Mapping forest dynamics under climate change: A matrix model

Liang, Jingjing; Zhou, Mo; Verbyla, David L.; Zhang, Lianjun; Springsteen, Anna L.; Malone, Thomas F.

doi:10.1016/j.foreco.2011.08.017

Cited by 37 publications

(26 citation statements)

References 46 publications

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“…Matrix models maybe a convenient tool to predict forest C dynamics (Ma et al 2017). Not unlike other models, the matrix models have their limitations, and our results should be interpreted in appropriate contexts (Liang et al 2011). One weakness of our study's matrix models is that their geographic coverage was limited to the study region in the eastern US.…”

Section: Me (Deciduous)mentioning

confidence: 96%

“…Prediction of AGB using LiDAR, Landsat, and forest inventory plots, which has been seldom studied but is key to local, regional, and national forest C monitoring programs, can now be analyzed by matrix models. Due to their simplicity and accuracy, the matrix models are also applicable to estimate forest population dynamics under climate change (Liang et al 2011), assessments of fire regime responses to changing climate (Ma et al 2016) and assessments of harvesting regimes under climate and fire uncertainty (Ma and Zhou 2017). In addition, the matrix models may also be incorporated with adaptive management of timber and biomass resources (Zhou et al 2008), land-use change (Woodall et al 2015b), and detailed regimes of natural disturbances (Wear and Coulston 2015).…”

Section: Me (Deciduous)mentioning

confidence: 99%

See 1 more Smart Citation

Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data

Wörns

Domke

D’Amato

et al. 2018

Environ. Res. Lett.

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The ability to harmonize data sources with varying temporal, spatial, and ecosystem measurements (e.g. forest structure to soil organic carbon) for creation of terrestrial carbon baselines is paramount to refining the monitoring of terrestrial carbon stocks and stock changes. In this study, we developed and examined the short-(5 years) and long-term (30 years) performance of matrix models for incorporating light detection and ranging (LiDAR) strip samples and time-series Landsat surface reflectance high-level data products, with field inventory measurements to predict aboveground biomass (AGB) dynamics for study sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC). The rows and columns of the matrix were stand density (i.e. number of trees per unit area) sorted by inventory plot and by species group and diameter class. Through model comparisons in the short-term, we found that average stand basal area (B) predicted by three matrix models all fell within the 95% confidence interval of observed values. The three matrix models were based on (i) only field inventory variables (inventory), (ii) LiDAR and Landsat-derived metrics combined with field inventory variables (LiDAR+Landsat+inventory), and (iii) only Landsat-derived metrics combined with field inventory variables (Landsat+inventory), respectively. In the long term, predicted AGB using LiDAR+Landsat+inventory and Land-sat+inventory variables had similar AGB patterns (differences within 7.2 Mg ha −1 ) to those predicted by matrix models with only inventory variables from 2015-2045. When considering uncertainty derived from fuzzy sets all three matrix models had similar AGBs (differences within 7.6 Mg ha −1 ) by the year 2045. Therefore, the use of matrix models enabled various combinations of LiDAR, Landsat, and field data, especially Landsat data, to estimate large-scale AGB dynamics (i.e. central component of carbon stock monitoring) without loss of accuracy from only using variables from forest inventories. These findings suggest that the use of Landsat data alone incorporating elevation (E), plot slope (S) and aspect (A), and site productivity (C) could produce suitable estimation of AGB dynamics (ranging from 67.1-105.5 Mg ha −1 in 2045) to actual AGB dynamics using matrix models. Such a framework may afford refined monitoring and estimation of terrestrial carbon stocks and stock changes from spatially explicit to spatially explicit and spatially continuous estimates and also provide temporal flexibility and continuity with the Landsat time series.

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Section: Me (Deciduous)mentioning

confidence: 96%

Section: Me (Deciduous)mentioning

confidence: 99%

Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data

Wörns

Domke

D’Amato

et al. 2018

Environ. Res. Lett.

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“…Climate condition, forest fuels, ignition agents, topography and human activities are five major factors that strongly influence forest fire activity and forest dynamics [35][36][37][38]. Forest fires, in turn, impact climate conditions, plant ecosystems and human life [2,3,39], and vegetation responds and adapts to changes in the environment by establishing an appropriate structure and composition as a consequence [40][41][42][43].…”

Section: Forest Fire and Forest Pattern Terminologymentioning

confidence: 99%

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

2013

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Abstract:The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre-and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions.

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“…A recent review article notes that matrix models have already been used in almost all possible situations related to forestry [23], including large-scale mapping of timber, biomass resources assuming constant climate [24,25], and under climate change scenarios [26]. However, the authors of the review [23] understand by "Matrix models" only the tree-based diameter class models and do not cover the use of area-based matrix models for large-scale applications.…”

Section: Introductionmentioning

confidence: 99%

An Area-Based Matrix Model for Uneven-Aged Forests

Sallnäs

Berger

Räty

et al. 2015

Forests

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Abstract:In this paper a new concept for modeling uneven-aged forests (UEAF) is presented. The term UEAF in this article encloses all forests that deviate from the even-aged structure. The matrix model is area-based, in that the forest under study is described by a distribution of areas over fixed state-spaces spanned by stem number and volume per hectare classes. Dynamics is introduced as transitions of areas inside the state-space during the simulation. Harvesting activities and the occurrence of calamities are explicitly handled. The model is designed to be suitable for large-scale analyses. The concept was tested in an application to Austrian National Forest Inventory (NFI) data. Results shown, including a comparison to older inventory data, indicate that it is worth further elaborating on the concept and the model. The work will be continued and in the next step the model concept will be applied in several other countries.

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Mapping forest dynamics under climate change: A matrix model

Cited by 37 publications

References 46 publications

Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data

Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

An Area-Based Matrix Model for Uneven-Aged Forests

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