Hectares Of Forest Research Articles

Innovation Perspectives for the Bioeconomy of Non-Timber Forest Products in Brazil

Brazil has great potential for the development of an NTFP bioeconomy as it has 500 million hectares of forest. In this article, I seek to identify, through a literature review, innovations in products and processes inserted in the value chain of Brazilian NTFPs with a greater productive value. The hypothesis is that the prospects for the development of the bioeconomy of NTFPs depend on the establishment of a series of innovations along the value chain and in public policies. The production value of NTFPs reached USD 365 million in 2020 and the main NTFPs were açaí, yerba mate, carnaúba pode, Brazil nut, babassu, and pequi. I observed that the products with the highest production value developed innovations in the cultivation of species and in the development of new products. Innovations related to social and commercial organization have been developed within the scope of working in networks. Several policies were implemented based on the construction of a collective concept for the activity related to the NTFPs, sociobiodiversity. Even so, these actions need to be continued and strengthened for the transition to an inclusive, sustainable bioeconomy that takes into account traditional knowledge. Investment in research needs to be constant for the development of new products. Sustainable planting in diversified systems can also be considered an important strategy. Partnerships between the government and the various actors in the value chains are necessary and urgent to ensure innovations, also in the regulatory and organizational environment of NTFPs’ value chains.

Is there more to trees than timber? Estimating the private amenity value of forests using a hedonic land model for combined agricultural properties

Forests offer a range of amenity values besides the production value of timber. We know from existing studies, that the trees' height and species affect the public's recreational values, but it is less clear how much the joy of owning a property with forest matters, and whether the joy depends on characteristics like tree height and species.In this paper, we estimate landowners' private amenity value of forests. We do this by estimating four hedonic land value models for different sizes of property. In the models, forests are described by the size of forest area with a canopy height above three meters, the size of forest area with a canopy height below three meters, mean canopy height, and the share of broadleaf forest.The value of an additional hectare of forest is predicted using the hedonic model and compared with the forest's expectation value based on accounting statistics for forest income, including hunting rents, discounted using an average real market interest rate of 1.7%. We find a difference of 6500–9900 EUR/ha between the estimated land value models and the production values for properties larger than 10 ha. We interpret this difference as the forest owner's willingness to pay for forest amenities in addition to the timber value. The results offer insight into the value of owning forests for the large majority of private forest owners who own small patches of land. They can be valuable in the design of land-use policies aiming to increase forest area and ecosystem service provision on private land that benefits both the public and the private owner.

Monitoring early-successional trees for tropical forest restoration using low-cost UAV-based species classification

Logged forests cover four million square kilometers of the tropics, capturing carbon more rapidly than temperate forests and harboring rich biodiversity. Restoring these forests is essential to help avoid the worst impacts of climate change. Yet monitoring tropical forest recovery is challenging. We track the abundance of early-successional species in a forest restoration concession in Indonesia. If the species are carefully chosen, they can be used as an indicator of restoration progress. We present SLIC-UAV, a new pipeline for processing Unoccupied Aerial Vehicle (UAV) imagery using simple linear iterative clustering (SLIC)to map early-successional species in tropical forests. The pipeline comprises: (a) a field verified approach for manually labeling species; (b) automatic segmentation of imagery into “superpixels” and (c) machine learning classification of species based on both spectral and textural features. Creating superpixels massively reduces the dataset's dimensionality and enables the use of textural features, which improve classification accuracy. In addition, this approach is flexible with regards to the spatial distribution of training data. This allowed us to be flexible in the field and collect high-quality training data with the help of local experts. The accuracy ranged from 74.3% for a four-species classification task to 91.7% when focusing only on the key early-succesional species. We then extended these models across 100 hectares of forest, mapping species dominance and forest condition across the entire restoration project.

Lidar-Imagery Fusion Reveals Rapid Coastal Forest Loss in Delaware Bay Consistent with Marsh Migration

Tidal wetland ecosystems and their vegetation communities are broadly controlled by tidal range and inundation frequency. Sea-level rise combined with episodic flooding events are causing shifts in thresholds of vegetation species which reconstructs the plant zonation of the coastal landscape. More frequent inundation events in the upland forest are causing the forest to convert into tidal marshes, and what is left behind are swaths of dead-standing trees along the marsh–forest boundary. Upland forest dieback has been well documented in the mid-Atlantic; however, reliable methods to accurately identify this dieback over large scales are still being developed. Here, we use multitemporal Lidar and imagery from the National Agricultural Imagery Program to classify areas of forest loss in the coastal regions of Delaware. We found that 1197 ± 405 hectares of forest transitioned to non-forest over nine years, and these losses were likely driven by major coastal storms and severe drought during the study period. In addition, we report decreases in Lidar-derived canopy height in forest loss areas, suggesting forest structure changes associated with the conversion from forest to marsh. Our results highlight the potential value of integrating Lidar-derived metrics to determine specific forest characteristics that may help predict future marsh migration pathways.

Fire Dynamics of the Bolivian Amazon

This study identifies the spatial and temporal trends, as well as the drivers, of fire dynamics in the Bolivian Amazon basin. Bolivia ranks in the top ten countries in terms of total annual burnt, with fires affecting an estimated 2.3 million hectares of forest in 2020. However, in comparison to the Brazilian Amazon, there has been little research into the fire regime in Bolivia. The sparse research and the limited literature on the subject indicate that fire activity is higher in the Bolivian Amazon basin’s dry forests and flooded savanna zones, and that agriculture and drought are the primary causes of fire activity. In this study, trend analysis and emerging hotspot analysis are deployed to identify the spatial and temporal patterns of fire activity and boosted regression tree models to identify the drivers of forest fire within each ecoregion of the Bolivian Amazon basin. Comparable to most of the Brazilian literature, this study finds that fire activity and fire season length is higher in the flooded Beni Savanna, and Chiquitano seasonally dry tropical forests than in the Bolivian Amazon ecoregion. This study also identifies moisture stress and human activity as the main drivers of fire dynamics within the region. It is intended that this research will offer a foundation for future research and conservation activities aimed at better understanding the fire regime of the Bolivian Amazon basin.

Trees planted under a global restoration pledge have mixed futures under climate change

Nations worldwide have committed to restoring millions of hectares of forest as a strategy to mitigate climate change with many other co‐benefits. Paradoxically, the suitability of climatic conditions for the trees being planted at the restoration sites is changing, which may reduce the long‐term viability of these projects. We assessed the potential future viability of trees planted as part of Ecuador's National Reforestation Plan from 2014 to 2017, committed under the global Bonn Challenge. We selected the 10 most frequently planted tree species (all native) in 1237 restoration sites in northwest Ecuador. We modeled the species' climatic suitability at the restoration sites under Representative Concentration Pathways (RCP) 4.5 and 8.5 using 14 individual general circulation models for the 2030s, 2050s, and 2070s. We combined the 14 suitability models to create continuous climatic suitability models for each species. We then assessed the species suitability against three modeling thresholds simultaneously. Seven of the species showed high and medium climatic viability, all of these species were native to the region of planting. Three species showed low climatic viability; two of these are native to the country but not to the region being restored. Our results also show how using continuous rather than discrete modeling can affect viability assessments. Our study suggests the importance of including climate niche modeling in restoration projects; however, choosing to plant species that are within their native distribution range may be a good strategy in tropical regions if climate niche projections are not available.

Climate legacies drive the distribution and future restoration potential of dryland forests.

Knowing the extent and environmental drivers of forests is key to successfully restore degraded ecosystems, and to mitigate climate change and desertification impacts using tree planting. Water availability is the main limiting factor for the development of forests in drylands, yet the importance of groundwater resources and palaeoclimate as drivers of their current distribution has been neglected. Here we report that mid-Holocene climates and aquifer trends are key predictors of the distribution of dryland forests worldwide. We also updated the global extent of dryland forests to 1,283 million hectares and showed that failing to consider past climates and aquifers has resulted in ignoring or misplacing up to 130 million hectares of forests in drylands. Our findings highlight the importance of a wetter past and well-preserved aquifers to explain the current distribution of dryland forests, and can guide restoration actions by avoiding unsuitable areas for tree establishment in a drier world.

Suitability of the global forest cover change map to assess climatic megadisturbance impacts on remote tropical forests

The occurrence and combination of extreme meteorological events may lead to severe (megadisturbance) impacts on conserved forests and protected areas all over the world. Recent research has shown that megadisturbance impacts (after the events of hurricanes and prolonged drought) may be detected in subtropical forest cover using changes in spectral indices derived from satellite imagery. The objective of this study is to assess the impact of megadisturbance on forest types of the Alejandro de Humboldt National Park, Republic of Cuba in the 2001–2017 time-period. The Global Forest Cover Change (GFCC, available on the Global Forest Watch website) product was validated and indicated the prominence of megadisturbance for year 2016 (85% of the total disturbed area), largely associated with the Hurricane Matthew event. A robust estimator of the disturbed forest area, based on GFCC-stratified sampled verification sites, suggests that 11,110 ± 1,771 hectares of forest (~ 16% of the Park’s total area) was affected by megadisturbance between 2001 and 2017. In 2017, about 1276 hectares of forests were impacted, presumably related to a long-lasting effect of megadisturbance due to Hurricane Matthew and prolonged droughts in previous years. Four types of tropical rainforests (especially lowland rainforest and submountainous sclerophyllous rainforest on serpentinite), that cover 43% of the National Park, accounted for about 85% of the impacts by megadisturbance. The Easternmost portion of these forests should be prioritized for conservation monitoring and possibly for forest restoration strategies. This study contributes to establishing methodological guidelines for rapid environmental assessment of remote, tropical protected areas facing the impacts of extreme meteorological events and climate change.

Sea level rise drives carbon and habitat loss in the U.S. mid-Atlantic coastal zone

Coastal marshes and seagrass beds store millions of tons of carbon in their sediments and sequester carbon at higher per-area rates than most terrestrial ecosystems. There is substantial interest in this “blue carbon” as a carbon mitigation strategy, despite the major threat that sea level rise (SLR) poses to these habitats. Many projections of habitat and carbon change with SLR emphasize the potential for inland marsh migration and increased rates of marsh carbon sequestration, but do not consider carbon fluxes associated with habitat conversion. We integrated existing data and models to develop a spatial model for predicting habitat and carbon changes due to SLR in six mid-Atlantic U.S. states likely to face coastal habitat loss over the next century due to low tidal ranges and sediment supply. Our primary model projection, using an intermediate SLR scenario (1.2 m SLR by 2104), predicts loss of 83% of existing coastal marshes and 26% of existing seagrasses in the study area. In addition, 270,000 hectares of forest and forested wetlands in low-lying coastal areas will convert to coastal marshes. These SLR-driven habitat changes cause the study area to shift from a carbon sink to a source in our primary model projection. Given the many uncertainties about the habitat and carbon changes represented in our model, we also identified the parameters and assumptions that most strongly affected the model results to inform future research needs. These included: land availability for inland marsh migration, the baseline extent and location of coastal marshes, proportion of stored carbon emitted from lost habitats (coastal marsh sediments or terrestrial biomass carbon), and methane emissions from freshwater habitats. The study area switched from a net carbon sink to a net carbon source under SLR for all but three model runs; in those runs, net carbon sequestration declined by 57–99%.

Acoustic Wave Based Forest Fire Extinguisher and Detection using Deep Learning

Apart from causing tragic loss of lives and valuable natural and individual properties including thousands of hectares of forest and hundreds of houses, forest fires are a great menace to ecologically healthy grown forests and protection of the environment. Every year, thousands of forest fire across the globe cause disasters beyond measure and description. This issue has been the research interest for many years; there are a huge amount of very well studied solutions available out there for testing or even ready for use to resolve this problem. Forest and urban fires have been and still are serious problem for many countries in the world. Currently, there are many different solutions to detect the forest fires. People are using sensors to detect the fire. But this case is not possible for large acres of forest. In this paper, we discuss a new approach for fire detection, in which modern technologies are used. In particular, we propose a platform that Artificial Intelligence. The computer vision methods for recognition and detection of smoke and fire, based on the still images or the video input from the cameras. Deep learning method “convolution neural network “for finding the amount of smoke and fire. The accuracy is based on the algorithm which we are going to use and the datasets and splitting them into train set and test set.

Temperature and Rainfall Patterns Constrain the Multidimensional Rewilding of Global Forests.

The long‐term contribution of global forest restoration to support multiple dimensions of biodiversity and ecosystem function remains largely illusive across contrasting climates and forest types. This hampers the capacity to predict the future of forest rewilding under changing global climates. Here, 120 studies are synthesized across five continents, and it is found that forest restoration promotes multiple dimensions of biodiversity and ecosystem function such as soil fertility, plant biomass, microbial habitat, and carbon sequestration across contrasting climates and forest types. Based on global relationship between stand age and soil organic carbon stock, planting 350 million hectares of forest under the UN Bonn Challenge can sequester >30 Gt soil C in the surface 20 cm over the next century. However, these findings also indicate that predicted increases in temperature and reductions in precipitation can constrain the positive effects of forest rewilding on biodiversity and ecosystem function. Further, important tradeoffs are found in very old forests, with considerable disconnection between biodiversity and ecosystem function. Together, these findings provide evidence of the importance of the multidimensional rewilding of forests, suggesting that on‐going climatic changes may dampen the expectations of the positive effects of forest restoration on biodiversity and ecosystem function.

Prediction, validation, and uncertainties of a nation-wide post-fire soil erosion risk assessment in Portugal

Wildfires are a recurrent and increasing threat in mainland Portugal, where over 4.5 million hectares of forests and scrublands have burned over the last 38 years. These fire-affected landscapes have suffered an intensification of soil erosion processes, which can negatively affect soil carbon storage, reduce fertility and forest productivity, and can become a source of pollutants. The main objective of the present study is to produce a post-fire soil erosion risk map for the forest and shrubland areas in mainland Portugal and assess its reliability. To this end, the semi-empirical Morgan–Morgan–Finney erosion model was used to assess the potential post-fire soil erosion according to distinct burn severity and climate scenarios, and the accuracy of the predictions was verified by an uncertainty analysis and validated against independent field datasets. The proposed approach successfully allowed mapping post-fire soil erosion in Portugal and identified the areas with higher post-fire erosion risk for past and future climate extremes. The outcomes of this study comprise a set of tools to help forest managers in their decision-making for post-fire emergency stabilization, ensuring the adequate selection of areas for mitigation to minimize the economic and environmental losses caused by fire-enhanced soil erosion.

A Value Chain Approach to Forest Landscape Restoration: Insights from Vietnam’s Production-Driven Forest Restoration

According to FAO figures, over 129 million hectares of forests have been lost globally since the 1990s, mainly in the tropical Global South, where agriculture acts as the main driver of forest conversion. International commitments, such as the Bonn Challenge, aim to reverse this trend through the application of forest landscape restoration (FLR) as an integrated and inclusive restoration approach. Beyond the discourse level, however, FLR implementation lags behind expectations due to insufficient funding and a disconnection with local implementation. We argue that, instead of relying on public resources for conservation-driven restoration, increased private sector engagement may point the way out of the funding impasse. However, this requires a shift towards production-driven FLR, which includes the livelihood needs of communities and smallholders as agents of landscape transition. For achieving the dual purpose of connecting landscapes with markets and promoting sustainable landscape restoration, we ascribe value chains and their economic, social and ecological configurations a key role in production-driven FLR. Drawing on Vietnam’s forest restoration pathway as an illustrative case, we examine how production-driven forest restoration, smallholder engagement and value chain upgrading can stimulate positive landscape transitions. We conclude that, depending on their configuration, value chains can have negative or positive social and ecological impacts at the landscape level. Furthermore, regulated, progressive and high-value commodity chains may perform better in the areas of integrated FLR objectives landscape integrity, ecological functionality and human well-being.

Embedded ANN-Based Forest Fire Prediction Case Study of Algeria

One of the major environmental challenges is forest fires, each year millions of hectares of forest are destroyed throughout the world, resulting in economic and ecological damages, as well as the loss of human life. Therefore, predicting forest fires is of great importance for governments; However, there is still limited study on this topic in Algeria. In this paper, we present an application of artificial neural networks to predict forest fires in embedded devices. We used meteorological data obtained from wireless sensor networks. In the experimentation, nine machine learning model are compared. The findings from this study make several contributions to the current literature. First, our model is suitable for embedded and real-time training and prediction. Moreover, it should provide better performances and accurate predictions against other models.

Slowing Deforestation in the Brazilian Amazon: Avoiding Legal Deforestation by Compensating Farmers and Ranchers

The Amazon Forest is an unquestionable cradle of planetary biological diversity and plays a fundamental role in regional and global climate change regulation. Annual deforestation rates in the Brazilian Amazon have gone up since 2012, presenting a grim scenario for 2021. The majority of this deforestation is illegal, but a significant proportion, 11.3 million hectares of forest in privately owned land, can be legally deforested and impact local climate, compromising Brazil’s Nationally Determined Contribution (NDC) to the Paris Agreement. Thus, a fundamental question remains: how can we avoid potentially legal deforestation? Farmers and ranchers who have the right to deforest have long demanded financial incentives to keep their forests standing, but few (if any) mechanisms exist to achieve it. We developed a compensation mechanism called CONSERV, hypothesizing that legal deforestation can be avoided through targeted compensation. CONSERV can potentially contribute to climate change mitigation and foster business models geared towards conservation and increased agricultural productivity when implemented at scale. We present CONSERV’s concept and potential to become an operational mechanism for Payment for Ecosystem Services/REDD + in line with Brazil’s Native Vegetation Protection Law and NDC commitment. Furthermore, we introduce some ideas on how CONSERV ensures permanence and minimizes leakage while gaining scale. To successfully maintain climatic stability and ecosystems, we need to reduce deforestation, both illegal and legal. CONSERV can help us solve the latter.

Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana). Part 1: Model description

Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clemens). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources.

Potential greenhouse gas emissions mitigation through increased grazing pressure: a case study in North Portugal

Wildfires have been an important process affecting forests and rangelands worldwide. In the Mediterranean region, wildfires burn about half a million hectares of forest and scrubland every year. Fuel loads are the main factor controlling fire risk and its propagation. The reduction of fuel loads by grazing could help to decrease the spread and intensity of wildfires in this region. This study aims to assess the contribution of sheep grazing on fuel load management and their role to the mitigation of wildfire greenhouse gas (GHG) emissions. The methodological approach is based on a simulation of the grazing pressure required to reduce a given quantity of fuel, under the assumption that if it is not consumed, it becomes fuel. Following, a simulation model was designed to estimate the total GHG emissions prevented through grazing, by reducing the risk of fire. These emissions were estimated based on the Intergovernmental Panel on Climate Change (IPCC) framework. The accumulated fuels were estimated to be 3126.65 kg dry matter (DM) ha−1 and the biomass potentially consumed by sheep was 1416.03 kg DM ha−1 yr−1, corresponding to 45.29% of accumulated fuel loads. Our findings suggest a value of 3.88 sheep ha−1 day−1 as the ideal to reduce 4833.63 kg CO2eq ha−1 yr−1 of emissions, distributed between CO2 (−2221.76 kg CO2eq ha−1 yr−1; 45.96%), NOx (−1873.41 kg CO2eq ha−1 yr−1; 38.76%), CO (−454.55 kg CO2eq ha−1 yr−1; 9.40%), CH4 (−186.35 kg CO2eq ha−1 yr−1; 3.86%) and N2O (−97.56 kg CO2eq ha−1 yr−1; 2%). The results of this study also underline that livestock can help to mitigate climate change in areas prone to wildfires.

GLOBAL WARMING AND PAKISTAN: LOCAL RESPONSE TO A GLOBAL CHALLENGE

GLOBAL WARMING AND PAKISTAN: LOCAL RESPONSE TO A GLOBAL CHALLENGE

Ecosystem dynamics after forest fire. Blideen Atlas case

Human disturbances affect the majority of terrestrial ecosystems. The radical changes in the behaviour of ecological systems, partial or total destruction of plant biomass, often with the death of fundamental entities. Of these disturbances, fires affect many terrestrial ecosystems, particularly forests, by changing their floristic composition, their structure and their functioning; the global average annual forest area burned is estimated at 65 million ha. The Mediterranean basin, a hotspot, annually loses between 0.5 and 1 million hectares of forest. In Algeria, the annual average of areas destroyed by fire is between 45,000 and 50,000 ha. Our work set out to study the changes in the plant coverage of the land one year after the passage of fire. We studied the effects at the floristic, ecological and dynamic level in a forest ecosystem located in the north of Algeria (Atlas Blideen). The phytoecological inventory of vegetation (74 surveys, 162 species) was carried out from subjective sampling. To highlight the different groupings in the study area, classical statistical treatments (factorial analysis of correspondences) were applied to the floristic and ecological data. The results of computer processing made it possible to individualize and classify four plant groups according to the degree of the fire. The qualitative and quantitative analysis of these groups shows a therophytization of the flora, due to the regression of the forest cover (disappearance of the phanerophytes) caused by the passage of fire, with a floral procession represented mainly by Asteraceae, Poaceae and Fabaceae and an index disturbance which greatly exceeds 50%. However, some tree and shrub taxa such as cork oak, holm oak and Pistacia lentiscus tree have the capacity to reappear by rejecting the calcined stumps.

Investigating the Correlation between Multisource Remote Sensing Data for Predicting Potential Spread of Ips typographus L. Spots in Healthy Trees

In the last decade, thousands of hectares of forests have been lost in the Czech Republic, primarily related to European spruce bark beetle (Ips typographus L.), while more than 50% of the remaining Czech forests are in great danger, thus posing severe threats to the resilience, stability, and functionality of those forests. The role of remote sensing in monitoring dynamic structural changes caused by pests is essential to understand and sustainably manage these forests. This study hypothesized a possible correlation between tree health status and multisource time series remote sensing data using different processed layers to predict the potential spread of attack by European spruce bark beetle in healthy trees. For this purpose, we used WorldView-2, Pléiades 1B, and SPOT-6 images for the period of April to September from 2018 to 2020; unmanned aerial vehicle (UAV) imagery data were also collected for use as a reference data source. Our results revealed that spectral resolution is crucial for the early detection of infestation. We observed a significant difference in the reflectance of different health statuses, which can lead to the early detection of infestation as much as two years in advance. More specifically, several bands from two different satellites in 2018 perfectly predicted the health status classes from 2020. This method could be used to evaluate health status classes in the early stage of infestation over large forested areas, which would provide a better understanding of the current situation and information for decision making and planning for the future.