Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

Fuchs, Hans U.; Magdon, Paul; Kleinn, Christoph; Flessa, Heiner

doi:10.1016/j.rse.2008.07.017

Cited by 141 publications

(113 citation statements)

References 46 publications

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“…Regression analysis resulted in adjusted R 2 values up to 0.96 and 0.94 for net carbon and water fluxes, respectively. Airborne LiDAR (García et al 2010) and QuickBird and ASTER spectral, texture, and transformation features (Fuchs et al 2009) have also been used to assess carbon stocks. Regarding pest control, several studies detected defoliation and other effects and may be used to infer the resistance of a study area to pest attack.…”

Section: Regulating Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Regulating Servicesmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…Semi-empirical approaches combine both empirical and physical modelling, e.g., by using the output from CR models to train neural networks to estimate biophysical parameters [235]. [239]; (AGB), [240,241] Ordinary least squares (height, density, DBH), [242] Reduced major axis (AGB), [243]; (LAI), [244] Canonical Correlation Analysis (forest structural conditions), [222] Redundancy Analysis (forest structural conditions), [245,246] Trend analysis (growth), [247] Non-parametric regression kNN (AGB, carbon), [248] CART (tree cover), [249]; (basal area, no. of trees) [250] RF (AGB) [243,251] SVM (height, density, DBH), [242] Physical Radiative transfer/canopy reflectance model Geometric-Optical (LAI), [252]; (AGB), [253]; (Chlorophyll), [254] Turbid-medium (LAI), [255] hybrid (allometry), [256] Computer simulation…”

Section: Physical Vs Empirical Modelsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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“…NDVI images were derived for each site, and individual sample location values were extracted. Sample NDVI values were interpreted from the adjacent cells (n ‫ס‬ 12) using bilinear interpolation to decrease any effects of misregistration errors that result from matching imagery with sample locations (Fuchs et al, 2009).…”

Section: Satellite Remote Sensing Datamentioning

confidence: 99%

“…The use of high spatial-resolution multispectral data (e.g. IKONOS data [4 m spatial resolution]) has been minimally investigated in Arctic environments (Laidler et al, 2008;Fuchs et al, 2009). The improvement in satellite spatial resolving power provides enhanced capacity for the estimation of biophysical variables and the prediction of carbon flux values (Laidler et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Spatially continuous data characterizing vegetation and terrain patterns at regional and landscape scales can be acquired with satellite-based optical remote sensing and various spectral derivatives (Boelman et al, 2003;Laidler et al, 2008;Fuchs et al, 2009). With a greater understanding of spectral response to variations in biophysical variables (i.e., vegetation patterns, structure, and ecological site factors), remote sensing data may be able to predict and monitor changes in CO 2 flux patterns over time (Stow et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

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Vegetation Community, Foliar Nitrogen, and Temperature Effects on Tundra CO₂Exchange across a Soil Moisture Gradient

Dagg

Lafleur

2011

Arctic, Antarctic, and Alpine Research

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Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

Cited by 141 publications

References 46 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Vegetation Community, Foliar Nitrogen, and Temperature Effects on Tundra CO₂Exchange across a Soil Moisture Gradient

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Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

Cited by 141 publications

References 46 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Vegetation Community, Foliar Nitrogen, and Temperature Effects on Tundra CO2Exchange across a Soil Moisture Gradient

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Resources

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Vegetation Community, Foliar Nitrogen, and Temperature Effects on Tundra CO₂Exchange across a Soil Moisture Gradient