LIVIVO - Search results -

Search results

Result 1 - 10 of total 69

Search options

Book ; Online: Monitoring Forest Carbon Sequestration with Remote Sensing

Du, Huaqiang / Fan, Wenyi / Li, Mingshi / Fan, Weiliang / Mao, Fangjie

2023

Keywords	Research & information: general ; Mathematics & science ; Probability & statistics ; forest height ; synthetic aperture radar (SAR) ; interferometry ; random volume over ground (RVoG) model ; three-stage inversion method ; bamboo forest ; BEPS model ; gross primary productivity ; net primary productivity ; spatiotemporal evolution ; climate change ; backscatter coefficients ; polarization decomposition ; collinearity ; ridge regression ; RF ; PCA ; aboveground carbon density ; LiDAR ; stratified estimation ; machine learning algorithm ; Northeast China ; canopy closure ; the GOST model ; fisheye camera photos ; transects ; LAI ; forest height inversion ; three-stage algorithm ; coherence optimization ; complex coherence amplitude inversion ; SRTM ; random forest ; stochastic gradient boosting ; random forest Kriging ; wavelet analysis ; carbon storage ; land use/cover change ; scenario simulation ; PLUS model ; InVEST model ; remote sensing inversion ; dynamic change ; driving factors ; Shaoguan City ; above-ground biomass (AGB) ; airborne LiDAR ; airborne hyperspectral ; wavelet transform ; feature fusion ; Landsat time-series ; VCT model ; classifying forest types ; forest aboveground biomass ; forest aboveground biomass (AGB) ; scale effect ; random forest (RF) ; scale correction ; phenology ; dynamic threshold method ; northeast China ; TIMESAT ; forest carbon stocks ; simulation ; LUCC ; multi-source data ; feature selection ; aboveground biomass ; habitat dataset ; Landsat 8-OLI images ; pine forest ; model comparison ; 3D green volume ; UAV-Lidar ; urban forest ; random forest model ; remote sensing ; MODIS ; FY-3C VIRR ; Yunnan Province ; mangrove forests ; Hainan Island ; deep learning ; influential mechanism ; Bayesian hierarchical modelling ; geostatistics ; Eucalyptus grandis ; Eucalyptus camaldulensis ; Pinus patula ; spatial random effects ; spatially varying coefficient ; rubber plantation ; time series ; shapelet ; Landsat ; Pinus densata ; terrain niche index ; dynamic model ; canopy volume ; diameter at breast height (DBH) ; aboveground biomass (AGB) ; stem volume (V) ; near-infrared reflectance of vegetation ; carbon budget ; L-band PolInSAR ; RVoG model ; forest density ; terrain slope ; coherence ; extinction coefficient ; signal penetration ; 3-PG model ; eucalyptus ; forest age ; forest structure ; sensitivity ; clumping index ; estimation ; impact analysis ; field measurement ; Sentinel-2 images ; artificial neural network ; random forests ; quantile regression neural network ; Pinus densata forests
Language	English
Size	1 electronic resource (652 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel
Document type	Book ; Online
Note	English
HBZ-ID	HT030377968
ISBN	9783036572093 ; 3036572090
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: An improved framework for assessing the impact of different urban development strategies on land cover and ecological quality changes -A case study from Nanjing Jiangbei New Area, China

Shi, Fang / Yang, Boxiang / Li, Mingshi

Ecological Indicators. , p.109998-

2023 , Page(s) 109998–

Abstract: Land cover (LC) change has been highly valued by policy makers for driving the evolution of regional ecological and environmental quality. The core area of Nanjing Jiangbei New Area (NJNA) has undergone the development process from traditional ... ...

Abstract	Land cover (LC) change has been highly valued by policy makers for driving the evolution of regional ecological and environmental quality. The core area of Nanjing Jiangbei New Area (NJNA) has undergone the development process from traditional urbanization to new-type urbanization (NTU), however the impacts of different urban development strategies or pathways on the change pace and direction of land cover and ecological quality are not adequately understood in a quantitative manner which is not conducive for similar regions to learn lessons from past development processes. Based on RapidEye, GF-1 and SPOT-6 images acquired in 2009, 2015 and 2021, a Modified Mask-RCNN framework was devised and implemented to create a high accuracy LC dataset first in this analysis. Then, the applicability or efficacy of the Remote Sensing Ecological Index (RSEI) model and the Regional Optimized Remote Sensing Ecological Index (RO-RSEI) model, developed from the Landsat observations was compared in characterizing the ecological quality. Finally, the attribution analysis of spatio-temporal changes in LC and their induced ecological quality evolutions were carried out in conjunction with socio-economic factors. The results showed that the overall accuracies of the LC classifications were estimated at 91.50% (2009), 91.25% (2015), and 92.75% (2021), respectively. The mean values of RO-RSEI analyses were at 0.652, 0.649, and 0.655, respectively, indicating that in the past 12 years, the ecological quality first got degraded then got ameliorated along with the urbanization. The urban green spaces got restored and increased in the NTU process, and protecting the forest and cropland was also reinforced thanks to the strict enforcement of related policies. These positive changes are attributed to the popularization of ecological civilization philosophy and the escalation of social awareness of environmental protection and low carbon development. The proposed framework can provide technical reference for ecological quality monitoring under the progress of NTU.
Keywords	Landsat ; carbon ; case studies ; cropland ; data collection ; environmental protection ; environmental quality ; forests ; issues and policy ; land cover ; models ; philosophy ; socioeconomics ; urbanization ; China ; Land Cover Classification ; Ecological Environment Quality ; Modified Mask-RCNN ; RO-RSEI ; New-type Urbanization
Language	English
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Pre-press version ; Use and reproduction
ZDB-ID	2036774-0
ISSN	1872-7034 ; 1470-160X
ISSN (online)	1872-7034
ISSN	1470-160X
DOI	10.1016/j.ecolind.2023.109998
Database	NAL-Catalogue (AGRICOLA)

In stock of ZB MED Bonn / Germany

Z 7436: Show issues

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: An improved spatio-temporal clustering method for extracting fire footprints based on MCD64A1 in the Daxing’anling Area of north-eastern China

Su, Huiyi / Ma, Xiu / Li, Mingshi

International Journal of Wildland Fire. 2023, v. 32, no. 5 p.679-693

2023

Abstract: Background Understanding the spatio-temporal dynamics associated with a wildfire event is essential for projecting a clear profile of its potential ecological influences. Aims To develop a reliable framework to extract fire footprints from MODIS-based ... ...

Abstract	Background Understanding the spatio-temporal dynamics associated with a wildfire event is essential for projecting a clear profile of its potential ecological influences. Aims To develop a reliable framework to extract fire footprints from MODIS-based burn products to facilitate the understanding of fire event evolution. Methods This study integrated the Jenks natural breaks classification method and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the fire footprints in Daxing’anling region of China between 2001 and 2006 from MCD64A1 burned area data. Key results The results showed that the fire footprints extracted by the model gained an overall accuracy of 80% in spatial and temporal domains after an intensive validation by using the historical fire records provided by the local agency. The agreement of burned area between the extracted fire patches and the historical fire records for those matched fire points was characterised by an overall determination coefficient R 2 at 0.91. Conclusions The proposed framework serves as an efficient and convenient wildfire management tool for areas requiring large-scale and long-term wildfire monitoring. Implications The current framework can be used to create a reliable large-scale fire event database by providing an important alternative for the improvement of field investigation.
Keywords	algorithms ; databases ; evolution ; models ; wildfires ; wildland fire management ; China ; clustering ; Daxing’anling ; DBSCAN ; fire footprint ; Jenks natural breaks ; MCD64A1 ; remote sensing ; wildfire
Language	English
Size	p. 679-693.
Publishing place	CSIRO Publishing
Document type	Article ; Online
ZDB-ID	1331562-6
ISSN	1448-5516 ; 1049-8001
ISSN (online)	1448-5516
ISSN	1049-8001
DOI	10.1071/WF22198
Database	NAL-Catalogue (AGRICOLA)

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Bonn / Germany

Z 6872: Show issues

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

Article: A new method for monitoring start of season (SOS) of forest based on multisource remote sensing

Zhang, Yali / Li, Mingshi

ITC journal. 2021 Dec. 15, v. 104

2021

Abstract: As a sensitive indicator of climate change, forest phenology (e.g., the start of season (SOS)) has profound impacts on the global carbon cycle. Traditional phenological observations are based on surface observation networks. Generally, the outcomes of ... ...

Abstract	As a sensitive indicator of climate change, forest phenology (e.g., the start of season (SOS)) has profound impacts on the global carbon cycle. Traditional phenological observations are based on surface observation networks. Generally, the outcomes of this manner are less representative and hard to implement in wide forested regions. Remote sensing based observation of forest SOS has currently been a popular way. Those sensors with coarse spatial resolution have been widely used to estimate forest SOS, but they create serious estimation errors in areas of high heterogeneity. Medium-resolution sensors, such as Landsat, face significant challenges in SOS monitoring due to the long revisit period. In this study, we aimed to develop a new method to estimate forest SOS from 2013 to 2019. First, we collected all available Landsat and Sentinel-2 images, and then redefined the linear regression coefficients for the bandpass adjustment to weaken the surface reflectance (SR) differences in different sensors. Subsequently, we improved and developed the modified continuous change detection and classification (MCCDC) model to generate daily vegetation index curves. Finally, we adopted the logistic regression model to test the potential of the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI) and land surface water index (LSWI) in evaluating the annual SOS. The reduced root mean square error (RMSE) for all bands after the integration indicated that the adjustment was successful. We visually compared Landsat’s synthetic images with the actual acquired images and found that their respective false-colour composites were highly similar. Assessing the SOS from the EVI, NDVI and LSWI showed different estimated results. By comparing the annual SOS derived from the three indices with the field observations, it was found that the SOS based on the EVI maintained a low consistency with the field observations. The SOS accuracy from LSWI was the highest and most forest SOS from LSWI in the study area were mainly concentrated in 80–150 days. These three indices all showed that the SOS always fluctuated by ± 4 days from 2013 to 2019. Facing the lack of clear remote sensing images with medium spatial resolution in cloudy and rainy areas, this study proposes an improved method to generate clear daily vegetation index images at a spatial resolution of 30 m, making annual SOS monitoring promising and feasible.
Keywords	Landsat ; climate change ; forests ; global carbon budget ; phenology ; reflectance ; regression analysis ; surface water ; vegetation index
Language	English
Dates of publication	2021-1215
Publishing place	Elsevier B.V.
Document type	Article
ISSN	1569-8432
DOI	10.1016/j.jag.2021.102556
Database	NAL-Catalogue (AGRICOLA)

Full text online

Accessible to users with ZB MED library card

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾
- Full text online
- Order with fees

Article: Characterizing changes in land cover and forest fragmentation from multitemporal Landsat observations (1993-2018) in the Dhorpatan Hunting Reserve, Nepal

Zhang, Yali / Sharma, Sandeep / Bista, Manjit / Li, Mingshi

Journal of forestry research. 2022 Feb., v. 33, no. 1

2022

Abstract: Natural forces and anthropogenic activities greatly alter land cover, deteriorate or alleviate forest fragmentation and affect biodiversity. Thus land cover and forest fragmentation dynamics have become a focus of concern for natural resource management ... ...

Abstract	Natural forces and anthropogenic activities greatly alter land cover, deteriorate or alleviate forest fragmentation and affect biodiversity. Thus land cover and forest fragmentation dynamics have become a focus of concern for natural resource management agencies and biodiversity conservation communities. However, there are few land cover datasets and forest fragmentation information available for the Dhorpatan Hunting Reserve (DHR) of Nepal to develop targeted biodiversity conservation plans. In this study, these gaps were filled by characterizing land cover and forest fragmentation trends in the DHR. Using five Landsat images between 1993 and 2018, a support vector machine algorithm was applied to classify six land cover classes: forest, grasslands, barren lands, agricultural and built-up areas, water bodies, and snow and glaciers. Subsequently, two landscape process models and four landscape metrics were used to depict the forest fragmentation situations. Results showed that forest cover increased from 39.4% in 1993 to 39.8% in 2018. Conversely, grasslands decreased from 38.2% in 1993 to 36.9% in 2018. The forest shrinkage was responsible for forest loss during the period, suggesting that the loss of forest cover reduced the connectivity between forest and non-forested areas. Expansion was the dominant component of the forest restoration process, implying that it avoided the occurrence of isolated forests. The maximum value of edge density and perimeter area fractal dimension metrics and the minimum value of aggregation index were observed in 2011, revealing that forests in this year were most fragmented. These specific observations from the current analysis can help local authorities and local communities, who are highly dependent on forest resources, to better develop local forest management and biodiversity conservation plans.
Keywords	Landsat ; biodiversity ; biodiversity conservation ; data collection ; forest management ; forest restoration ; forests ; fractal dimensions ; habitat fragmentation ; land cover ; landscapes ; research ; shrinkage ; snow ; support vector machines ; Nepal
Language	English
Dates of publication	2022-02
Size	p. 159-170.
Publishing place	Springer Singapore
Document type	Article
ZDB-ID	2299615-1
ISSN	1993-0607 ; 1007-662X
ISSN (online)	1993-0607
ISSN	1007-662X
DOI	10.1007/s11676-021-01325-9
Database	NAL-Catalogue (AGRICOLA)

Full text online

Accessible to users with ZB MED library card

Order via subito

Details ▾
- Full text online
- Order with fees

Article ; Online: Spatial Downscaling of Forest Above-Ground Biomass Distribution Patterns Based on Landsat 8 OLI Images and a Multiscale Geographically Weighted Regression Algorithm

Wang, Nan / Sun, Min / Ye, Junhong / Wang, Jingyi / Liu, Qinqin / Li, Mingshi

Forests. 2023 Mar. 07, v. 14, no. 3

2023

Abstract: Forest above-ground biomass (AGB) is an excellent indicator for the health status and carbon sink potential of forest ecosystems, as well as the effectiveness of sustainable forest management practices. However, due to the strong heterogeneity of forest ... ...

Abstract	Forest above-ground biomass (AGB) is an excellent indicator for the health status and carbon sink potential of forest ecosystems, as well as the effectiveness of sustainable forest management practices. However, due to the strong heterogeneity of forest structures, acquiring high-accuracy and high-resolution AGB distributions over wide regions is often prohibitively expensive. To fill the resulting gap, this paper uses part of Lishui city, Zhejiang province as the study area, based on 168 forest sample observations, and proposes a novel integrated framework that combines a multi-scale geographically weighted regression (MGWR) with the co-kriging algorithm to refine the spatial downscaling of AGB. Specifically, optimal predictor variable sets identified by random forest importance ranking, multiple stepwise regression, and Pearson VIF methods were first assessed based on their total explanatory power (R square), followed by reconfirmation of the optimal predictor variable set based on the non-stationarity impact of each variable’s action scale (bandwidth) on the output pattern of AGB downscaling. The AGB downscaling statistical algorithms included MGWR, GWR, random forest (RF), and the ordinary least square (OLS), and their downscaling performances were quantitatively compared to determine the best downscaling method. Ultimately, the downscaled AGB pattern was produced using the best method, which was further refined by considering the spatial autocorrelation in AGB samples by implementing a co-kriging interpolation analysis of the predicted AGB downscaling residuals. The results indicated that the variable set selected by random forest importance ranking had the strongest explanatory power, with a validation R square of 0.58. This was further confirmed by the MGWR analysis which showed that the set of variables produced a more spatially smooth downscaled AGB pattern. Among the set of optimal variables, elevation and aspect affected AGB at local scales, representing a strong spatial heterogeneity. Some textural features and spectral features showed a smooth action scale relative to AGB, showing insignificant spatial scale processes. In the study area with complex terrain, using aspect as a covariant, the co-kriging (CK) model achieved a higher simulation accuracy for the MGWR-predicted AGB residuals than the ordinary kriging model. Overall, the proposed MGWR-CK model, with a final validation R square value of 0.62, effectively improved the spatial distribution characteristics and textural details of AGB mapping without the additional costs of procuring finer satellite images and GIS-based features. This will contribute to the accurate assessment of carbon sinks and carbon stock changes in subtropical forest ecosystems globally.
Keywords	Landsat ; aboveground biomass ; algorithms ; autocorrelation ; carbon ; carbon sinks ; health status ; kriging ; landscapes ; models ; spatial variation ; sustainable forestry ; tropical forests ; China
Language	English
Dates of publication	2023-0307
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f14030526
Database	NAL-Catalogue (AGRICOLA)

Order via subito

Article ; Online: Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models.

Su, Huiyi / Bista, Manjit / Li, Mingshi

Scientific reports

2021 Volume 11, Issue 1, Page(s) 14135

Abstract: Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species ... ...

Abstract	Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km
MeSH term(s)	Animals ; Animals, Wild/physiology ; Conservation of Natural Resources ; Ecosystem ; Forests ; Humans ; Nepal ; Parks, Recreational ; Ursidae/physiology
Language	English
Publishing date	2021-07-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-93540-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Book ; Online: Biological Valuation Map of Flanders

Li, Mingshi / Grujicic, Dusan / De Saeger, Steven / Heremans, Stien / Somers, Ben / Blaschko, Matthew B.

A Sentinel-2 Imagery Analysis

2024

Abstract: In recent years, machine learning has become crucial in remote sensing analysis, particularly in the domain of Land-use/Land-cover (LULC). The synergy of machine learning and satellite imagery analysis has demonstrated significant productivity in this ... ...

Abstract	In recent years, machine learning has become crucial in remote sensing analysis, particularly in the domain of Land-use/Land-cover (LULC). The synergy of machine learning and satellite imagery analysis has demonstrated significant productivity in this field, as evidenced by several studies. A notable challenge within this area is the semantic segmentation mapping of land usage over extensive territories, where the accessibility of accurate land-use data and the reliability of ground truth land-use labels pose significant difficulties. For example, providing a detailed and accurate pixel-wise labeled dataset of the Flanders region, a first-level administrative division of Belgium, can be particularly insightful. Yet there is a notable lack of regulated, formalized datasets and workflows for such studies in many regions globally. This paper introduces a comprehensive approach to addressing these gaps. We present a densely labeled ground truth map of Flanders paired with Sentinel-2 satellite imagery. Our methodology includes a formalized dataset division and sampling method, utilizing the topographic map layout 'Kaartbladversnijdingen,' and a detailed semantic segmentation model training pipeline. Preliminary benchmarking results are also provided to demonstrate the efficacy of our approach.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2024-01-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Mapping Spatiotemporal Changes in Forest Type and Aboveground Biomass from Landsat Long-Term Time-Series Analysis—A Case Study from Yaoluoping National Nature Reserve, Anhui Province of Eastern China

Yang, Boxiang / Zhang, Yali / Mao, Xupeng / Lv, Yingying / Shi, Fang / Li, Mingshi

Remote Sensing. 2022 June 10, v. 14, no. 12

2022

Abstract: A natural reserve’s forest is an important base for promoting natural education, scientific research, biodiversity conservation and carbon accounting. Dynamic monitoring of the forest type and forest aboveground biomass (AGB) in a nature reserve is an ... ...

Abstract	A natural reserve’s forest is an important base for promoting natural education, scientific research, biodiversity conservation and carbon accounting. Dynamic monitoring of the forest type and forest aboveground biomass (AGB) in a nature reserve is an important foundation for assessing the forest succession stage and trend. Based on the Landsat images covering the National Nature Reserve of Yaoluoping in Anhui province spanning from 1987 to 2020, a total of 42 Landsat scenes, the forest cover product set was first developed by using the well-established vegetation change tracker (VCT) model. On this basis, a new vegetation index, NDVI_DR, which considers the phenological characteristics of different forest types, was proposed to distinguish coniferous forest from broad-leaved forest. Next, multiple modeling factors, including remote sensing spectral signatures, vegetation indices, textural measures derived from gray level co-occurrence matrix and wavelet analysis and topographic attributes, were compiled to model the AGB in 2011 by forest type separately by using the stochastic gradient boosting (SGB) algorithm. Then, using the 2011 Landsat image as the base, all the Landsat images in the other years involved in the modelling were relatively normalized by using the weighted invariant pixels (WIP) method, followed by an extrapolation of the 2011 AGB model to other years to create a time-series of AGB. The results showed that the overall accuracy of the VCT-based forest classification products was over 90%. The annual forest type classifications derived from NDVI_DR thresholding gained an overall accuracy above 92%, with a kappa coefficient above 0.8. The 2011 forest-type-dependent SGB-based AGB estimation model achieved an independent validation R² at 0.63 and an RMSE at 11.18 t/ha for broad-leaved forest, and 0.61 and 14.26 t/ha for coniferous forest. The mapped time-series of AGB showed a gradual increasing trend over the past three decades. The driving factors responsible for the observed forest cover and AGB changes were analyzed to provide references for reasonable protection and development. The proposed methodology is a reliable tool for evaluating the management status, which can be extended to other similar regions.
Keywords	Landsat ; aboveground biomass ; algorithms ; biodiversity conservation ; carbon ; case studies ; coniferous forests ; conservation areas ; deciduous forests ; education ; forest succession ; models ; phenology ; time series analysis ; topography ; vegetation index ; wavelet ; China
Language	English
Dates of publication	2022-0610
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14122786
Database	NAL-Catalogue (AGRICOLA)

Order via subito

Article: Active forest management accelerates carbon storage in plantation forests in Lishui, southern China

Diao, Jiaojiao / Liu, Jinxun / Zhu, Zhiliang / Wei, Xinyuan / Li, Mingshi

Beijing Forestry University. Forest ecosystems. 2022 Dec., v. 9

2022

Abstract: China has committed to achieving peak CO₂ emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The ... ...

Abstract	China has committed to achieving peak CO₂ emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics. We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes. 1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 Mg·ha‒¹) and 64 years (232.04 Mg·ha‒¹), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 Tg C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest. This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO₂ emissions and reach carbon neutrality.
Keywords	Cunninghamia lanceolata ; Pinus massoniana ; Quercus ; aboveground biomass ; biosphere ; carbon ; carbon dioxide ; carbon sequestration ; carbon sinks ; climate change ; elderly ; energy ; forest damage ; forest management ; forest plantations ; forests ; industry ; simulation models ; China
Language	English
Dates of publication	2022-12
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	2760380-5
ISSN	2197-5620
ISSN	2197-5620
DOI	10.1016/j.fecs.2022.100004
Database	NAL-Catalogue (AGRICOLA)

Order via subito

To top

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Bonn / Germany

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

In stock of ZB MED Bonn / Germany

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito