Forest Carbon Emissions Research Articles

Global rise in forest fire emissions linked to climate change in the extratropics.

Climate change increases fire-favorable weather in forests, but fire trends are also affected by multiple other controlling factors that are difficult to untangle. We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls. This delineation revealed that rapidly increasing forest fire emissions in extratropical pyromes, linked to climate change, offset declining emissions in tropical pyromes during 2001 to 2023. Annual emissions tripled in one extratropical pyrome due to increases in fire-favorable weather, compounded by increased forest cover and productivity. This contributed to a 60% increase in forest fire carbon emissions from forest ecoregions globally. Our results highlight the increasing vulnerability of forests and their carbon stocks to fire disturbance under climate change.

Nilai Ekonomi Pengelolaan Hutan Lindung Blok Kawae KH. Maria/RTK 25 BKPH Maria Donggomasa Dinas LHK Provinsi NTB

One part of the Sari watershed, which was a strategic area for Bima City, was the Kawae Block protected forest area. This forest area acted as a catchment area for the Bima City Region and supports the lives of the people of Wawo, Bima Regency and its surroundings. In the last decade, the Kawae Block protected forest area has come under pressure from vulnerability in several ways, especially pressure from destruction due to illegal logging and encroachment on forest areas for farming of seasonal crops (corn). The aim of this research were to analyze the factors that directly cause damage and the economic value of damage as well as the economic value of potential natural resources in the Kawae Block Protected Forest Area. This research was conducted in the Kawae KH Block Protected Forest Area. Maria/RTK 25 BKPH Maria Donggomasa Provincial LHK Service. NTB with an area of 1,500 hectares. The sample used was 210 respondents. The variables used in this research were the price value of NTFPs and the price value of water to calculate the value of direct benefits of forest areas, ecological benefits, flood control and carbon emission reduction/carbon credits to calculate the value of indirect benefits of areas and the value of biodiversity to calculate the value of selected benefits. Data analysis was carried out for regional economic valuation using the total economic value valuation method (Total Economic Value). The research resulted in the factors that directly cause damage to the area; they were land burning, encroachment on areas for agricultural land for seasonal crops/corn, excessive community management, rosewood theft/illegal logging and opening of farming roads. Meanwhile, the total economic value of the HL Kawae Block area was IDR. 61,135,306,500,-. The economic value resulting from sustainable forest management is much more profitable than the results of cultivating seasonal crops (especially corn).

Mapping forest-based natural climate solutions

Natural climate solutions are critical actions of ecosystem stewardship to mitigate climate change. However, prioritizing locations and possible actions is challenging. We demonstrate a generalizable approach for identifying potential opportunities for natural climate solutions by creating a spatial hierarchy of land management restrictions. Global forest carbon stocks and flux models were then used to explore forest-based natural climate solutions in the high-carbon density coastal temperate rainforests of western North America. Our results show 13 million hectares are available for action, an area that holds 4,900 ± 640 megatonnes of carbon dioxide equivalent and represents 45% of regional and 0.5% of global aboveground forest carbon stocks. Based on historical trends, a 10% reduction in average annual forest carbon loss through improved forest management and conservation could reduce forest carbon emissions by 9.1 megatonnes of carbon dioxide equivalent per year, corresponding to 5.2% of the 2030 land-based climate commitments made by the United States and Canada. Large-scale implementation of natural climate solutions will require collaborative planning with forest-dependent communities, industry, governments, and Indigenous peoples.

A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights

Abstract Forests play a crucial role in mitigating the impacts of climate change by sequestering carbon in their biomass and soil. However, Ethiopia faces the threat of soil carbon emissions due to deforestation and continuous cultivation. This study reviewed the analogies in phytobiomass and soil carbon evaluation methods in Ethiopia. Index-base and year-wise analysis methods were used for the compilation of the study. Developing nations, such as Ethiopia, duly enhance resilient measures to assess forest carbon stocks for effective climate change mitigation, particularly with reference to emissions from deforestation and degradation. Even though more than 90% of Ethiopia’s energy comes from forest biomass, deforestation significantly affects the carbon stored in aboveground biomass, which is the largest reservoir. Estimating forest biomass and carbon emissions entails uncertainties, with error ranges around ±50% for aboveground pools and ±90% for soil carbon pools. Various tier methodologies are employed by experts to estimate forest biomass and carbon stock emissions, with Tier 2 factors serving as default emissions but country-specific factors offering improved accuracy over Tier 1. Tier 3 methodologies require highly specific inventory data on carbon stocks in different pools and common measurements of key carbon stocks. Forest conservation enhances biodiversity, ecosystem resilience, and essential ecosystem services, fostering soil health, regulating water cycles, and supporting diverse plant and animal species. In conclusion, directing efforts towards forest conservation not only helps maintain biodiversity and ecosystem services but also significantly contributes to mitigating climate change by enhancing carbon storage capacities and reducing greenhouse gas emissions.

Soil Aggregate Stability and Organic Carbon Content among Different Forest Types in Temperate Ecosystems in Northeastern China

Soil aggregates play a crucial role in substance and energy cycles in soil systems. The fixation of soil organic carbon (SOC) is closely tied to the safeguarding mechanisms of soil aggregates. Carbon fixation involves the conversion of atmospheric carbon dioxide into organic molecules by autotrophic organisms. Soil aggregates play a significant role in carbon stabilization, allowing for the physical occlusion of SOC. This study focuses on five forest types, Betula platyphylla, Betula dahurica, Quercus mongolica, Larix gmelinii, and mixed forests comprised of Larix gmelinii and Quercus mongolica, in the Heilongjiang Central Station Black-billed Capercaillie National Nature Reserve, northeast of China. This study investigated the soil aggregate stability (SAS) (water sieving) and aggregate-associated organic carbon (AAOC) at different soil depths in five forest types. Our findings demonstrated that fine macro-aggregates (0.25–2 mm) were the main types of soil aggregates among all the forest types. The SAS gradually decreased with increasing soil depth. Notably, broad-leaved forests exhibited relatively high soil stability. The fine macro-aggregates (0.25–2 mm) had the highest AAOC content, and the AAOC level was highest in the topsoil layer. The SAS and AOCC levels of the Betula platyphylla forest and Betula dahurica forest were higher than those of other forest types and were significantly affected by the forest type, soil depth, and soil physicochemical properties. Collectively, our findings reveal the key factors influencing aggregate stability and the variations in soil organic carbon content in different forest types. These observations provide a basis for studying the mechanisms of soil aggregate carbon sequestration, as well as for the sustainable development of forest soil carbon sequestration and emission reduction.

Contribution of Biogas Technology in Forest Conservation and Carbon Emission Reduction, Gimbi District, Western Ethiopia

Contribution of Biogas Technology in Forest Conservation and Carbon Emission Reduction, Gimbi District, Western Ethiopia

Decoupling REDD+ understanding of local stakeholders on the onset of materializing carbon credits from forests in Nepal

REDD+1 is an economic incentivizing mechanism aimed at reducing or offsetting of carbon emissions in forests, while realizing multiple benefits alongside climate action. Engaging local stakeholders is crucial for its sustainable implementation and benefit-sharing mechanism. This study focuses on the knowledge and understanding of local-level stakeholders about REDD+ and its associated attributes, revealing significant knowledge gaps between areas with and without REDD+ ​pilot activities. For this, we conducted the semi-structured questionnaire interviews (n ​= ​136), key informant interviews (n ​= ​27), and focus group discussions (n ​= ​4) with local-level REDD+ ​stakeholders (LLRS) comprising both inside and outside of pilot project districts in three provinces of Nepal, by adopting the concept of socio-ecological systems (SES). Data were analyzed using a generalized linear model (GLM) and visualized through Sankey diagrams. The results indicate a poor understanding (29%) of LLRS on the REDD+ ​process, its relationship with forests, concerns among stakeholders, and its potential significance. The perception of REDD+ ​knowledge, mechanisms, and benefits significantly (p ​< ​0.05) varied across study areas, age groups, genders, professional backgrounds, educational levels, ownership of private forests, and types of household energy sources used among respondents. Despite receiving readiness funds, stakeholders’ comprehension of the REDD+ ​process remains limited, indicating suboptimal policy implementation. Knowledge gaps were influenced by social background, voices and choices, and the fear of REDD+ ​disrupting traditional practices among the LLRS. The study emphasizes the need to redress the concerns of LLRS by considering their social backgrounds and traditional practices through informed and participatory decision-making, enhance communication, transparency, and inclusive forest governance. The findings show that current external support has not sufficiently enhanced capacity among LLRS, suggesting the need for sufficient and sustainable support through national policy and financing mechanisms. Further, the study identified extremely poor REDD+ ​-related knowledge dissemination within communities, exacerbating challenges in implementation and benefit-sharing mechanisms, revealing the simplification of its process is essential. The study advocates for revising REDD+ ​-related policies to optimize benefits, ensure smooth implementation, realize fair and equitable carbon credits from forests, and foster shared responsibility and ownership among all stakeholders in climate actions through improved forest governance.

Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product

Carbon emissions from forest fires release large amounts of carbon and have important implications for the global and regional carbon cycle and atmospheric carbon concentrations. Considering the significant spatial and temporal variations in different forest fires, this study explores the relationship between different forests and carbon emissions from forest fires. This study developed a high-resolution (0.05° × 0.05°) daily global inventory of carbon emissions from biomass burning during 2016–2022. The inventory estimates of carbon emissions from biomass burning are based on the newly released FY-3 data product, satellite and observational data of biomass density, and spatial and temporal variable combustion factors. Forest fire carbon emissions were assessed using active fire data from FY-3 series satellites from 2016 to 2022, and it was linearly compared with GFED, FEER, and GFAS data on time and spatial scales with R2 of 0.7, 0.73, and 0.69, respectively. The results show spatial patterns of forest cover and carbon emissions, with South America, Africa, South-East Asia, and northern Asia as high-emission zones. The analysis shows an overall upward trend in global forest fire carbon emissions over the study period. Different types of forests exhibited specific emission patterns and temporal variations. For example, most needleleaf forest fires occur in areas with low tree cover, while broadleaf forest fires tend to occur in areas with high tree cover. The study showed that there was a relationship between inter-annual trends in forest fire carbon emissions and land cover, with biomass burning occurring mainly in the range of 60–70% tree cover. However, there were also differences between evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, deciduous needleleaf forest, and mixed forest indicating the importance of considering differences in forest types when estimating emissions. This study identifies the main sources of carbon emissions from forest fires globally, which will help policymakers to take more targeted measures to reduce carbon emissions and provide a reliable basis for appropriate measures and directions in future carbon mitigation actions.

Assessing forest conservation outcomes of a nature reserve in a subtropical forest ecosystem: effectiveness, spillover effects, and insights for spatial conservation prioritization

Protected areas (PAs) are pivotal in the conservation of global forest ecosystems, and understanding their effectiveness and spillover effects is crucial to evaluate their overall performance. While PAs are well-documented for their role in reducing deforestation, research examining their influence on mitigating non-stand replacement forest degradation or forest carbon emissions remains insufficient. Additionally, the nuanced heterogeneity in the effectiveness and spillover effects of PAs are not yet fully understood, impeding our ability to leverage outcome-based evidence for crafting spatial conservation strategies to expand PAs. In this study, integrating a rigorous counterfactual analysis and remote sensing observations, we probed the effectiveness and spillover effects of the Gutianshan National Nature Reserve (GNNR), located in Kaihua County of China, in curbing forest degradation, forest loss, and forest carbon emissions. We also explored the heterogeneity of these effects and identified spatial conservation priorities based on nonuniform impacts of effectiveness. Our findings showed that the GNNR designation remarkably curtailed forest degradation, forest loss, and forest carbon emissions by 78.56 %, 95.54 %, and 97.01 %, respectively, while also extending these mitigating effects to adjacent areas. However, we also detected leakage effects, causing a displacement of forest carbon emissions in areas 5–15 km around the reserve. The effectiveness and spillover effects exhibited heterogeneous patterns across geographical conditions. Informed by this heterogeneity, we pinpointed areas showing high conservation suitability as spatial conservation priorities, covering 35.64 % of Kaihua County, thereby boosting future forest and carbon management efforts. Our results advance the understanding of PA effectiveness and spillover effects within low-altitude subtropical forest ecosystems and offer insights into integrating outcome-based and area-based conservation approaches to achieve the 30 × 30 conservation target at a regional scale.

Decoupling Analysis of Carbon Emissions and Forest Area in China from 2004 to 2020

As the largest ecological carbon sequestration systems on the Earth, forests play a significant role in reducing carbon dioxide, and countries around the world are actively expanding their forest areas. However, China’s carbon emissions and forest area have shown an upward trend, which has seriously hindered the implementation of forestry carbon sequestration projects. This paper analyzed the temporal variation, spatial distribution, and deviation degree of the forest area and carbon emissions in China from 2004 to 2020 by using a decoupling model and a coordination model. Firstly, according to the decoupling model, the national carbon emissions and forest area are negatively decoupled. At the provincial level, Beijing, Shanghai, Jiangsu, Guizhou, Yunnan, and Gansu have weak decoupling. Expansive link areas include Shanxi, Henan, Hubei, Ningxia, and Xinjiang. The other 19 provinces show expansive negative decoupling. Secondly, according to the coordination model, national carbon emissions are coordinated to the forest area. Zhejiang, Fujian, Jiangxi, and Guangdong are basically coordinated provinces. More coordinated provinces include Ningxia. The other 25 provinces are coordinated provinces. Finally, according to the comprehensive measurement model, Inner Mongolia, Qinghai, Shaanxi, Hainan, Jilin, Anhui, Liaoning, and Heilongjiang are high-quality expansive negative decoupling provinces. Chongqing, Hunan, Tianjin, Shandong, Hebei, and Guangxi are moderate to strong expansive negative decoupling provinces. This study not only provides a new perspective for analyzing forest carbon sinks, but also provides theoretical guidance for enhancing the natural carbon sink capacity, helping to achieve global carbon peak and carbon neutrality goals.

The role and transmission mechanism of forest resource abundance on low-carbon economic development in the Yangtze River Delta region: Insights from the COP26 targets

Climate change is getting more and more attention. The COP26 emphasized the need to stop deforestation and reduce greenhouse gas emissions to help mitigate climate change. As such, China has spare no effort to promote green and low-carbon development to achieve the goals of carbon peaking and carbon neutrality. This paper aims to analyze the impact and transmission mechanism of the forest resources abundance at the city level in the Yangtze River Delta (YRD) region on the realization of low-carbon economic development with the targets of COP26. From the static and dynamic relative carbon emission indicators, based on the panel data of 41 cities from 2007 to 2019, this article constructs spatial econometric models and a mediation effect model to empirically verify the impact of forest resources on low-carbon development and the transmission mechanism. The conclusions are as follows: (1) Forest resource abundance and carbon emissions per GDP show a significant negative relationship at the 1% level, and the energy consumption structure has the greatest impact. (2) The abundance of forest resources has a significant positive correlation with the carbon sequestration capacity per GDP at 1%. (3) The forest resources abundance is significantly negatively correlated with the net carbon emissions per GDP, which means after considering the carbon sequestration effect, forest resources abundance has a greater impact on the carbon emissions intensity. (4) Forest resources mainly produce a transmission mechanism for low-carbon economic development through the urbanization effect. Economic development is positively correlated with carbon emissions but negatively correlated with forest carbon sequestration. Although forest carbon sinks play a role in low-carbon economic development, it is not enough to rely solely on forest carbon sinks because of social cost. Finally, this paper proposes policy recommendations to promote low-carbon economic development from the perspectives of administrative orders, economic incentives, and voluntary participation.

Economic and Natural Factors of Spatial Heterogeneity of Forest Carbon Emissions in Russia in the 2010s

Economic and Natural Factors of Spatial Heterogeneity of Forest Carbon Emissions in Russia in the 2010s

NPP and Carbon Emissions under Forest Fire Disturbance in Southwest and Northeast China from 2001 to 2020

With climate change, frequent forest fires and prolonged fire period occur all over the world. Moreover, carbon emission from forest fire affects the carbon cycle of the forest ecosystem. However, this effect varies by region with no uniform conclusions, and fewer comparative studies exist on such differences between regions. In this paper, net primary productivity (NPP) data MOD17A3 were used as an important parameter of forest carbon absorption, along with MODIS fire spot data MCD14DL and burned area data MCD64A1. Forest carbon lost under forest fire interference in the northeast and southwest natural forest areas of China was studied to explore the role of forest fire in the carbon cycle process and its differences in the unlike regions of China. Here, by means of kernel density analysis and M-K trend test, the characteristics of forest fires in China’s southwest and northeast forests were calculated. Forest carbon emission under forest fire disturbance was quantified by reference to the forest fire emission factor list. We show that (1) the total number of forest fire spots in the southwest region from 2001 to 2020 was 1.06 × 105, 1.28 times that of Northeast China. However, the total burned area in the southwest was only 67.84% of that in the northeast. (2) The total carbon emissions from forest fires in the southwest from 2001 to 2020 was 37,559.94 Gg, 10.77% larger than the northeast forest, CH4 and CO2 were 13.52% and 11.29% larger respectively. Moreover, the carbon emissions of forest fire in the northeast showed a downward trend, R2 = 0.16 (p &lt; 0.1), while it remained basically unchanged in the southwest. The contribution of carbon emissions from forest fires changed with forest types, it was shown as: evergreen needleleaf forest (14.98%) &gt; evergreen broadleaf forest (10.81%) &gt; deciduous needleleaf forest (6.52%) &gt; deciduous broadleaf forest (5.22%). (3) From 2001 to 2020, under the premise that the NPP both manifested upward trends, the NPP of the burned areas showed a significant downward trend in the southwest forest, with R2 = 0.42 (p &lt; 0.05), while it increased in the northeast forest, with R2 = 0.37 (p &lt; 0.05). It showed negative correlation between NPP of burned areas and forest fire carbon emissions, and forest fire disturbance had no significant effect on forest NPP in Northeast China, while net carbon loss occurred in Southwest China. In general, under different forest fire characteristics, NPP, which represents forest carbon uptake, and carbon emissions from forest fires show differences. The impact of forest fire disturbance on forest carbon process varies with regions. The study can provide some ideas on the effects of forest fire disturbance on climate change.

Temporal trends in CO2 emissions from Picea rubens stumps: A chronosequence approach

Understanding the forest carbon cycle has become increasingly important as carbon dioxide (CO2) emissions contribute to the changing climate. Decomposition is a major component of the forest carbon cycle; however, aspects of wood decomposition remain poorly understood, especially for stumps. To fill this knowledge gap, we examined the change in CO2 emissions over time from Picea rubens Sarg. (red spruce) stumps using a 32-year chronosequence (i.e., 0, 2, 4, 8, 23, and 32 years since harvest) derived from detailed harvesting records in a northern conifer forest in central Maine, USA, that has experienced repeated partial harvests. We found low initial CO2 flux (3.2 μmol CO2 m−2 s−1 at year 0) followed by a rapid increase, peaking 8 years post-harvest (24.3 μmol CO2 m−2 s−1) followed in turn by a decrease to very low rates by years 23 and 32 (1.4 and 1.7 μmol CO2 m−2 s−1, respectively). We found no clear relationship between CO2 emissions and any of the environmental or stump variables tested (wood temperature, wood moisture, soil moisture, and/or stump volume), suggesting that time since harvest was the overriding influence on CO2 flux rates. The large variability in CO2 flux rates among stumps of the same time since harvest points to the need for future research that includes larger sample sizes and covers a wider range of environmental and stump variables to better capture potential sources of variation. Our results add to the growing body of research on carbon emissions from deadwood that can inform forest carbon-cycle models. In addition, forest managers, who are increasingly interested in carbon management, can use these results to assess harvesting impacts on forest carbon emissions.

Tree mortality and carbon emission as a function of wildfire severity in south-eastern Australian temperate forests

Disturbance trends over recent decades indicate that climate change is resulting in increased fire severity and extent in Australia's temperate Eucalyptus forests. As disturbance cycles become shorter and more severe, empirical measurements are required to identify potential change in forest carbon (C) stock and emissions. However, such estimates are rare in the literature.The 2019–2020 wildfires burnt through 6 to 7 million ha of mainly temperate open Eucalyptus forest in south-east Australia, with top down emission estimates ranging from 97 to 130 tonnes CO2 ha−1. Study sites that had been assessed for all aboveground C pools prior to the wildfires, were burnt in January 2020 by wildfire that varied in severity. Here we quantify the impact of high and low/moderate fire severities on tree mortality, C loss and C redistribution and assess implications for future C storage in these temperate Eucalyptus forests.Higher fire severity resulted in greater overstorey tree mortality but not understorey or loss of dead standing trees than in low/moderate severity fires. High severity fires combusted almost twice as much C from live trees (42 Mg C ha−1) as low/moderate severity fires (25 Mg C ha−1), while C loss from dead standing trees was similar among fire severity classes (average 17 Mg C ha−1). Total aboveground C lost across study sites was 42 Mg C ha−1 for high and 47 Mg C ha−1 for low/moderate severity, with an average of 45 Mg C ha−1 equivalent to 15 % (high severity) and 14 % (low/moderate severity) of AGC.Extrapolating our findings to other tall to medium open Eucalyptus forests across Victoria revealed that 37.33 ± 12.25 Tg C (mean ± s.e.) or 152 ± 50 Mg CO2 ha−1 was lost to the atmosphere from the 0.9 million ha of these productive forests, equating to about 20 % of Australia's total net annual emissions.

Increasing Paper and Cardboard Recycling: Impacts on the Forest Sector and Carbon Emissions

Increasing Paper and Cardboard Recycling: Impacts on the Forest Sector and Carbon Emissions

Estimation of biomass burning emissions by integrating ICESat-2, Landsat 8, and Sentinel-1 data

Anthropogenic carbon emissions directly contribute to global warming, which has induced severe environmental concerns like extreme droughts and devastating fires. To evaluate the effects of fires on carbon cycling and climate change, it is crucial to accurately estimate the amount of carbon released during fires. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission offers spaceborne light detection and ranging (LiDAR) measurements of global forests, providing excellent opportunities for monitoring forest dynamics and carbon emissions. The ICESat-2 Land and vegetation height product (ATL08), Landsat 8 data, and Sentinel-1 data were integrated to estimate biomass burning emissions with a three-phase hierarchy model. In Phase 1, the least absolute shrinkage and selection operator (LASSO) regression was used to establish the relationship between ATL08 LiDAR metrics and reference aboveground biomass (AGB), R2 0.77 and RMSE 56.67 Mg ha−1. In Phase 2, the LASSO regression predicted AGB for all ATL08 segments. A Random Forest Regression model was trained with Landsat 8 reflectance data and derived vegetation indices, Sentinel-1 data, and terrain variables as predictors and with the LASSO predicted AGB as the response variable, R2 0.71 and RMSE 45.91 Mg ha−1. Wall-to-wall maps of pre-fire and post-fire AGB were produced with the Random Forest model. In Phase 3, the difference between the pre-fire and the post-fire AGB was converted to carbon emissions. The estimated biomass burning emissions of the 2018 Carr fire group, the 2018 Camp fire, and the 2019 Walker fire in California are 3.54 (± 0.067) Tg C, 1.66 (± 0.041) Tg C, and 0.45 (± 0.022) Tg C, respectively. Forests have the highest biomass burning emissions with an average emission of 30.09 Mg ha−1, approximately twice and four times the average emissions from shrubs and grasses. This study highlights the merit of integrating spaceborne remote sensing data, including LiDAR data, optical data, and radar data, for estimating biomass burning emissions, opening an avenue for accurate forest biomass and carbon dynamics monitoring, as well as climate change mitigation.

Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks

Detecting early deforestation is a fundamental process in reducing forest degradation and carbon emissions. With this procedure, it is possible to monitor and control illegal activities associated with deforestation. Most regular monitoring projects have been recently proposed, but most of them rely on optical imagery. In addition, these data are seriously restricted by cloud coverage, especially in tropical environments. In this regard, Synthetic Aperture Radar (SAR) is an attractive alternative that can fill this observational gap. This work evaluated and compared a conventional method based on time series and a Fully Convolutional Network (FCN) with bi-temporal SAR images. These approaches were assessed in two regions of the Brazilian Amazon to detect deforestation between 2019 and 2020. Different pre-processing techniques, including filtering and stabilization stages, were applied to the C-band Sentinel-1 images. Furthermore, this study proposes to provide the network with the distance map to past-deforestation as additional information to the pair of images being compared. In our experiments, this proposal brought up to 4% improvement in average precision. The experimental results further indicated a clear superiority of the DL approach over a time series-based deforestation detection method used as a baseline in all experiments. Finally, the study proved the benefits of pre-processing techniques when using detection methods based on time series. On the contrary, the analysis revealed that the neural network could eliminate noise from the input images, making filtering innocuous and, therefore, unnecessary. On the other hand, the stabilization of the input images brought non-negligible accuracy gains to the DL approach.

MULTI-ATTENTION GHOSTNET FOR DEFORESTATION DETECTION IN THE AMAZON RAINFOREST

Abstract. Efficient deforestation detection techniques are essential to monitor and control illegal logging, thus reducing forest loss and carbon emissions in the Amazon rainforest. Recent works based on Deep Learning (DL) models have been proposed for that purpose. DL-based methods, however, are known to require large amounts of training data to be properly trained. Moreover, the deforestation detection application is characterized by a high class imbalance, as recent deforestation areas usually represent a small fraction of the geographic extents being monitored. In order to produce a lightweight architecture in terms of the number of learnable parameters and address the high class imbalance of the deforestation detection application, we propose a DL model based on the GhostNet architecture, which combines Ghost modules in a fully convolutional architecture. The proposed architecture also includes Spatial Attention Mechanisms attached to the skip connections of the GhostNet in order to better capture the spatial relationships among class features. Experiments were carried out using Sentinel-2 images of a region in the Para´ state, Brazil, in the Amazon rainforest. The results obtained show that the proposed model achieves accuracy levels that are superior to those delivered by state-of-the-art DL architectures, with a lower computational cost due to the smaller number of learnable parameters.

Forest Carbon Emission Sources Are Not Equal: Putting Fire, Harvest, and Fossil Fuel Emissions in Context

Climate change has intensified the scale of global wildfire impacts in recent decades. In order to reduce fire impacts, management policies are being proposed in the western United States to lower fire risk that focus on harvesting trees, including large-diameter trees. Many policies already do not include diameter limits and some recent policies have proposed diameter increases in fuel reduction strategies. While the primary goal is fire risk reduction, these policies have been interpreted as strategies that can be used to save trees from being killed by fire, thus preventing carbon emissions and feedbacks to climate warming. This interpretation has already resulted in cutting down trees that likely would have survived fire, resulting in forest carbon losses that are greater than if a wildfire had occurred. To help policymakers and managers avoid these unintended carbon consequences and to present carbon emission sources in the same context, we calculate western United States forest fire carbon emissions and compare them with harvest and fossil fuel emissions (FFE) over the same timeframe. We find that forest fire carbon emissions are on average only 6% of anthropogenic FFE over the past decade. While wildfire occurrence and area burned have increased over the last three decades, per area fire emissions for extreme fire events are relatively constant. In contrast, harvest of mature trees releases a higher density of carbon emissions (e.g., per unit area) relative to wildfire (150–800%) because harvest causes a higher rate of tree mortality than wildfire. Our results show that increasing harvest of mature trees to save them from fire increases emissions rather than preventing them. Shown in context, our results demonstrate that reducing FFEs will do more for climate mitigation potential (and subsequent reduction of fire) than increasing extractive harvest to prevent fire emissions. On public lands, management aimed at less-intensive fuels reduction (such as removal of “ladder” fuels, i.e., shrubs and small-diameter trees) will help to balance reducing catastrophic fire and leave live mature trees on the landscape to continue carbon uptake.