Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Heiskanen, Janne

doi:10.1080/01431160500353858

Cited by 162 publications

(113 citation statements)

References 46 publications

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“…Under a hypothetical situation with no changes in climate and stomatal conductance, increasing standing forest biomass should increase transpiration if the canopy LAI and root biomass increased. At least in the case of LAI, an increase in Sweden has been indicated by remote sensing and modelled data , and LAI and standing forest biomass are typically strongly correlated in Scandinavian forests (Heiskanen, 2006). Studies in many forest ecosystems across the world have found with reforestation a similar increase in evapotranspiration and/or decrease in runoff at the plot scale (Andréassian, 2004;Bosch and Hewlett, 1982;Sørensen et al, 2009) and basin scale (Fang et al, 2001;Huang et al, 2003;Li et al, 2016;Xu et al, 2014;X.…”

Section: Dominant Forest-related Effects On the Evaporative Ratiomentioning

confidence: 98%

Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space

Jaramillo

Cory

Arheimer

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. During the last 6 decades, forest biomass has increased in Sweden mainly due to forest management, with a possible increasing effect on evapotranspiration. However, increasing global CO 2 concentrations may also trigger physiological water-saving responses in broadleaf tree species, and to a lesser degree in some needleleaf conifer species, inducing an opposite effect. Additionally, changes in other forest attributes may also affect evapotranspiration. In this study, we aimed to detect the dominating effect(s) of forest change on evapotranspiration by studying changes in the ratio of actual evapotranspiration to precipitation, known as the evaporative ratio, during the period 1961-2012. We first used the Budyko framework of water and energy availability at the basin scale to study the hydroclimatic movements in Budyko space of 65 temperate and boreal basins during this period. We found that movements in Budyko space could not be explained by climatic changes in precipitation and potential evapotranspiration in 60 % of these basins, suggesting the existence of other dominant drivers of hydroclimatic change. In both the temperate and boreal basin groups studied, a negative climatic effect on the evaporative ratio was counteracted by a positive residual effect. The positive residual effect occurred along with increasing standing forest biomass in the temperate and boreal basin groups, increasing forest cover in the temperate basin group and no apparent changes in forest species composition in any group. From the three forest attributes, standing forest biomass was the one that could explain most of the variance of the residual effect in both basin groups. These results further suggest that the water-saving response to increasing CO 2 in these forests is either negligible or overridden by the opposite effect of the increasing forest biomass. Thus, we conclude that increasing standing forest biomass is the dominant driver of long-term and large-scale evapotranspiration changes in Swedish forests.

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Section: Dominant Forest-related Effects On the Evaporative Ratiomentioning

confidence: 98%

Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space

Jaramillo

Cory

Arheimer

et al. 2018

Hydrol. Earth Syst. Sci.

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show abstract

“…The descriptions of individual predictors are given in Deo et al [8] and USGS EROS [49]. These predictors were considered to simplify model and minimize error, although published works have reported varying sensitivity of the same predictors in different forest types [31,50,51]. Table 2.…”

Section: Landsat Time-series Data and Pixel Level Curve Fittingmentioning

confidence: 99%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)-in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R 2 ) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R 2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

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“…LAI is the ratio of leaf area per unit land area or the number of layers of leaves present above the ground (Chen and Black, 1992). LAI has a direct bearing on light interception by the canopy and biomass productivity in any crop (Tianxiang et al, 2002;Heiskanen, 2006). In the present study, we demonstrate for the first time use of satellite-based remote sensing technique as an easy, fast and powerful tool to monitor leaf retention by rubber plantations.…”

Section: Resultsmentioning

confidence: 64%

Satellite based remote sensing technique as a tool for real time monitoring of leaf retention in natural rubber plantations affected by abnormal leaf fall disease

Pradeep

Meti

Jose

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Most parts of the traditional natural rubber growing regions of India, extending from Kanyakumari district of Tamil Nadu in the South to Kasaragod district of Kerala in the North received excess and prolonged rains during 2013. This led to severe incidence of Abnormal Leaf Fall (ALF) disease caused by the fungus, <i>Phytophthora</i> sp. The present study demonstrated the first time use of satellite remote sensing technique to monitor ALF disease by estimating Leaf Area Index (LAI) in natural rubber holdings in near real time. Leaf retention was monitored in between April and December 2012 and 2013 by estimating LAI using MODIS 15A2 product covering rubber holdings spread across all districts in the traditional rubber growing region of the country that was mapped using Resourcesat LISS III 2012 and 2013 data. It was found that as the monsoon advanced, LAI decreased substantially in both years, but the reduction was much more substantial and prolonged in many districts during 2013 than 2012 reflecting increased leaf fall due to ALF disease in 2013. The decline was more pronounced in central and northern Kerala than in the South. Kanyakumari district of Tamil Nadu is generally known to be free from ALF disease, but there was considerable leaf loss due to ALF in June 2012 and June and July 2013 even as the monsoon was unusually severe in 2013. Weighted mean LAI during for the entire period of April to December was estimated as a weighted average of LAI and per cent of total area under rubber in each district in the study area for the two years. This was markedly less in 2013 than 2012. The implications of poor leaf retention for biomass production (net primary productivity), carbon sequestration and rubber yield are discussed.

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Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Cited by 162 publications

References 46 publications

Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space

Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Satellite based remote sensing technique as a tool for real time monitoring of leaf retention in natural rubber plantations affected by abnormal leaf fall disease

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