Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Suchenwirth, Leonhard; Stümer, Wolfgang; Schmidt, Tobias; Förster, Michael; Kleinschmit, Birgit

doi:10.3390/f5071635

Cited by 16 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Suchenwirth et al . (, ) distinguished riparian vegetation types and used high spatial resolution remote sensing data to estimate carbon stocks along the Danube River, Austria, which proved comparable to field‐based estimates (Fig. ; Cierjacks et al ., ).…”

Section: A Review Of Oc Stocks In Riparian Ecosystemsmentioning

confidence: 97%

“…To facilitate more rapid assessment of riverine biomass and OC stocks, different remote sensing techniques have been applied (Filippi et al, 2014). Suchenwirth et al (2012Suchenwirth et al ( , 2014 distinguished riparian vegetation types and used high spatial resolution remote sensing data to estimate carbon stocks along the Danube River, Austria, which proved comparable to fieldbased estimates (Fig. 4; Cierjacks et al, 2010).…”

Section: Riparian Vegetationmentioning

confidence: 99%

See 1 more Smart Citation

Banking carbon: a review of organic carbon storage and physical factors influencing retention in floodplains and riparian ecosystems

Sutfin

Wohl

Dwire

2015

Earth Surf Processes Landf

239

270

View full text Add to dashboard Cite

Rivers are dynamic components of the terrestrial carbon cycle and provide important functions in ecosystem processes. Although rivers act as conveyers of carbon to the oceans, rivers also retain carbon within riparian ecosystems along floodplains, with potential for long‐term (> 102 years) storage. Research in ecosystem processing emphasizes the importance of organic carbon (OC) in river systems, and estimates of OC fluxes in terrestrial freshwater systems indicate that a significant portion of terrestrial carbon is stored within river networks. Studies have examined soil OC on floodplains, but research that examines the potential mechanistic controls on OC storage in riparian ecosystems and floodplains is more limited. We emphasize three primary OC reservoirs within fluvial systems: (1) standing riparian biomass; (2) dead biomass as large wood (LW) in the stream and on the floodplain; (3) OC on and beneath the floodplain surface, including litter, humus, and soil organic carbon (SOC). This review focuses on studies that have framed research questions and results in the context of OC retention, accumulation and storage within the three primary pools along riparian ecosystems. In this paper, we (i) discuss the various reservoirs for OC storage in riparian ecosystems, (ii) discuss physical conditions that facilitate carbon retention and storage in riparian ecosystems, (iii) provide a synthesis of published OC storage in riparian ecosystems, (iv) present a conceptual model of the conditions that favor OC storage in riparian ecosystems, (v) briefly discuss human impacts on OC storage in riparian ecosystems, and (vi) highlight current knowledge gaps. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Section: A Review Of Oc Stocks In Riparian Ecosystemsmentioning

confidence: 97%

Section: Riparian Vegetationmentioning

confidence: 99%

Banking carbon: a review of organic carbon storage and physical factors influencing retention in floodplains and riparian ecosystems

Sutfin

Wohl

Dwire

2015

Earth Surf Processes Landf

239

270

View full text Add to dashboard Cite

show abstract

“…For SOC-S, the models obtained for SOC-S 10 (R 2 = 0.82, RMSE = 9.93 Mg ha −1 ; RF model) were better than those for SOC-S 40 (R 2 = 0.47, RMSE = 9.08 Mg ha −1 ; MSNPP model). Nonparametric techniques, such as kNN algorithms, have been used frequently to generate spatially-explicit forest biomass models using ALS and inventory data [48,88] but little research has been performed about the use of kNN regression models for C stock estimations in comparison to traditional linear regression methods [89]. However, kNN is a very simple classifier that works well in basic recognition problems and normality is not needed to obtain robust and understandable models [48].…”

Section: Low Density Als Data and The C Stock In Biomass And Socmentioning

confidence: 99%

Airborne Laser Scanning Cartography of On-Site Carbon Stocks as a Basis for the Silviculture of Pinus Halepensis Plantations

et al. 2018

View full text Add to dashboard Cite

Forest managers are interested in forest-monitoring strategies using low density Airborne Laser Scanning (ALS). However, little research has used ALS to estimate soil organic carbon (SOC) as a criterion for operational thinning. Our objective was to compare three different thinning intensities in terms of the on-site C stock after 13 years (2004–2017) and to develop models of biomass (Wt, Mg ha−1) and SOC (Mg ha−1) in Pinus halepensis forest, based on low density ALS in southern Spain. ALS was performed for the area and stand metrics were measured within 83 plots. Non-parametric kNN models were developed to estimate Wt and SOC. The overall C stock was significantly higher in plots subjected to heavy or moderate thinning (101.17 Mg ha−1 and 100.94 Mg ha−1, respectively) than in the control plots (91.83 Mg ha−1). The best Wt and SOC models provided R2 values of 0.82 (Wt, MSNPP) and 0.82 (SOC-S10, RAW). The study area will be able to stock 134,850 Mg of C under a non-intervention scenario and 157,958 Mg of C under the heavy thinning scenario. High-resolution cartography of the predicted C stock is useful for silvicultural planning and may be used for proper management to increase C sequestration in dry P. halepensis forests.

show abstract

“…Field-based estimations can be combined with GIS techniques and remote sensing approaches to obtain large-scale maps of vegetation, soil and total carbon stock distributions. Although in a very limited number, these spatial modelling approaches have been successfully applied in the USA [13,14] and Mexico [15], but also in Europe [12,16,17]. The most common methodologies rely on calibrating satellite measurements (spectral, textural and geometric variables) to in situ estimates of aboveground biomass (AGB) at field study plots.…”

Section: Introductionmentioning

confidence: 99%

“…The information is even scarcer for riparian systems located in the Mediterranean region, where estimates of AGB and carbon sequestration are extremely needed. Multispectral and hyperspectral data with high spatial resolution have been successfully used in floodplain areas to perform remote-sensing AGB-retrieval models, using in situ AGB as training and validation data [13][14][15][16]21]. However, in riparian habitats, and especially in Mediterranean regions, very high spatial resolution is mandatory to map vegetation due to the limited width and high structural complexity of riparian vegetation patches [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Carbon Stock Estimations in a Mediterranean Riparian Forest: A Case Study Combining Field Data and UAV Imagery

Fernandes

Aguiar

Martins

et al. 2020

Forests

View full text Add to dashboard Cite

This study aims to estimate the total biomass aboveground and soil carbon stocks in a Mediterranean riparian forest and identify the contribution of the different species and ecosystem compartments to the overall riparian carbon reservoir. We used a combined field and object-based image analysis (OBIA) approach, based on unmanned aerial vehicle (UAV) multispectral imagery, to assess C stock of three dominant riparian species. A linear discriminator was designed, based on a set of spectral variables previously selected in an optimal way, permitting the classification of the species corresponding to every object in the study area. This made it possible to estimate the area occupied by each species and its contribution to the tree aboveground biomass (AGB). Three uncertainty levels were considered, related to the trade-off between the number of unclassified and misclassified objects, leading to an error control associated with the estimated tree AGB. We found that riparian woodlands dominated by Acacia dealbata Link showed the highest average carbon stock per unit area (251 ± 90 tC ha−1) followed by Alnus glutinosa (L.) Gaertner (162 ± 12 tC ha−1) and by Salix salviifolia Brot. (73 ± 17 tC ha−1), which are mainly related to the stem density, vegetation development and successional stage of the different stands. The woody tree compartment showed the highest inputs (79%), followed by the understory vegetation (12%) and lastly by the soil mineral layer (9%). Spectral vegetation indices developed to suppress saturation effects were consistently selected as important variables for species classification. The total tree AGB in the study area varies from 734 to 1053 tC according to the distinct levels of uncertainty. This study provided the foundations for the assessment of the riparian carbon sequestration and the economic value of the carbon stocks provided by similar Mediterranean riparian forests, a highly relevant ecosystem service for the regulation of climate change effects.

show abstract

Large-Scale Mapping of Carbon Stocks in Riparian Forests with Self-Organizing Maps and the k-Nearest-Neighbor Algorithm

Cited by 16 publications

References 51 publications

Banking carbon: a review of organic carbon storage and physical factors influencing retention in floodplains and riparian ecosystems

Banking carbon: a review of organic carbon storage and physical factors influencing retention in floodplains and riparian ecosystems

Airborne Laser Scanning Cartography of On-Site Carbon Stocks as a Basis for the Silviculture of Pinus Halepensis Plantations

Carbon Stock Estimations in a Mediterranean Riparian Forest: A Case Study Combining Field Data and UAV Imagery

Contact Info

Product

Resources

About