growing, and brick burning in Mwazisi. Landsat images for 1991, 2004, and 2017 were downloaded from the United States Geological Survey website and used to analyze changes in forest cover. Interviews with households (n = 399) and Natural Resource Committee members, a focus group discussion with key officers, and observations were conducted during field data collection in 2017. The results of the land cover analysis showed that forest covered 66% of the study area in 1991, and by 2017 it had decreased to 45.8%. Most households depend on wood from customary land forests for tobacco curing (69%) and brick burning (68%). Furthermore, 47.6% of the households have expanded their agriculture land by approximately 0.57 hectares during the past 15 years. The interview survey and the focus group discussion identified that the underlying driving factors towards these anthropogenic activities are: (a) population growth, (b) poverty, (c) expensive alternative building materials, (d) lack of awareness, (e) lack of resources, (f) lack of commitment from the tobacco companies, and (g) market system of the cash crops grown in the area. In conclusion, a set of economic, institutional, social, and demographic factors, which are associated with imbalanced relationship between rural and urban areas, underpin agriculture expansion, tobacco growing, and brick burning, and have thereby contributed to the decline of the forest cover in Mwazisi, Malawi.

Strategic planning to increase forest cover in Central American transboundary areas of the Mesoamerican Biological Corridor requires understanding land-cover and land-use change trends and drivers. We estimated forest cover change from remotely sensed land-cover and land-use classifications from 1986, 2001, and 2010, in the tri-national Trifinio Region, bordering El Salvador, Guatemala, and Honduras. Our analysis spanned subnational, national, regional, and protected border areas. We determined correlations with direct drivers of deforestation, developing a multilevel linear regression model. Higher population density significantly correlated with deforestation; coffee, agroforestry, and pasture replaced forests. The tri-national park retained forests compared to neighboring areas, but additionality requires more research. The literature on drivers suggests similar processes and factors in other tropical regions. Forest cover governance efficacy is highly variable. Results indicate relationship be...

Anthropogenic land-cover change, e.g. deforestation and forest degradation cause carbon emissions. To estimate deforestation and forest degradation, it is important to have reliable data on forest cover. In this analysis, we evaluated annual MODIS Percent Tree Cover (PTC) data for the detection of forest change including deforestation, forest degradation, reforestation and revegetation. The annual MODIS PTC data (2000 – 2010) were pre-processed by applying quality layer. Based on the PTC values of the annual MODIS data, forest change maps were produced and assessed by comparing with the data from visual interpretation of SPOT-5 images. The assessment was applied to two case-studies: Ayuquila Basin and Monarch Reserve. Results show that the detected deforestation patches by visual interpretation are roughly 4 times in quantity more than those by MODIS PTC data, which can be partially due to the much higher spatial resolution of SPOT-5, being able to pick up small deforestation patches. This analysis found poor spatial overlapping for both case-studies. Possible reasons for the discrepancy in quantity and spatial coincidence were provided. It is necessary to refine the methodology for forest change detection by PTC images; also to refine the validation data in terms of data periods and forest change categories to ensure a better assessment.

Global Forest Change datasets have the potential to assist countries with national forest measuring, reporting and verification (MRV) requirements. This paper assesses the accuracy of the Global Forest Change data against nationally derived forest change data by comparing the forest loss estimates from the global data with the equivalent data from Guyana for the period 2001–2017. To perform a meaningful comparison between these two datasets, the initial year 2000 forest state needs first to be matched to the definition of forest land cover appropriate to a local national setting. In Guyana, the default definition of 30% tree cover overestimates forest area is by 483,000 ha (18.15%). However, by using a tree canopy cover (i.e., density of tree canopy coverage metric) threshold of 94%, a close match between the Guyana-MRV non-forest area and the Global Forest Change dataset is achieved with a difference of only 24,210 ha (0.91%) between the two maps. A complimentary analysis using a two-stage stratified random sampling design showed the 94% tree canopy cover threshold gave a close correspondence (R2 = 0.98) with the Guyana-MRV data, while the Global Forest Change default setting of 30% tree canopy cover threshold gave a poorer fit (R2 = 0.91). Having aligned the definitions of forest for the Global Forest Change and the Guyana-MRV products for the year 2000, we show that over the period 2001–2017 the Global Forest Change data yielded a 99.34% overall Correspondence with the reference data and a 94.35% Producer’s Accuracy. The Guyana-MRV data yielded a 99.36% overall Correspondence with the reference data and a 95.94% Producer’s Accuracy. A year-by-year analysis of change from 2001–2017 shows that in some years, the Global Forest Change dataset underestimates change, and in other years, such as 2016 and 2017, change is detected that is not forest loss or gain, hence the apparent overestimation. The conclusion is that, when suitably calibrated for percentage tree cover, the Global Forest Change datasets give a good first approximation of forest loss (and, probably, gains). However, in countries with large areas of forest cover and low levels of deforestation, these data should not be relied upon to provide a precise annual loss/gain or rate of change estimate for audit purposes without using independent high-quality reference data.

We used Landsat-based estimates of tree cover change to document the loss and gain of forest in the Dominican Republic between 2000 and 2016. Overall, 2,795 km2 of forest were lost, with forest gain occurring on only 393 km2, yielding a net loss of 2,402 km2 of forest, a decline of 11.1% or 0.7% per year. Deforestation occurred in all of the major forest types in the country, and ranged from a 13% decline in the area of semi-moist broadleaf forest to a 5.9% loss of cloud forest, mostly attributed to agriculture. Fire was a significant driver of forest loss only in Hispaniolan pine (Pinus occidentalis) forests and, to a lesser extent, in adjacent cloud forest. Deforestation rates were lower within protected areas, especially in dry and semi-moist broadleaf forests at lower elevations. Protected areas had a smaller, and generally negligible, effect on rates of forest loss in pine forest and cloud forest, largely due to the effects of several large wildfires. Overall, rates of deforestation in the Dominican Republic were higher than regional averages from across the Neotropics and appeared to have accelerated during the later years of our study period. Stemming deforestation will likely require enforcement of prohibitions on large-scale agricultural production within protected areas and development of alternatives to short-cycle, shifting agriculture.

The accurate information of forest cover change is important to measure the amount of carbon release and sink. The newly-available remote sensing based products and method such as Daichi Forest/Non-Forest (FNF), Global Forest Change (GFC) datasets and Semi-automatic Claslite systems offers the benefit to derive these information in a quick and simple manner. We measured the accuracy by constructing area-proportion error matrix from 388 random sample points and assessed the consistency analysis by looking at the spatial pattern of deforestation and regrowth from built-up area, roads, and rivers from 2010 – 2015 in Katingan district, Central Kalimantan. Accuracy assessment showed that those 3 datasets indicate low to medium accuracy level in which the highest accuracy was achieved by Claslite who produced 71 % ± 5 % of overall accuracy. The consistency analysis provides a similar spatial pattern of deforestation and regrowth measured from the road, river, and built-up area though their distance sensitivity are different one to another.

Forest is in trouble. The most recent (2015) FAO Forest Resources Assessment shows an encouraging trend towards a decrease in deforestation rates, but it also points out that since 1990 total forest loss corresponds to an area the size of South Africa. Efforts to curtail deforestation require reliable assessments, yet current definitions for what a forest exactly is differ significantly across countries, institutions and epistemic communities. Those differences have implications for forest management efforts: they entail different understandings about where exactly a forest starts and ends, and therefore also engender misunderstandings about where a forest should start and end, and about how forests should be managed. This special issue brings together different perspectives from practitioners and academic disciplines – including linguistics, geographic information science and human geography – around the problem of understanding and characterizing forest. By bringing together different disciplinary viewpoints, we hope to contribute to ongoing interdisciplinary efforts to analyse forest change. In this introduction, we propose that interrogating the relationship between forest definitions, boundaries and ways of valuing forests constitutes a productive way to critically conceptualize the trouble that forest is in.

The availability of dense time series of Landsat images pro-vides a great chance to reconstruct forest disturbance and change history with high temporal resolution, medium spatial resolution and long period. This proposal aims to apply forest change detection method in Hainan Jianfengling Forest Park using yearly Landsat time-series images. A simple detection method from the dense time series Landsat NDVI images will be used to reconstruct forest change history (afforestation and deforestation). The mapping result showed a large decrease occurred in the extent of closed forest from 1980s to 1990s. From the beginning of the 21st century, we found an increase in forest areas with the implementation of forestry measures such as the prohibition of cutting and sealing in our study area. Our findings provide an effective approach for quickly detecting forest changes in tropical original forest, especially for afforestation and deforestation, and a comprehensive analysis tool for forest resource protection.

Deforestation is one of the major threats to habitats in the Dominican Republic. In this work we present a forest baseline for the year 2000 and a deforestation map for the year 2011. Maps were derived from Moderate Resolution Imaging Radiometer (MODIS) products at 250m resolution. The vegetation continuous fields product (MOD44B) for the year 2000 was used to produce the forest baseline, while the vegetation indices product (MOD13Q1) was used to detect change between 2000 and 2011. Major findings based on the data presented here are reported in the manuscript “Habitat suitability and protection status of four species of amphibians in the Dominican Republic” (Sangermano et al., Appl. Geogr.,) [7].63, 2015, 55–65

We examine how forest cover change can affect agricultural productivity using household panel data and remote sensing data on forest change. The focus is on rural Indonesia, where intensive biodiversity loss is occurring from deforestation while agriculture is the main industry. We estimate an agricultural production function and find that farmers experienced reduction of agricultural productivity of 44% or $2.63 billion across rural Indonesia between 2001 and 2014. In addition, we explore the mechanisms underlying the productivity loss and find that biological pest control is the most plausible explanation.

Humid tropical forest monitoring with EO is limited by frequent cloud cover and rapid forest regrowth. Both can be overcome by using temporally dense SAR image stacks. We present a method that uses the coefficient of variation of multi-temporal SAR data stacks to map tropical forest disturbances. The SAR data pre-processing and the forest change detection workflows are described and illustrated. The method is tested at a humid tropical forest site in the Republic of Congo. At this test site we use data from three different SAR sensors: ALOS PALSAR, Sentinel-1 and TerraSAR-X. The forest disturbance maps are validated by visual interpretation and compared to the Landsat based Humid Tropical Forest Disturbance Alerts available from Global Forest Watch. Change mapping accuracies for plots larger than 0.5 ha are very high: 76% for ALOS PALSAR, 96% for TerraSAR-X and 98% for Sentinel-1. For Sentinel-1, producer accuracies were derived for different forest disturbance types. The overall accuracy is 81.8%, with highest values for deforestation in oil palm plantations and burnt areas. The results are similar to the accuracy of the Humid Tropical Forest Disturbance Alert layer, which detects 85.6% of all reference areas. We also show that fusion of the disturbance maps on a result level is possible. The presented method could be adapted to near real-time processing and to a combined processing with optical EO data.

This study uses fractal analysis to quantify the spatial changes of forest resources caused by an increase of deforested areas. The method introduced contributes to the evaluation of forest resources being under significant pressure from anthropogenic activities. The pressure on the forest resources has been analyzed for Maramures, County, one of the most deforested counties in Romania. In order to evaluate this, the deforested areas were calculated for the period of 2001-2014, by using the Global Forest Change 2000-2014 database. The Fractal Fragmentation Index (FFI) and Fixed Grid 2D Lacunarity (FG2DL) were used to quantify the degree of fragmentation and dispersion of the forested areas, and thereby the extent to which a forest area is affected by deforestation. The process of quantifying the pressure on forested areas included the creation of a database for the period of 2000-2014 containing economic activities (turnover) related to woody recourses, important indicators of forest exploitation. Taken together, the results obtained indicate a dramatic increase in deforested areas (over 19,122 ha in total for the period of analysis), in Maramures, County.

The central role of forests in climate change mitigation, as recognized in the Paris agreement, makes it increasingly important to develop and test methods for monitoring and evaluating the carbon effectiveness of REDD+. Over the last decade, hundreds of subnational REDD+ initiatives have emerged, presenting an opportunity to pilot and compare different approaches to quantifying impacts on carbon emissions. This study (1) develops a Before-After-Control-Intervention (BACI) method to assess the effectiveness of these REDD+ initiatives; (2) compares the results at the meso (initiative) and micro (village) scales; and (3) compares BACI with the simpler Before-After (BA) results. Our study covers 23 subnational REDD+ initiatives in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam. As a proxy for deforestation, we use annual tree cover loss. We aggregate data into two periods (before and after the start of each initiative). Analysis using control areas ('control-intervention') suggests better REDD+ performance, although the effect is more pronounced at the micro than at the meso level. Yet, BACI requires more data than BA, and is subject to possible bias in the before period. Selection of proper control areas is vital, but at either scale is not straightforward. Low absolute deforestation numbers and peak years influence both our BA and BACI results. In principle, BACI is superior, with its potential to effectively control for confounding factors. We conclude that the more local the scale of performance assessment, the more relevant is the use of the BACI approach. For various reasons, we find overall minimal impact of REDD+ in reducing deforestation on the ground thus far. Incorporating results from micro and meso level monitoring into national reporting systems is important, since overall REDD+ impact depends on land use decisions on the ground.

Satellite remote sensing assessment provides a systematic and object means to quantify the rate and form of forest changes. The main objective of this study was to quantify the forest change and forest degradation in one of the oldest forests of Iran named Jangal-Abr Forest at the north of Shahrood city. Based on Landsat-TM and OLI images acquired from 1987 to 2013, four vegetation and soil fraction images were produced and compared to map the land-cover changes occurred over 26 years. Spectral mixture analysis (SMA) method was applied to estimate fraction images of the selected components over the study area. This technique examines the direction of physical changes between multi-temporal images. Then, change vector analysis (CVA) approach was used for detecting forest changes. Results revealed that during the last 26 years, 147.5 km2 of forests were degraded mostly with medium severity. Part of the deforestation related to the northern and eastern regions of the study area in the vicinity of residential areas in contrast, forest regrowth area during the study period was estimated to be 62 km2 typically classified with low and medium severity in the eastern parts of the study area. CVA demonstrates that deforestation in the Jangal-Abr area has significantly accelerated from 4.6 km2/year in period 1987–2000 to about 9.6 km2/year in period 2010–2013. The rate of regrowth has been decreased gradually from 3.3 km2/year in period 1 (1987–2000) to about 0.76 km2/year in period 3 (1987–2000). By comparison the results of SMA method with Normalized Difference Vegetation Index (NDVI) changes method, it was found that except for some negligible differences, the results of both methods was very closed in terms of both spatially and the area of degradation and regrowth regions.

Annual forest cover loss indicator maps for the humid tropics from 2000 to 2005 derived from time-series 500 m data from the MODerate Resolution Imaging Spectroradiometer (MODIS) were compared with annual deforestation data from the PRODES (Amazon Deforestation Monitoring Project) data set produced by the Brazilian National Institute for Space Research (INPE). The annual PRODES data were used to calibrate the MODIS annual change indicator data in estimating forest loss for Brazil. Results indicate that MODIS data may be useful in providing a first estimate of national forest cover change on an annual basis for Brazil. When directly compared with PRODES change at the MODIS grid scale for all years of the analysis, MODIS change indicator maps accounted for 75% of the PRODES change. This ratio was used to scale the MODIS change indicators to the PRODES area estimates. A sliding threshold of percent PRODES forest and 2000 to 2005 deforestation classes per MODIS grid cell was used to match the scaled MODIS to the official PRODES change estimates, and then to differentiate MODIS change within various sub-areas of the PRODES analysis. Results indicate significant change outside of the PRODES-defined intact forest class. Total scaled MODIS change area within the PRODES historical deforestation and forest area of study is 120% of the official PRODES estimate. Results emphasize the importance of synoptic monitoring of all forest change dynamics, including the cover dynamics of intact humid forest, regrowth, plantations, and cerrado tree cover assemblages. Results also indicate that operational MODIS-only forest cover loss algorithms may be useful in providing near-real time areal estimates of annual change within the Amazon Basin.

By the middle of the 19th century, nearly all of central Massachusetts, USA, was cut at least once and 80% deforested. When did deforestation begin? We recorded all the trees and other corner markers used in 18th century proprietors' surveys of Shrewsbury, MA. From 1718–1799, the percentage of corner markers that were “trees” declined from 45% to 20%, and the relative abundance of white oak tended to decrease. Unexpectedly, the relative abundance of white pine more than doubled. Overall, deforestation and alteration of species composition were already occurring in the 18th century due to changing conditions, selective harvest, and differences in the resilience of the dominant trees. Proprietors' records can provide data on forest change as well as forest composition.

This study aims to assess and quantify forest transition within and around one of the few remaining forest protected landscapes in south west Nigeria, Okomu forest reserve. The study utilised multi-temporal Landsat and UK-DMC-2 satellite images over three epochs (1987, 1999 and 2011) to assess forest landscape change over the study area. The unsupervised Iterative Self Organising Data Analysis technique was used to generate forest maps and subsequently used for forest change detection over two periods (1987 – 1999 and 1999 – 2011). From the results generated we were able to determine the effectiveness level of forest protected status assigned the study area as a means of reducing deforestation from 1987 to 2011. To achieve this objective, trends of forest change within and around the reserve were investigated. These result showed that forest protected status assigned to the reserve has not fully mitigated the effects of deforestation within and around the reserve. The annual deforestation rates of the reserve increased from 3.5% in period 1 (1987 – 1999) to 5.1% in period 2 (1999 – 2011). We suggest that government needs to review forest policies and laws and improve upon the technical capacity of forest managers to improve forest management. Overall, the study has demonstrated the usefulness of using remote sensing and geographic information system to better understand dynamics of forest cover transition in forest protected areas across tropical forests.