Discrepancies between Global Forest Net Primary Productivity Estimates Derived from MODIS and Forest Inventory Data and Underlying Factors

Park, Jin Han; Park, Chan

doi:10.3390/rs13081441

Cited by 11 publications

(5 citation statements)

References 38 publications

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“…Our NPP estimates tend to be slightly higher, which is mainly due to the fact that our workflow can detect structural changes in the forest, and thus also forest degradation. MODIS generally tends to produce lower NPP estimates, which has already been discussed in other studies [15,34].…”

Section: Impact Of Fragmentation On Edge and Core Forestssupporting

confidence: 58%

Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity

Bauer,

Huth,

Bogdanowski

et al. 2024

Remote Sensing

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The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality (“edge effects”). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region.

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Section: Impact Of Fragmentation On Edge and Core Forestssupporting

confidence: 58%

Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity

Bauer,

Huth,

Bogdanowski

et al. 2024

Remote Sensing

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“…In addition to the methods used in this article, there are also many methods to evaluate the carbon sink level by remote sensing technology. The data sources include NPP [73,74], LiDAR [75], carbon satellites [76], and so on. However, these methods are mainly used to evaluate the past change trend or the current carbon storage level and cannot predict the future situation.…”

Section: Applicability and Limitationsmentioning

confidence: 99%

How to Account for Changes in Carbon Storage from Coal Mining and Reclamation in Eastern China? Taking Yanzhou Coalfield as an Example to Simulate and Estimate

Han

Mao

et al. 2022

Remote Sensing

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Carbon sequestration in terrestrial ecosystems plays an essential role in coping with global climate change and achieving regional carbon neutrality. In mining areas with high groundwater levels in eastern China, underground coal mining has caused severe damage to surface ecology. It is of practical significance to evaluate and predict the positive and negative effects of coal mining and land reclamation on carbon pools. This study set up three scenarios for the development of the Yanzhou coalfield (YZC) in 2030, including: (1) no mining activities (NMA); (2) no reclamation after mining (NRM); (3) mining and reclamation (MR). The probability integral model (PIM) was used to predict the subsidence caused by mining in YZC in 2030, and land use and land cover (LULC) of 2010 and 2020 were interpreted by remote sensing images. Based on the classification of land damage, the LULC of different scenarios in the future was simulated by integrating various social and natural factors. Under different scenarios, the InVEST model evaluated carbon storage and its temporal and spatial distribution characteristics. The results indicated that: (1) By 2030, YZC would have 4341.13 ha of land disturbed by coal mining activities. (2) Carbon storage in the NRM scenario would be 37,647.11 Mg lower than that in the NMA scenario, while carbon storage in the MR scenario would be 18,151.03 Mg higher than that in the NRM scenario. Significantly, the Nantun mine would reduce carbon sequestration loss by 72.29% due to reclamation measures. (3) Carbon storage has a significant positive spatial correlation, and coal mining would lead to the fragmentation of the carbon sink. The method of accounting for and predicting carbon storage proposed in this study can provide data support for mining and reclamation planning of coal mine enterprises and carbon-neutral planning of government departments.

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“…Net primary productivity (NPP) refers to the total amount of organic matter accumulated by vegetation in terrestrial ecosystems per unit of time and space [1], which is calculated as the difference between the carbon absorbed through photosynthesis and the carbon released through respiration [2]. This concept reflects the production capacity of vegetation under comprehensive influence (climate, terrain, human factors, pathogen, etc.)…”

Section: Introductionmentioning

confidence: 99%

Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China

Zhao,

Jia,

Liu

2023

Sustainability

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The objective of this study is to analyze the spatiotemporal dynamics of net primary production (NPP) change combined with land use and to further explore the driving factors of NPP change, allowing us to provide a scientific reference point for optimizing the land-use structure and improving regional carbon sequestration capacity. The average annual NPP ranged from 200 to 300 gC/m2•a in the period of 2001–2020 in our study area. We used trend analysis and linear regression analysis to explore the spatial and temporal dynamic changes in annual NPP and analyzed the driving mechanism in a semiarid region (western Jilin Province) of China in the period of 2001–2020. The results showed that NPP presented a trend of fluctuating growth, and the spatial distribution of NPP showed that NPP values of cultivated land, forest and grassland were generally higher than those of other land-use types. The high value in the southeastern region and low value in the northwestern region were identified because there were large areas of cultivated land distributed in the southeastern part of the study area in the period of 2001–2020. The main driving factors that affected NPP were annual precipitation, CO2 emissions, GDP and hours of sunshine. NPP was positively correlated with annual precipitation, CO2 emissions and GDP, and it had a significant negative correlation with hours of sunshine. Our study provides important support for research into land-use structure and improvements to the regional carbon sequestration capacity, making an important contribution to regional sustainable development.

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Discrepancies between Global Forest Net Primary Productivity Estimates Derived from MODIS and Forest Inventory Data and Underlying Factors

Cited by 11 publications

References 38 publications

Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity

Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity

How to Account for Changes in Carbon Storage from Coal Mining and Reclamation in Eastern China? Taking Yanzhou Coalfield as an Example to Simulate and Estimate

Dynamic Changes and Driving Mechanisms of Net Primary Production (NPP) in a Semi-Arid Region of China

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