Changes in forest production, biomass and carbon: Results from the 2015 UN FAO Global Forest Resource Assessment

Köhl, Michael; Lasco, Rodel D.; Cifuentes, Miguel; Jonsson, Oerjan; Korhonen, K.; Mundhenk, Philip; Návar, José; Stinson, G.

doi:10.1016/j.foreco.2015.05.036

Cited by 244 publications

(136 citation statements)

References 27 publications

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“…It covers an area of around 328.64 km 2 and is a UNESCO Biosphere Reserve since 2004 due to its high biodiversity and various ecosystems, such as tropical forest, mangroves, and wetlands [20,21]. The elevation of the area varies from 0 to 282.7 m a.s.l.…”

Section: Description Of the Study Areamentioning

confidence: 99%

“…Forests provide resources for millions of people and make a high contribution to employment, economic development, and terrestrial biodiversity in many countries [1,2]. However, forests are sensitive to climate variations, i.e., an increase of temperature and decrease of precipitation that leads to drought, and these variations make forests more susceptible to fire [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

et al. 2016

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Abstract:The Cat Ba National Park area (Vietnam) with its tropical forest is recognized as being part of the world biodiversity conservation by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and is a well-known destination for tourists, with around 500,000 travelers per year. This area has been the site for many research projects; however, no project has been carried out for forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authorities. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then, a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the support vector machine (SVM). The results show that the Kernel logistic regression model has a high level of performance in both the training and validation dataset, with a prediction capability of 92.2%. Since the Kernel logistic regression model outperforms the benchmark model, we conclude that the proposed model is a promising alternative tool that should also be considered for forest fire susceptibility mapping in other areas. The results of this study are useful for the local authorities in forest planning and management.

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Section: Description Of the Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

et al. 2016

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“…Forest ecosystems store the majority (~80%) of terrestrial biomass and carbon [1], as well as contribute significantly to global carbon emissions (~33% over the past 150 years) through land use and land cover changes [2][3][4]. Sustainable land use and forestry practices focused on enhancing carbon storage in live woody biomass have been recognized as effective means to reduce the effects of emissions and mitigate the effects of climate change, because forests are estimated to absorb about 25% of annual anthropogenic carbon-dioxide emissions [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)-in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R 2 ) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R 2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

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“…Although NFIs from different countries have been shaped by local information needs, the work done by the Food and Agriculture Organization (FAO) in conducting global FRAs since 1948 has aided in developing national forest reporting standards. Currently, new assessments are carried out every 5 years and the 2015 assessment covered 93.5 % of the global forest area (Köhl et al, 2015). Thus, as the aim of an FRA is to describe the state and change of the world's forests and keep policy makers informed, the same data could potentially be used to describe the current state of forests in land models.…”

Section: Discussionmentioning

confidence: 99%

An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

et al. 2018

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Abstract. Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface-atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAI max ), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAI max ) of managed forests in Fennoscandia, we compared our LAI max map with reference LAI max maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAI max values used in three leading land models. Comparison of the LAI max maps showed that our product provides a spatially more realistic description of LAI max in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

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Changes in forest production, biomass and carbon: Results from the 2015 UN FAO Global Forest Resource Assessment

Cited by 244 publications

References 27 publications

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

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