•Forest cover change processes were analysed in a protected landscape.•Machine learning image classification was implemented.•Agricultural land increased more than the forest cover and the developed ...land.•Agricultural land targeted the forest cover to a greater extent.•Forest policy should incorporate the monitoring of multiple land change factors.
Forest cover change is a major contributing factor to global environmental change. Whereas several studies have focused on the general land use and land cover dynamics, we focus on analysing forest cover change patterns in a protected landscape taking into consideration how other land categories are increasing at the expense of the forest. In this study, we analyse forest cover change patterns and associated proximate land use factors between 1987 and 2017 using Landsat images from the Tano-Offin Forest Reserve (TOFR) in Ghana. Using the Random Forest machine learning algorithm, we classified the images into forest, developed land, and agricultural land. The study finds that forest cover losses are 1.9 and 1.4 times the amount of forest cover gains in 1987–2002 and 2002–2017, respectively. We find that even though the forest cover is more likely to recover from the agricultural land, land developers mostly targeted the agricultural land. The focus of Ghana's Forest and Wildlife Policy and the underlying process of forest cover change in the TOFR suggest that a country's forest policy should focus on a combination of diverse and spatially explicit proximate factors that are likely to threaten the integrity of forests.
Forest cover and forest cover loss for the last decade, 2000–2010, have been quantified for the Democratic Republic of the Congo (DRC) using Landsat time-series data set. This was made possible via ...an exhaustive mining of the Landsat Enhanced Thematic Mapper Plus (ETM+) archive. A total of 8881 images were processed to create multi-temporal image metrics resulting in 99.6% of the DRC land area covered by cloud-free Landsat observations. To facilitate image compositing, a top-of-atmosphere (TOA) reflectance calibration and image normalization using Moderate Resolution Imaging Spectroradiometer (MODIS) top of canopy (TOC) reflectance data sets were performed. Mapping and change detection was implemented using a classification tree algorithm. The national year 2000 forest cover was estimated to be 159,529.2thousand hectares, with gross forest cover loss for the last decade totaling 2.3% of forest area. Forest cover loss area increased by 13.8% between the 2000–2005 and 2005–2010 intervals, with the greatest increase occurring within primary humid tropical forests. Forest loss intensity was distributed unevenly and associated with areas of high population density and mining activity. While forest cover loss is comparatively low in protected areas and priority conservation landscapes compared to forests outside of such areas, gross forest cover loss for all nature protection areas increased by 64% over the 2000 to 2005 and 2005 to 2010 intervals.
► Forest cover and loss from 2000 to 2010 have been quantified using Landsat data. ► We used an automatic image processing system to create multi-temporal metrics. ► Forest cover loss was analyzed at the national level and within protected areas. ► Gross forest cover loss from 2000 to 2010 totaling 2.3% of year 2000 forest area.
Forest certification is a voluntary conservation tool that aims to promote sustainable forest management. While research on forest certification has increased recently, there remains a significant ...gap in understanding how and to what extent certification can promote forest conservation.
Mediterranean cork oak open woodlands are ecosystems of high conservation and socio-economic value. However, these ecosystems are threatened by increased adult oak mortality and regeneration failure, often due to inadequate management and the rise of pests and diseases, aggravated by climate change.
Forest certification prescribes management practices intended to enhance tree regeneration and maintain stand health conditions. Therefore, it is anticipated that forest certification could mitigate the observed decline of oak trees in Mediterranean regions. Here, we investigate whether forest certification contributes to the ecological sustainability of Mediterranean cork oak open woodlands in Portugal. We compare the stand biometrics of non-certified and certified cork oak stands before and after certification implementation, using both National Forest Inventory data and field sampling from 2005 and 2020.
Our findings indicate that the density of adult oak trees decreased by 16 % in certified estates and 28 % in non-certified estates between 2005 and 2020. Similarly, cork oak cover declined by 6 % tree cover in certified plots and 19 % in non-certified plots during the same period. Consequently, by 2020, tree density was 20 % higher in certified stands than in the non-certified ones, and tree cover was 36 % higher in certified stands. Tree diameter and height increased at similar rates in both certified and non-certified stands from 2005 to 2020.The age structure of the stands also remained consistent, showing a bell-shaped distribution of tree diameters in both years. However, results on oak regeneration were inconclusive.
Our results suggest that cork oak decline, measured by the changes in density and cover of adult trees from 2005 to 2020, is slower in certified cork oak woodlands. Nonetheless, the increase in tree diameter and the age structure shape indicate potential regeneration issues in both certified and non-certified stands, needing further measures to address the aging of cork oak open woodlands.
•Cork oak tree density decreased between 2005 and 2020 throughout the study area.•The decrease in cork oak density was greater in non-certified stands than in certified stands.•In 2020, certified cork oak stands had a higher tree density and cover.•Certification is contributing to mitigating the decline of cork oak woodlands.
•An insect epidemic occurred in North-Western Russia from 2001 to 2014.•We analyzed the subsequent annual tree loss caused by bark beetles.•Summer temperatures (June) were the most important ...driver.•High previous-year temperature increased tree mortality.•A longer sunshine duration also contributed to tree loss.
Acute or chronic drought stress caused by climate change can contribute to the weakening of forest ecosystems and lead to extensive bark beetle infestations. Siberian spruce (Picea obovata Ledeb.) forests of the Dvinsko-Pinegskiy, a natural reserve in the Arkhangelsk region, Russia, have been subject to unprecedented tree cover loss caused by the Eurasian spruce bark beetle (Ips typographus L.) in the last two decades. This is the first recorded case of such an extensive outbreak of Ips typographus occurring at higher latitudes. We used remote sensing and climate data to model and compute annual tree-loss change due to natural factors, with a focus on bark beetle outbreaks, over a 14-year period (2001–2014). Usinglinear regression models, we found a combination of average annual temperature and precipitation, temperature and precipitation in June, to be the most important drivers of annual tree-loss.
The protection of forests is crucial to providing important ecosystem services, such as supplying clean air and water, safeguarding critical habitats for biodiversity, and reducing global greenhouse ...gas emissions. Despite this importance, global forest loss has steadily increased in recent decades. Protected Areas (PAs) currently account for almost 15% of Earth's terrestrial surface and protect 5% of global tree cover and were developed as a principal approach to limit the impact of anthropogenic activities on natural, intact ecosystems and habitats. We assess global trends in forest loss inside and outside of PAs, and land cover following this forest loss, using a global map of tree cover loss and global maps of land cover. While forests in PAs experience loss at lower rates than non-protected forests, we find that the temporal trend of forest loss in PAs is markedly similar to that of all forest loss globally. We find that forest loss in PAs is most commonly-and increasingly-followed by shrubland, a broad category that could represent re-growing forest, agricultural fallows, or pasture lands in some regional contexts. Anthropogenic forest loss for agriculture is common in some regions, particularly in the global tropics, while wildfires, pests, and storm blowdown are a significant and consistent cause of forest loss in more northern latitudes, such as the United States, Canada, and Russia. Our study describes a process for screening tree cover loss and agriculture expansion taking place within PAs, and identification of priority targets for further site-specific assessments of threats to PAs. We illustrate an approach for more detailed assessment of forest loss in four case study PAs in Brazil, Indonesia, Democratic Republic of Congo, and the United States.
Extreme events, such as extreme droughts and intense temperatures, have become more frequent and severe, contributing to increased mortality rates in Norway spruce (Picea abies) due to bark beetle ...attacks. In particular, the most devastating outbreak of the spruce bark beetle in Central Europe began after the extreme drought year of 2018. This drought event also corresponds to the peak of the outbreak of the Eurasian spruce bark beetle (Ips typographus L.) in the study area - the School Forest Enterprise in the area surrounding the town of Kostelec nad Černými Lesy (Czech Republic). The study covers the period from 2012 to 2022, when there was a significant tree cover loss of 845.4 ha (15% of the spruce dominated area) that was caused by bark beetle. The primary objectives of the study were to identify the key meteorological variables affecting annual tree cover loss, bark beetle damage spot initiation, and spreading. We used the Global Forest Change dataset and meteorological data from the nearest weather station. The predictor variables were modelled in two ways: Generalised Additive Models (GAMs) regression and ridge regression. The study found that different climatic variables influenced the initialisation and spreading of bark beetle infestations. The most important climatic factor for initialisation is the duration of solar radiation in April of the previous year. The average annual air temperature in the current year plays an important role in the spreading of bark beetle spots. The higher area of spot initialisation occurred in the initial beetle outbreak phase, while the area of spreading of bark beetle spots started to increase at the peak, and was higher in later phases. Regarding annual tree cover loss, the most important factors are the duration of solar radiation in June and September of the current year, as well as the average annual precipitation of the previous year.
•Identified pattern of spreading bark beetle outbreak.•Meteorological variables are critical for beetle spread.•Analysed Ips typographus outbreaks in the Czech Republic.•Analyzed using Generalised Additive Models and Ridge regression.
The remote sensing science and application communities have developed increasingly reliable, consistent, and robust approaches for capturing land dynamics to meet a range of information needs. ...Statistically robust and transparent approaches for assessing accuracy and estimating area of change are critical to ensure the integrity of land change information. We provide practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data. The good practice recommendations address the three major components: sampling design, response design and analysis. The primary good practice recommendations for assessing accuracy and estimating area are: (i) implement a probability sampling design that is chosen to achieve the priority objectives of accuracy and area estimation while also satisfying practical constraints such as cost and available sources of reference data; (ii) implement a response design protocol that is based on reference data sources that provide sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the “reference classification” will be considerably more accurate than the map classification being evaluated); (iii) implement an analysis that is consistent with the sampling design and response design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of change such as wetland loss or types of persistence such as stable forest) based on the reference classification of the sample units; (vi) quantify uncertainty by reporting confidence intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the reference classification; and (viii) document deviations from good practice that may substantially affect the results. An example application is provided to illustrate the recommended process.
•Provides good practice recommendations for accuracy assessment and area estimation•Recommendations are a synthesis of existing methods.•Recommendations satisfy accepted scientific practice.•Recommendations address sampling design, response design and analysis.•An example illustrating recommended workflow is included.
The combination of its equatorial location and number of islands of Indonesia provides numbers of biomass storage capability, bioenergy resources and abundant natural wealth of forest, while known as ...home to the world's third largest tropical forest. Forest loss can cause many adverse impacts on ecosystem balance and biodiversity by changing the configuration and structure of the forest landscape. Over the last twenty years, the deforestation in Indonesia keeps increasing with uneven distribution between the western and eastern part of the country. Consistent and efficient observation of forest loss using remote sensing data provides important information for understanding the role of forest in the role of forest conversion in the global carbon cycle, biodiversity analysis, and selecting potential strategies on reducing deforestation. In this study, a Global Forest Change Datasets were utilized to analyze, calculate, and map forest loss in Papua, Indonesia from 2001 to 2021. Cloud data processing is carried out using the google earth engine to obtain the total forest loss data in district scale of Papua Province. Three indicators used in this method, the tree cover in the year of 2000 as the forest baseline, yearly forest gain (2000 - 2012), and yearly forest loss (2001 - 2021). The results of the study show that in the last two decades, Papua Province has lost 2,806% or 9.062 km2 of its forest cover area, while the semi-urban and urban areas are 2-20 times more prone to deforestation compared to the dense forest area. Although increasing each year, Papua Province has a very low threat of deforestation compared to other provinces in Indonesia. However, the anthropogenic conversion to the productive plantation site might become a significant hazard in the upcoming years, not only in urban and semi-urban area, but also in remote and dense forest areas.
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