Estimation of biomass, net primary production and net ecosystem production of China's forests based on the 1999–2003 National Forest Inventory

2010 ◽  
Vol 25 (6) ◽  
pp. 544-553 ◽  
Author(s):  
Bin Wang ◽  
Juying Huang ◽  
Xiaosheng Yang ◽  
Biao Zhang ◽  
Moucheng Liu
Keyword(s):  
Primary Production ◽  
Forest Inventory ◽  
Net Primary Production ◽  
National Forest Inventory ◽  
Net Ecosystem Production ◽  
National Forest ◽  
Ecosystem Production

scholarly journals Land Cover and Land Use Change Decreases Net Ecosystem Production in Tropical Peatlands of West Kalimantan, Indonesia

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1587
Author(s):  
Imam Basuki ◽  
J. Boone Kauffman ◽  
James T. Peterson ◽  
Gusti Z. Anshari ◽  
Daniel Murdiyarso
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Soil Respiration ◽  
Primary Production ◽  
Oil Palm ◽  
Net Primary Production ◽  
Net Ecosystem Production ◽  
Carbon Sinks ◽  
Peat Swamp ◽  
Ecosystem Production

Deforested and converted tropical peat swamp forests are susceptible to fires and are a major source of greenhouse gas (GHG) emissions. However, information on the influence of land-use change (LUC) on the carbon dynamics in these disturbed peat forests is limited. This study aimed to quantify soil respiration (heterotrophic and autotrophic), net primary production (NPP), and net ecosystem production (NEP) in peat swamp forests, partially logged forests, early seral grasslands (deforested peat), and smallholder-oil palm estates (converted peat). Peat swamp forests (PSF) showed similar soil respiration with logged forests (LPSF) and oil palm (OP) estates (37.7 Mg CO2 ha−1 yr−1, 40.7 Mg CO2 ha−1 yr−1, and 38.7 Mg CO2 ha−1 yr−1, respectively), but higher than early seral (ES) grassland sites (30.7 Mg CO2 ha−1 yr−1). NPP of intact peat forests (13.2 Mg C ha−1 yr−1) was significantly greater than LPSF (11.1 Mg C ha−1 yr−1), ES (10.8 Mg C ha−1 yr−1), and OP (3.7 Mg C ha−1 yr−1). Peat swamp forests and seral grasslands were net carbon sinks (10.8 Mg CO2 ha−1 yr−1 and 9.1 CO2 ha−1 yr−1, respectively). In contrast, logged forests and oil palm estates were net carbon sources; they had negative mean Net Ecosystem Production (NEP) values (−0.1 Mg CO2 ha−1 yr−1 and −25.1 Mg CO2 ha−1 yr−1, respectively). The shift from carbon sinks to sources associated with land-use change was principally due to a decreased Net Primary Production (NPP) rather than increased soil respiration. Conservation of the remaining peat swamp forests and rehabilitation of deforested peatlands are crucial in GHG emission reduction programs.

Temporal changes of forest net primary production and net ecosystem production in west central Canada associated with natural and anthropogenic disturbances

2003 ◽  
Vol 33 (12) ◽  
pp. 2340-2351 ◽  
Author(s):  
Zhong Li ◽  
Michael J Apps ◽  
Werner A Kurz ◽  
Ed Banfield
Keyword(s):  
Primary Production ◽  
Net Primary Production ◽  
Anthropogenic Disturbances ◽  
Temporal Variations ◽  
Net Ecosystem Production ◽  
Forest Sector ◽  
Natural Disturbances ◽  
Carbon Budget Model ◽  
West Central ◽  
Ecosystem Production

Temporal variations of net primary production (NPP) and net ecosystem production (NEP) in west central Canadian forests over the period of 1920–1995 and their responses to natural and anthropogenic disturbances were simulated using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS2). The results show that forest NPP in the region was 215 g C·year–1·m–2 in 1920, varied between 105 and 317 g·C year–1·m–2 depending on ecoclimatic province, but gradually increased to 330 (158 to 395) g C·year–1·m–2 in the early 1980s before declining to 290 (148 to 395) g C·year–1·m–2 by 1995. Forest NEP was estimated to be 53 (–13 to 88) g C·year–1·m–2 in 1920–1924, increased to 75 (5 to 98) g C·year–1·m–2 in 1960, and then declined to 26 (–14 to 53) g C·year–1·m–2 in 1991–1995. Natural disturbances played a greater role than harvest in determining the temporal pattern of forest NPP and NEP during the period because of the larger area affected by natural disturbances. This study also indicated that ignoring disturbances would lead to an overestimation of forest NPP and NEP in ecosystem modeling.

scholarly journals Impact of nitrogen fertilization on soil respiration and net ecosystem production in maize

2018 ◽  
Vol 64 (No. 8) ◽  
pp. 353-360 ◽  
Author(s):  
Lamptey Shirley ◽  
Li Lingling ◽  
Xie Junhong
Keyword(s):  
Soil Respiration ◽  
Primary Production ◽  
Soil Water ◽  
Nitrogen Fertilization ◽  
Field Experiments ◽  
Net Primary Production ◽  
Net Ecosystem Production ◽  
Water Dynamics ◽  
Ecosystem Production ◽  
Heavy Carbon

Agriculture in the semi-arid is often challenged by overuse of nitrogen (N), inadequate soil water and heavy carbon emissions thereby threatening sustainability. Field experiments were conducted to investigate the effect of nitrogen fertilization levels (N<sub>0</sub> – 0, N<sub>100</sub> – 100, N<sub>200</sub> – 200, N<sub>300</sub> – 300 kg N/ha) on soil water dynamics, soil respiration (Rs), net ecosystem production (NEP), and biomass yields. Zero nitrogen soils decreased Rs by 23% and 16% compared to N<sub>300</sub> and N<sub>200</sub> soils, respectively. However, biomass yield was greatest under N<sub>300</sub> compared with N<sub>0</sub>, which therefore translated into increased net primary production by 89% and NEP by 101% compared to N<sub>0</sub>. To a lesser extent, N<sub>200</sub> increased net primary production by 69% and net ecosystem production by 79% compared to N<sub>0</sub>. Grain yields were greatest under N<sub>300</sub> compared with N<sub>100</sub> and N<sub>0</sub>, which therefore translated into increased carbon emission efficiency (CEE) by 53, 39 and 3% under N<sub>300</sub> compared to N<sub>0</sub>, N<sub>100</sub> and N<sub>200</sub> treatments, respectively. There appears potential for 200 kg N/ha to be used to improve yield and increase CEE.

Steigende Bestandszahlen bei Spechten und anderen Vogelarten dank Zunahme von Totholz? | An increase in the population of woodpeckers and other bird species thanks to an increase in the quantities of deadwood?

2009 ◽  
Vol 160 (11) ◽  
pp. 334-340 ◽  
Author(s):  
Pierre Mollet ◽  
Niklaus Zbinden ◽  
Hans Schmid
Keyword(s):  
Forest Inventory ◽  
Bird Species ◽  
National Forest Inventory ◽  
Population Increase ◽  
National Forest ◽  
Forest Species ◽  
Breeding Populations ◽  
Willow Tit ◽  
Middle Spotted Woodpecker ◽  
Important Habitat

Results from the monitoring programs of the Swiss Ornithological Institute show that the breeding populations of several forest species for which deadwood is an important habitat element (black woodpecker, great spotted woodpecker, middle spotted woodpecker, lesser spotted woodpecker, green woodpecker, three-toed woodpecker as well as crested tit, willow tit and Eurasian tree creeper) have increased in the period 1990 to 2008, although not to the same extent in all species. At the same time the white-backed woodpecker extended its range in eastern Switzerland. The Swiss National Forest Inventory shows an increase in the amount of deadwood in forests for the same period. For all the mentioned species, with the exception of green and middle spotted woodpecker, the growing availability of deadwood is likely to be the most important factor explaining this population increase.

scholarly journals A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Matieu Henry ◽  
Zaheer Iqbal ◽  
Kristofer Johnson ◽  
Mariam Akhter ◽  
Liam Costello ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Data Collection ◽  
Land Cover ◽  
Forest Inventory ◽  
Sustainable Forest Management ◽  
National Forest Inventory ◽  
Forest Resources ◽  
National Forest ◽  
Land Cover Map

Abstract Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

scholarly journals Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Johannes Schumacher ◽  
Marius Hauglin ◽  
Rasmus Astrup ◽  
Johannes Breidenbach
Keyword(s):  
Forest Inventory ◽  
Regression Models ◽  
Laser Scanning ◽  
Site Index ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
National Forest ◽  
Practical Applications ◽  
Airborne Laser ◽  
Sentinel 2

Abstract Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

scholarly journals Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Caileigh Shoot ◽  
Hans-Erik Andersen ◽  
L. Monika Moskal ◽  
Chad Babcock ◽  
Bruce D. Cook ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Inventory ◽  
Forest Type ◽  
Vegetation Indices ◽  
National Forest Inventory ◽  
Classification Algorithm ◽  
Lidar Data ◽  
National Forest ◽  
Interior Alaska ◽  
Type Information

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

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