Forest Cover Data Research Articles

Assessing GEDI data fusions to map woodpecker distributions and biodiversity hotspots

Abstract In forested systems, woodpecker species richness has been linked with songbird diversity, and identifying woodpecker biodiversity hotspots may contribute important information for conservation planning. The availability of global forest structure data via the Global Ecosystem Dynamics Investigation (GEDI) instrument provides a new tool for examining broad extent relationships amongst environmental variables, forest structure, and woodpecker diversity hotspots. Within the Marine West Coast Forest ecoregion, USA, we used eBird data for 7 woodpecker species to model encounter rates based on bioclimatic variables, process data (e.g. duration and timing of survey), MODIS forest land cover data, and GEDI-fusion metrics. The GEDI-fusion metrics included foliage height diversity (fhd), rh98 (a representation of canopy height), and canopy cover, which were created by combining GEDI data with Landsat, Sentinel-1, topographic, and climatic information within a random forest modeling framework. AUCs for the species-specific models ranged from 0.77–0.98, where bioclimatic and process predictors were amongst the most important variables for all species. GEDI-fusion forest structure metrics were highly ranked for all species, with fhd included as a highly ranked predictor for all species. The structural metrics included as top predictors for each species were reflective of known species-specific habitat associations. Hotspots in this ecoregion tended to be inland and occurred most often on privately-owned lands. Identification of hotspots is the first step towards management plans focused on biodiversity, and understanding ownership patterns is important for future conservation efforts. The near-global extent of GEDI data, along with recent studies that recommend woodpeckers as indicators of biodiversity across multiple forest types at local and global scales, suggest that synthesis of GEDI-derived data applied to woodpecker detection information might be a powerful approach to identifying biodiversity hotspots.

Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand

BackgroundThailand aimed to eliminate malaria by 2024, and as such is planning for future prevention of re-establishment in malaria free provinces. Understanding the receptivity of local areas to malaria allows the appropriate targeting of interventions. Current approaches to assessing receptivity involve collecting entomological data. Forest coverage is known to be associated with malaria risk, as an environment conducive to both vector breeding and high-risk human behaviours.MethodsGeolocated, anonymized, individual-level surveillance data from 2011 to 2021 from the Thai Division of Vector-Borne Disease (DVBD) was used to calculate incidence and estimated Rc at village level. Forest cover was calculated using raster maps of tree crown cover density and year of forest loss from the publicly available Hansen dataset. Incidence and forest cover were compared graphically and using Spearman’s rho. The current foci classification system was applied to data from the last 5 years (2017–2021) and forest cover for 2021 compared between the classifications. A simple risk score was developed to identify villages with high receptivity.ResultsThere was a non-linear decrease in annual cases by 96.6% (1061 to 36) across the two provinces from 2011 to 2021. Indigenous Annual Parasite Index (API) and approximated Rc were higher in villages in highly forested subdistricts, and with higher forest cover within 5 km. Forest cover was also higher in malaria foci which consistently reported malaria cases each year than those which did not. An Rc > 1 was only reported in villages in subdistricts with > 25% forest cover. When applying a simple risk score using forest cover and recent case history, the classifications were comparable to those of the risk stratification system currently used by the DVBD.ConclusionsThere was a positive association between forest coverage around a village and indigenous malaria cases. Most local transmission was observed in the heavily forested subdistricts on the international borders with Laos and Cambodia, which are where the most receptive villages are located. These areas are at greater risk of importation of malaria due to population mobility and forest-going activities. Combining forest cover and recent case surveillance data with measures of vulnerability may be useful for prediction of malaria recurrence risk.

Generation of High Temporal Resolution Fractional Forest Cover Data and Its Application in Accurate Time Detection of Forest Loss

Fractional Forest cover holds significance in characterizing the ecological condition of forests and serves as a crucial input parameter for climate and hydrological models. This research introduces a novel approach for generating a 250 m fractional forest cover product with an 8-day temporal resolution based on the updated GLASS FVC product and the annualized MODIS VCF product, thereby facilitating the development of a high-quality, long-time-series forest cover product on a global scale. Validation of the proposed product, employing high spatial resolution GFCC data, demonstrates its high accuracy across various continents and forest cover scenarios globally. It yields an average fit coefficient of determination (R2) of 0.9085 and an average root-mean-square error of 7.22%. Furthermore, to assess the availability and credibility of forest cover data with high temporal resolution, this study integrates the CCDC algorithm to map forest disturbances and quantify the yearly and even monthly disturbed trace area within two sub-study areas of the Amazon region. The achieved sample validation accuracy is over 86%, which substantiates the reliability of the data. This investigation offers a fresh perspective on monitoring forest changes and observing forest disturbances by amalgamating data from diverse sources, enabling the mapping of dynamic forest cover over an extensive time series with high temporal resolution, thereby mitigating data gaps and enhancing the precision of existing products.

Taylor Shepherd Golden Optimization-Enabled ResUNet for Forest Change Detection Using Satellite Images

The pivotal task of remote sensing image (RSI) processing change detection (CD) highly aims to accurately detect changes in land cover based on multi-temporal images. With the advent of deep learning, technology has delivered remarkable results in the last years in the detection of variations in forest land cover data. Some of the conventional CD techniques are weak and are highly susceptible to errors and can result even in inaccurate outcomes. Thus, certain techniques are not desirable for real-time CD applications. To abridge this gap, this research introduces an innovative work for forest CD utilizing the proposed Taylor Shepherd Golden Optimization_ResUNet (TSGO_ResUNet) and Fuzzy Neural network (Fuzzy NN) for segment mapping. Here, the segmentation process is accomplished using ResUNet to determine the exact boundary or shape of each object for every pixel in the image. Furthermore, TSGO is achieved by consolidating Taylor Shuffled Shepherd Optimization (TSSO) with Golden Search Optimization (GSO). In addition, the devised TSGO_ResUNet + Fuzzy NN has gained maximum accuracy and kappa coefficient of 0.952 and 0.785, and minimum error rate of 0.051.

Forest cover change and its carbon dynamic of the karst area in Bulusaraung, South Sulawesi, Indonesia

Information on land forest cover changes and carbon stocks in karst areas is vital for management planning. Therefore, an analysis of the dynamics of land forest cover, carbon stocks of secondary karst forests, and their future projections is necessary. This study aimed to analyze the carbon stocks of secondary forests, forest cover change, and their projections in karst areas. We used 43 sample plots of 20 m x 20 m to measure Above Ground Biomass (AGB) and soil organic carbon stocks. Forest cover data were collected using remote sensing and GIS tools. Data analysis of the ABG of the secondary forest for the karst plain and karst tower was conducted using the Chave equation. Land-cover change was analyzed by delineating the 2011, 2016, and 2021 land-cover data from satellite imagery and testing the accuracy using survey data fields and high-resolution images. Land-cover projections for 2026 and 2031 were obtained using Cellular Automata Markov (CA-Markov) analysis. The results revealed ten land cover types in the karst area of the Bulusaraung Forest Management Unit (FMU). Secondary dryland karst forests (tower and plain karst) were the dominant forest types (60%) in the Maros-Pangkep essential karst ecosystem area. Both karst types of secondary forests are relatively high carbon stores. Data projection shows that the mining area will grow extensively by 2031. Secondary dryland karst forests will be degraded by 15.5% in 20 years due to increased mining activities, conversion to paddy fields, dryland agriculture, and mixed dryland agriculture. Karst ecosystems are vital for carbon and water storage. Therefore, some strategies are required for the sustainable management of Maros-Pangkep Karst are as follows: (i) controlling the opening of mining areas according to sustainable environmental principles, (ii) encouraging the implementation of post-mining land rehabilitation, (iii) increasing public awareness of the importance of preserving karst areas, and (iv) implementing soil conservation rules by prioritizing agroforestry systems in dryland farming.

Development of a footprint description tool utilizing SMEAR Estonia eddy-covariance data and footprint modelling in combination with remote sensed forest species and land cover data

Abstract Understanding how forest ecosystems respond to environmental factors, particularly in the context of global climate change, is essential for devising effective mitigation strategies. This study focuses on quantifying the interaction between forest ecosystems and atmospheric gases. To achieve our objectives, we are using the eddy covariance (EC) flux method to measure air turbulence and gas concentrations above the forest canopy at the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) in southern Estonia. We apply a flux footprint (FFP) model to describe the spatial extent and position of the surface area contributing to the turbulent flux measurements. The FFP analysis provides valuable insights into the long-term changes in SMEAR Estonia, the FFP and its relationship with forest management and land use changes. Our findings reveal that the FFP area varies from year to year due to changes in wind speed and direction, affecting the contribution of different land cover elements to the overall FFP. The average changes in the FFP area at a height of 30 meters were approximately 4.9%, while those at a height of 70 meters were only 1.6%. Moreover, human activities, such as thinning and clear-cutting, influence the growing stock and increment of forest stands.

USING REMOTE SENSING TO DETECT FOREST COVER CHANGE IN SAM HOUSTON NATIONAL FOREST, TEXAS

Abstract. The Sam Houston National Forest is a large, forested area in Texas that has experienced significant land-use changes over the past few decades. The study area replicates plentiful climatic, physiographic, and edaphic differences in the country and this forest faces a serious problem of degradation and disturbance of different nature. In this study, we utilized remote sensing technology specifically Landsat 4 ETM and Landsat 8 from USGS Earth Explorer with spatial resolution 30 m, to analyze forest cover change in Sam Houston National Forest from 2001 to 2020. We also employed the Hansen Global Forest Cover Data from the Google Earth Engine Catalogue to assess the forest cover loss and gain within the study period. Also, the i-Tree software was used to estimate carbon sequestration in the forest and assess the potential benefits of forest management practices. Results of the study showed that the Sam Houston National Forest has experienced a net loss of forest cover over the past few decades, primarily due to agricultural expansion and urbanization. However, the forest has also shown signs of regrowth and recovery in certain areas, highlighting the potential for effective forest management practices to promote carbon sequestration and conservation. Overall, our study highlights the importance of remote sensing technology for understanding forest cover change and its implications for carbon sequestration and climate change mitigation.

Strong collaborative governance networks support effective Forest Stewardship Council-certified community-based forest management: Evidence from Southeast Tanzania

Research on community-based forest management indicates its conservation outcomes depend on local rule enforcement, extraction pressures, and community support. However, many community-based forest management projects, particularly in the Global South, also involve collaborative networks of non-state actors such as NGOs and private corporations. Many of these networks promote sustainability certification under programs like the Forest Stewardship Council. We report on analyses of longitudinal forest cover data constructed using satellite observations alongside inter-organizational collaborative governance network data constructed from archival sources, document analysis, and oral histories to assess how collaborative governance networks shape community-based forestry’s conservation effectiveness in eight villages in Kilwa District, Tanzania. Our findings indicate certified community-based forestry’s impacts on deforestation can depend on the composition and structure of collaborative governance networks. Using matched Cox proportional hazards models with geographic fixed effects, we find evidence that certified community-based forest management can stem forest loss as effectively as state-led forest management (in the form of National Forest Reserves). However, the characteristics of collaborative governance networks connecting organizations engaged in forest management in our study villages shape both which areas are selected into certified community-based forest management and villages’ overall deforestation rates. Specifically, we find that the more each village government’s organizational partners are connected to one another through bonding ties, and the more civil society organizations collaborate with each village government through bridging ties, the lower the village’s deforestation risk. More private sector organizations connected to village governments through bridging ties, however, are associated with higher deforestation risks. Our evidence highlights the importance of investments in inter-organizational networks for promoting sustainably certified community-based forest conservation.

Refining National Forest Cover Data Based on Fusion Optical Satellite Imageries in Indonesia

Precision mapping towards tropical forest cover data is critical to address the global climate crisis, such as land-based carbon measurement and potential conservation areas identification. In the recent decade, accessibility to open public datasets on forestry is rapidly increased. However, the availability of finer-resolution of forest cover data is still very limited. As a developing country with numerous rainforests, Indonesia suffered multifaceted threats, particularly deforestation. Thus, precise forest cover data can be useful to fulfill Indonesia’s nationally determined contribution to climate change. In this study, we mapped the national forest cover data for Indonesia using a new object-based image classification approach based on combined Planet-NICFI and Sentinel-2 optical imageries. Our findings had relatively high accuracy compared with the other studies, with the F score ranging from 0.67 to 0.99 and can capture the fragmented forest in fine resolution (i.e., ∼5 m). In addition, we found that Planet-NICFI bands had a higher contribution in predicting forest cover than Sentinel-2 imageries. Utilizing forest cover data for further analyses should be performed to help the achievement of national and global agenda, e.g., related to the FOLU net sink in 2030 and the Global Biodiversity Framework.

Reconstructing Long‐Term Forest Age of China by Combining Forest Inventories, Satellite‐Based Forest Age and Forest Cover Data Sets

AbstractForest age is one of the most important ecosystem characters for accurately estimating the magnitude and potential of carbon sink in forest ecosystems. During the past 40 years, national ecological restoration projects have led to the near doubling of the forest cover area in China, which has also substantially affected the dynamics of forest age. Therefore, there is an urgent need to generate long‐term forest age maps for China. This study reconstructed China forest age datasets (CFAD) from 1980 to 2015 at five year intervals at a 1 km spatial resolution by merging a satellite‐based forest age map in 2010 and forest cover dynamic maps from 1980 to 2015. The random forest method was used to reconstruct the forest age where forest age could not be inferred from the forest age base map in 2010 directly. CFAD showed a good agreement with the province‐level mean forest age derived from the several national forest inventories (R2 ranged from 0.66 to 0.86). In general, the younger forests are mainly distributed in southern and eastern China. The older forests are mainly distributed in the mountain areas of northeast, northwest and southwest China. The average age of China's forests increased from 18.2 to 44.0 years old from 1980 to 2015. Based on the current forest age and future afforestation planning, the average forest age in China is predicted to reach 71.6 years old in 2060. The CFAD provides an alternative data set to obtain improved estimates of local and national forest carbon sinks in China.

Examination of the amount of GEDI data required to characterize central Africa tropical forest aboveground biomass at REDD+ project scale in Mai Ndombe province

The Global Ecosystem Dynamics Investigation (GEDI) is the first spaceborne LiDAR designed to improve quantification of vegetation structure and forest aboveground biomass (AGB) including in the tropics where forest AGB inventory data are limited. GEDI is a sampling instrument on the International Space Station (ISS) and does not provide data on a regular, systematic basis. Reducing Emissions from Deforestation and Degradation and enhancement of carbon stocks (REDD+) projects require forest AGB inventories to quantify avoided carbon emissions achieved by conserving forest biomass. Although there is high confidence that GEDI can retrieve measurements that allow estimation of AGB at scale, less is known about how well its operational deployment performs for measurement of AGB to support REDD+ projects. This includes an understanding of the appropriate time period required to collect sufficient GEDI observations for reliable forest AGB assessment. This paper describes the first study to examine the amount of GEDI data needed to characterize tropical forest AGB at REDD+ project scale. In tropical Africa, the average REDD+ project size documented by the Center for International Forestry Research is equivalent to a square area of approximately 50 × 50 km (250,000 ha). Recently available good quality GEDI footprint-level AGB product data acquired over a 31 month period over Mai Ndombe province in the west of the Democratic Republic of the Congo were considered. A global 30 m percent tree cover product, updated with contemporary mapped forest cover loss, was used to map the intact forest across the province. Fifteen 50 × 50 km test sites, representing example REDD+ project areas with >80% forest cover and good quality AGB forest footprint data distributed across each site, were selected. The sites were selected from five AGB stratum defined from the GEDI data, and with three sites selected per stratum that had low, medium and high semivariogram sill values that reflect increasing within-site AGB spatial variation. The overall mean GEDI AGB (OMGA) was derived from all the good quality forest GEDI footprint AGB values acquired over the 31 months of GEDI operation at each site. The expected minimum number of GEDI orbits (norbitsp) required to characterize the OMGA to within p = ±5%, ±10%, and ±20% was derived by considering different combinations of GEDI orbits randomly selected from the 31 months of GEDI data. The expected minimum number of days (ndaysp) required to characterize the AGB over each site was derived by multiplying the site norbitsp values with a scalar coefficient of 13.03 days. The scalar coefficient was found by counting the temporal intervals between successive GEDI orbits containing good quality forest AGB data and is equivalent to the average number of days required to obtain a GEDI orbit containing good quality forest AGB data at 50 × 50 km scale. Among the 15 sites, observation periods ranging from 65 to 221 days (0.18 – 0.61 years), 143 – 534 days (0.39 – 1.46 years), and 390 – 742 days (1.07 – 2.03 years) were required to characterize the AGB to within ±20%, ±10%, and ±5% of the site OMGA, respectively. The Intergovernmental Panel on Climate Change (IPCC) recommended accuracy requirement for forest AGB estimates is 10%. Thus, to meet this accuracy requirement the findings of this study indicate that at least 534 days (1.46 years) would be required for REDD+ site monitoring using GEDI in Mai Ndombe province. In other central African tropical forest localities these observations periods may be different depending on the forest AGB and spatial variation, cloud cover, ephemeral surface water presence, and GEDI AGB retrieval sensitivity to the forest conditions.

User-Aware Evaluation for Medium-Resolution Forest-Related Datasets in China: Reliability and Spatial Consistency

Forest cover data are fundamental to sustainable forest management and conservation. Available medium-resolution publicly shared forest-related datasets provide primary information on forest distribution. The evaluation of relevant datasets is of great importance to learn about the differences, characterize the accuracy, and provide a reference for rational use. This study presents an evaluation and analysis of the forest-related datasets in China around 2020, including TreeCover and the forest-related layer (latter referred to as the forest datasets) in WorldCover, Esri land cover, FROM-GLC10, GlobeLand30, and GLC_FCS30. These forest datasets, that are obtained by aggregating forest-related lasses based on the classification schemes, are analyzed from spatial consistency and accuracy comparison. The results illustrate that forest datasets with 10m resolution are generally more precise than those with 30m resolution in China. WorldCover shows the highest accuracy, with producer accuracy and user accuracy of 91.4% and 87.09%, respectively. These datasets exhibit high accuracy but great spatial inconsistency. The more consistent the regions are, the more accurate the accuracy is. High consistency (≥5, i.e., classified into forests by five datasets) areas account for 56.49% of areas of forest classified (AFC), while the area of low consistency (≤2) reach 25.51% of AFC. The analysis delves into the datasets, offering a reliable reference for the usage of these datasets.

Protecting Brazilian Amazon Indigenous territories reduces atmospheric particulates and avoids associated health impacts and costs

Indigenous territories are considered important for conservation, but little is known about their role in maintaining human health. Here we quantified the potential human health and economic benefits of protecting these territories in the Brazilian Amazon, by using cardiovascular and respiratory diseases cases, pollutant and forest cover data. Between 2010 and 2019, 1.68 tons of Particulate Matter of small size (PM2.5) were released every year, with negative effects for human health. A lower number of diseases and infections was also found in municipalities with more forested areas, and with a low level of fragmentation, which probably is related to the potential capacity of the Amazon Forest to absorb PM2.5 (26,376.66 tons year−1, 27% of this absorption capacity in Indigenous territories). Our estimates indicate that by protecting Amazon Indigenous territories, over 15 million of respiratory and cardiovascular cases could be avoided every year, with ~$2 billion USD being saved only in health costs.

Reconstructing Long‐Term Forest Cover in China by Fusing National Forest Inventory and 20 Land Use and Land Cover Data Sets

AbstractAs an important type of terrestrial carbon sink, forests play a critical role in offsetting anthropogenic fossil fuel CO2 emissions which should help nations to achieve carbon neutrality goals worldwide. According to National Forest Inventory (NFI), China has experienced substantial increases in forest cover by benefiting from national afforestation projects initiated in the l980s. However, none of the current land use and land cover (LULC) data sets can reproduce the long‐term changes of forest cover derived from NFI in China. Here, by combining NFI and 20 LULC data sets, we developed a new method of reconstructing historical forest cover in China from 1980 to 2015 at 5‐year intervals. The new forest cover data set can accurately reproduce the historical changes of forest cover in China during this period. Validated against 3851 field survey samples covering the study period of 1985–2015, the data sets show high accuracy with overall accuracy varying from 76.9% to 99.4%. Accurate long‐term forest cover maps have great potential for use in estimating terrestrial carbon, tracking forest management, and other scientific research studies.

Community forest management led to rapid local forest gain in Nepal: A 29 year mixed methods retrospective case study

Despite widespread implementation of community forest (CF) management over the last 40 years, limited knowledge exists on CF-level processes that link introduction of CF management to long-term forest cover change. CF management has been widely adopted in Nepal but research on effects of CF management on forest cover change often lacks long-term forest cover data and does not attempt to directly link changes in local forest cover with forest management decisions by local forest users. This study used a mixed methods approach integrating a 29 year-long satellite record of annual forest cover (1988–2016) and local interview data from 2018 to 2020 across eight CFs in Nepal to evaluate the spatially explicit impacts of CF management on long-term forest cover change. Information on historical forest management practices and forest conditions were collected from qualitative open-ended interviews with 28 CF user group managers, forest management records, and field observations. Annual forest cover change from 1988 to 2016 was measured using Landsat satellite time series data. For each CF, the impacts of local forest management decisions on forest cover trends were then characterized before and after the local introduction of CF management. Although study CF user groups had a wide range of initial forest cover (11–93%) before CF management, forest cover increased in all eight study sites under CF management to between 58% and 99% by 2016. Forest cover gains were evident during an informal period of CF management that preceded formal registration and management in most CFs and were maintained over the ensuing period of formal CF management. Interview data show that forest cover gains directly followed the introduction of informal and formal CF management activities such as tree planting, forest patrols, and grazing regulations. In our case study CFs, the early success of CF management motivated the continued investment in forest management leading to long-term forest conservation. This study demonstrates that CF management has been effective at conserving and expanding forest cover in community forests, and that yearly forest change and management data can be used to assess both the immediate impact and long-term sustainability of CF management.

Diachronic quantitative snow avalanche risk assessment as a function of forest cover changes

This work proposes a holistic quantitative snow avalanche risk assessment that evaluates, at reasonable computational costs and for various types of buildings, the impact of forest cover changes on the probability distribution of runout distances, impact pressures and subsequent risk estimates. A typical case study of the French Alps shows that, from a completely deforested to a completely forested path, avalanche risk for a building located downslope decreases by 53–99%, depending on how forest cover is accounted for in avalanche statistical–dynamical modeling. Local forest cover data inferred from old maps and photographs further demonstrates that a 20–60% risk reduction actually occurred between 1825 and 2017 at the site because of the afforestation dynamics, with significant modulations according to the considered building technology. These results (1) assert the protective role of forests against snow avalanches, (2) highlight the potential of combining nature-based solutions with traditional structural measures to reduce risk to acceptable levels at reasonable costs, (3) suggest a significant decrease in risk to settlements in areas that encountered similar forest cover changes and (4) open the door to the quantification of long-term avalanche risk changes as a function of changes of all its hazard, vulnerability and exposure drivers in various mountain context.

Half-millennium evidence suggests that extinction debts of global vertebrates started in the Second Industrial Revolution

Extinction debt describes the time-lagged process of species extinction, which usually requires dozens to hundreds of years to be paid off. However, due to the lack of long-term habitat data, it is indeterminate how strong the signal of extinction debts is at the global scale and when the debts started. Here, by compiling the geographical distributions of 6120 reptiles, 6047 amphibians, and 4278 mammals and correlating them with annual forest cover data from 1500 to 1992, we show that the beginning of the Second Industrial Revolution (the mid-19th century) was the earliest signal of cumulative extinction debts for global forest-dwelling vertebrate groups. More importantly, the impact of global protected areas on mitigating accumulated vertebrate extinction debt is not as immediate as that of mitigating reduced forest cover but rather suffers from pronounced time-lag effects. As the disequilibrium of vertebrate richness and forested habitat is currently taking place, preventive actions should be taken to promote a well-balanced status among forest restoration, protected areas, and biodiversity conservation to slow the accumulating debts for global forest-dwelling vertebrates.

Malaria prevalence and forest cover change in Kenya: a geospatial analysis

BackgroundThe relationship between forest cover loss and infectious disease risk needs further investigation, particularly in sub-Saharan Africa, and with regards to the effect to vulnerable populations—namely, children. The proposed research investigates the links between forest cover change and malaria. We aimed to identify spatiotemporal associations between deforestation and key children's health outcomes in sub-Saharan Africa nations affected by deforestation. Key aims were identifying: the amount of forest cover change most associated with malaria incidence changes; how these factors affect particularly vulnerable populations; what the most notable mechanisms were linking forest cover change to malaria incidence changes; how these findings informed the current theoretical understanding of the relationship between forest cover change and infectious disease risk; and identifying policy solutions that could avert any observed negative outcomes. MethodsWe propose regression analyses to investigate forest cover data and malaria incidence and paediatric (aged 2–10 years) prevalence data from selected US Agency for International Development's Demographic and Health Survey (DHS) datasets to assess the association between malaria disease and forest cover change. The research will use DHS-derived cluster sites along the human–wildlife interface in Kenya as geographical variables. The anticipated outcomes would show the relationship between forest cover change and malaria incidence and paediatric prevalence, while allowing for further mechanistic investigation. To measure forest cover change, a Normalized Vegetation Index and Enhanced Vegetation Index will be used. Malaria incidence and paediatric prevalence by cluster site is quantified in the DHS dataset. We plan to run logistical, multiple, and longitudinal regression analyses plotting forest cover change versus malaria incidence and paediatric prevalence at the cluster level at 5-year time points from 2000 to 2020. FindingsThis study is in the process of being completed, thus the findings are currently incomplete, bbut preliminary data will be available at the Planetary Health Alliance Annual Meeting in October 2022. We expect to show an association between substantial forest cover loss and higher malaria burden. The expected results would provide a basis and further opportunity for research to explore the causal relationship between these data. InterpretationThe relationship between forest cover loss and human health in areas along the human–wildlife interface is poorly understood. This research proposes an investigation into ecological determinants of of malarial disease. Although the connection between deforestation and infectious diseases is becoming more clear, additional data that expounds upon the ecological burden of disease for malaria in Kenya, and children as a vulnerable population, is needed in the literature. Understanding the relationship between forest cover and malarial disease might allow for the investigation of mechanistic properties that could further the evidence for a casual relationship between specific factors. FundingNational Academy of the Sciences, University of Wisconsin-Madison Global Health Institute Graduate Research Award.

Combining Area-Based and Individual Tree Metrics for Improving Merchantable and Non-Merchantable Wood Volume Estimates in Coastal Douglas-Fir Forests

Forest management practices can increase climate change mitigation potential through applications focused on carbon budgets. One such application involves utilizing non-merchantable material (i.e., logging residues typically piled and burned) for bio-energy. However, limited remote sensing data is available for estimating wood residues until after timber has been harvested, at which point recovery of residual wood is of little financial interest. This research utilizes a hybrid method to develop models that provide pre-harvest estimates of the amount of merchantable and non-merchantable material that would result from harvesting and investigates the scalability and transferability of such measures to the harvest block level. Models were trained using 38 plots across two sites dominated by Douglas-fir, then expanded to ten harvest blocks, and transferred to eight blocks from two sites without training data before being compared against multiple independent block-level estimates. Model results showed root mean square errors of 35% and 38% for merchantable and non-merchantable volumes, respectively. Merchantable volume estimates in blocks with training had average absolute differences from the harvest scale (9–34%) similar to transferred blocks without training (15–20%). Non-merchantable model results were also similar in both trained and transferred harvest blocks, with the pre-harvest model results having lower differences from the post-harvest geospatial versus field surveys. The results from this study show promise for hybrid methods to improve estimates of merchantable wood volume compared to conventional forest cover data approaches, and provide the ability to predict non-merchantable volumes within the range of accuracy of post-harvest residue survey methods.

The resilience of Indian Western Himalayan forests to regime shift: Are they reaching towards no return point?

Forests across the latitudes are facing regime shift under the influence of changing climate where temperature and precipitation are recognized as prominent drivers. Regime shift of forests can be in the form of conversion of one forest type into another or the alteration of forests into degraded class such as “scrub”. The Indian Western Himalayan (IWH) region hosts valuable forests to support multiple ecosystem services which may be impacted under different thresholds of regime shift. We assessed the threshold of regime shift as transition of forests into scrub considering temperature and precipitation records of the recent decade (2000–2019). A logistic regression model was developed using the forest cover data of IWH as a dependent variable and climatic records (temperature and precipitation) obtained from ERA-5 data as independent variables. Probability values of two classes (forest and scrub) were computed and were used to define present resilience states. The majority of the forest of the IWH region may not withstand any significant rise in temperature or a reduced amount of precipitation as almost 88.68% of forests of the IWH are under the low to moderate resilience category. Forest resilience significantly decreases below 1500 mm of precipitation indicating its tipping point of regime shift into the scrub. Temperature below 6 °C is not favourable for forests whereas a temperature range of 10–20 °C was found as the conducive range for the existence of forests in the region. Such empirical study would support the formulation of management plans and policies for sustainable forest resources and to assess the impacts of climate change.