Applying dendrochronology and remote sensing to explore climate-drive in montane forests over space and time

Zhou, Yang; Yi, Yujun; Jia, Wenfei; Cai, Yanpeng; Yang, Wei; Li, Zhiwei

doi:10.1016/j.quascirev.2020.106292

Cited by 13 publications

(6 citation statements)

References 55 publications

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“…Direct measurements of tree productivity on historical scales are available from dendrochronological studies that use the annual radial stem increment (tree ring width; TRW) as a measure of tree growth and thus are able to produce time series of ecosystem productivity. Depending on the climatic conditions, annual stem increment in boreal of forest can either be limited by low temperatures or drought during the growing season (Beck et al, 2011;Hauck et al, 2019;Zhou et al, 2020). As the result of climate warming, boreal forests are increasingly switching from temperature-limited to drought-limited ecosystems (D'Arrigo et al, 2004;Buermann et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

Erasmi

Klinge

Dulamsuren

et al. 2021

Environ Monit Assess

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The monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

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Section: Introductionmentioning

confidence: 99%

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

Erasmi

Klinge

Dulamsuren

et al. 2021

Environ Monit Assess

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“…Some studies have detected strong correlations between tree-ring indexes and vegetation indexes (e.g., NDVI), and long-term sequences of vegetation change have been reconstructed on the basis of significant correlations [24][25][26][27]. Some studies have also shown that tree-ring parameters are decoupled from NDVI [28,29], and that many factors can affect the relationship between tree radial growth and the vegetation canopy, including the forest type, latitude, elevation, topography, and the spatial resolution of remote sensing data [30][31][32][33][34][35][36][37][38]. Therefore, the factors and processes affecting the relationship between tree-ring parameters and vegetation indexes are complex.…”

Section: Study Of the Relationships Between Tree-ring Parameters And ...mentioning

confidence: 99%

Vegetation Index Research on the Basis of Tree-Ring Data: Current Status and Prospects

Zhang,

Song,

Fan

et al. 2023

Forests

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The normalized difference vegetation index (NDVI) and tree-ring parameters are commonly used indicators in the research on forest ecology and responses to climate change. This paper compiles and analyzes the literature on vegetation index research on the basis of tree-ring information in the past 20 years and provides an overview of the relationship between tree-ring parameters and NDVI, as well as NDVI reconstruction. The research on the vegetation index based on tree-ring data is mainly concentrated in the middle and high latitudes, and relatively few studies are concentrated in the low latitudes. The tree-ring parameters have a strong correlation with the NDVI in the summer. In terms of tree-ring reconstruction NDVI, Sabina przewalskii is the tree with the longest reconstruction sequence so far, and the tree-ring width is the main proxy index. In addition, combining tree rings with the NDVI is useful for assessing forest decline, quantifying the forest response to drought, and monitoring forest productivity. In the future, it is necessary to consider a variety of environmental factors to find the optimal model construction parameters and carry out research on the climate response of forest tree growth and the reconstruction of the historical sequence of the vegetation index at large spatial scales.

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“…However, dendrochronological data operate on a tree to plot scale, collecting data over several years by means of a systematic design. These limitations have led some studies to explore the possibility of integrating tree-ring and remote-sensing data for stand-level estimation of forest parameters including changes in growth [28,29]. The use of remotely sensed data from technologies such as Airborne Laser Scanning (LiDAR) enables the precise estimation of various forest stand parameters such as changes in tree volume, supporting informed decision making in forest management [30].…”

Section: Introductionmentioning

confidence: 99%

Integrating Dendrochronological and LiDAR Data to Improve Management of Pinus canariensis Forests under Different Thinning and Climatic Scenarios

Navarro-Cerrillo,

Padrón Cedrés,

Cachinero-Vivar

et al. 2024

Remote Sensing

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Thinning focused on achieving growth and diameter management objectives has typically led to stands with reduced climate sensitivity compared to unthinned stands. We integrated dendrochronological with Airborne Laser Scanner (LiDAR) data and growth models to assess the long-term impact of thinning intensity on Canary pine (Pinus canariensis) radial growth. In 1988, 18 permanent treatment units were established in 73-year-old Canary pine plantations and three thinning treatments were applied (C–control-unthinned; 0% basal area removal; MT–moderate thinning: 10% and 15% basal area removal, and HT–heavy thinning: 46% and 45% basal area removal on the windward and leeward slopes, respectively). Dendrochronological data were measured in 2022 and expressed as basal area increment (BAI). The impact of climate on growth was examined by fitting linear regression models considering two different Representative Concentration Pathway (RCP) climate scenarios, RCP 2.6 and RCP 4.5. Finally, LiDAR data were used for standing segmentation to evaluate changes in overall growth under different climatic scenarios. The LiDAR–stand attributes differed between aspects. The BAI of the most recent 20 years (BAI20) after thinning was significantly higher for the moderate and heavy treatments on the leeward plots (F = 47.31, p < 0.001). On the windward plots, BAI decreased after moderate thinning. Considerable thinning treatments resulted in stronger changes in growth when compared to RCP climatic scenarios. From a silviculture perspective, the mapping of canopy structure and growth response to thinning under different climatic scenarios provides managers with opportunities to conduct thinning strategies for forest adaptation. Combining dendrochronological and LiDAR data at a landscape scale substantially improves the value of the separate datasets as forecasted growth response maps allow improving thinning management plans.

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Applying dendrochronology and remote sensing to explore climate-drive in montane forests over space and time

Cited by 13 publications

References 55 publications

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

Vegetation Index Research on the Basis of Tree-Ring Data: Current Status and Prospects

Integrating Dendrochronological and LiDAR Data to Improve Management of Pinus canariensis Forests under Different Thinning and Climatic Scenarios

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