Browsing by Subject "ABOVEGROUND BIOMASS"

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  • Pyorala, Jiri; Liang, Xinlian; Saarinen, Ninni; Kankare, Ville; Wang, Yunsheng; Holopainen, Markus; Hyyppa, Juha; Vastaranta, Mikko (2018)
    Terrestrial laser scanning (TLS) accompanied by quantitative tree-modeling algorithms can potentially acquire branching data non-destructively from a forest environment and aid the development and calibration of allometric crown biomass and wood quality equations for species and geographical regions with inadequate models. However, TLS's coverage in capturing individual branches still lacks evaluation. We acquired TLS data from 158 Scots pine (Pinus sylvestris L.) trees and investigated the performance of a quantitative branch detection and modeling approach for extracting key branching parameters, namely the number of branches, branch diameter (b(d)) and branch insertion angle (b) in various crown sections. We used manual point cloud measurements as references. The accuracy of quantitative branch detections decreased significantly above the live crown base height, principally due to the increasing scanner distance as opposed to occlusion effects caused by the foliage. b(d) was generally underestimated, when comparing to the manual reference, while b was estimated accurately: tree-specific biases were 0.89cm and 1.98 degrees, respectively. Our results indicate that full branching structure remains challenging to capture by TLS alone. Nevertheless, the retrievable branching parameters are potential inputs into allometric biomass and wood quality equations.
  • de Moura, Yhasmin Mendes; Balzter, Heiko; Galvão, Lênio S.; Dalagnol, Ricardo; Espírito-Santo, Fernando; Santos, Erone G.; Garcia, Mariano; Bispo, Polyanna Da Conceição; Oliveira, Raimundo C.; Shimabukuro, Yosio E. (2020)
    Tropical forests hold significant amounts of carbon and play a critical role on Earth ' s climate system. To date, carbon dynamics over tropical forests have been poorly assessed, especially over vast areas of the tropics that have been affected by some type of disturbance (e.g., selective logging, understory fires, and fragmentation). Understanding the multi-temporal dynamics of carbon stocks over human-modified tropical forests (HMTF) is crucial to close the carbon cycle balance in the tropics. Here, we used multi-temporal and high-spatial resolution airborne LiDAR data to quantify rates of carbon dynamics over a large patch of HMTF in eastern Amazon, Brazil. We described a robust approach to monitor changes in aboveground forest carbon stocks between 2012 and 2018. Our results showed that this particular HMTF lost 0.57 myr(-1) in mean forest canopy height and 1.38 MgCha(-1)yr(-1) of forest carbon between 2012 and 2018. LiDAR-based estimates of Aboveground Carbon Density (ACD) showed progressive loss through the years, from 77.9 MgCha(-1) in 2012 to 53.1 MgCha(-1) in 2018, thus a decrease of 31.8%. Rates of carbon stock changes were negative for all time intervals analyzed, yielding average annual carbon loss rates of -1.34 MgCha(-1)yr(-1). This suggests that this HMTF is acting more as a source of carbon than a sink, having great negative implications for carbon emission scenarios in tropical forests. Although more studies of forest dynamics in HMTFs are necessary to reduce the current remaining uncertainties in the carbon cycle, our results highlight the persistent effects of carbon losses for the study area. HMTFs are likely to expand across the Amazon in the near future. The resultant carbon source conditions, directly associated with disturbances, may be essential when considering climate projections and carbon accounting methods.
  • Kangas, Annika; Raty, Minna; Korhonen, Kari T.; Vauhkonen, Jari; Packalen, Tuula (2019)
    Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced by the multitude of scales the forest data are needed, and the challenges in acknowledging the errors due to the measurements and models in addition to sampling errors. Between NFIs, the challenges related to the different harmonization tasks were reviewed. While a design-based approach is often considered more attractive than a model-based approach as it is guaranteed to provide unbiased results, the model-based approach is needed for downscaling the information to smaller scales and acknowledging the measurement and model errors. However, while a model-based inference is possible in small areas, the unknown random effects introduce biased estimators. The NFIs need to cater for the national information requirements and maintain the existing time series, while at the same time providing comparable information across the countries. In upscaling the NFI information to continental and global information needs, representative samples across the area are of utmost importance. Without representative data, the model-based approaches enable provision of forest information with unknown and indeterminable biases. Both design-based and model-based approaches need to be applied to cater to all information needs. This must be accomplished in a comprehensive way In particular, a need to have standardized quality requirements has been identified, acknowledging the possibility for bias and its implications, for all data used in policy making.
  • Luoma, Ville; Vastaranta, Mikko; Eyvindson, Kyle; Kankare, Ville; Saarinen, Ninni; Holopainen, Markus; Hyyppa, Juha (Springer International Publishing AG, 2017)
    Lecture Notes in Geoinformation and Cartography
    Currently the forest sector in Finland is looking towards the next generation's forest resource information systems. Information used in forest planning is currently collected by using an area-based approach (ABA) where airborne laser scanning (ALS) data are used to generalize field-measured inventory attributes over an entire inventory area. Inventories are typically updated at 10-year interval. Thus, one of the key challenges is the age of the inventory information and the cost-benefit trade-off between using the old data and obtaining new data. Prediction of future forest resource information is possible through growth modelling. In this paper, the error sources related to ALS-based forest inventory and the growth models applied in forest planning to update the forest resource information were examined. The error sources included (i) forest inventory, (ii) generation of theoretical stem distribution, and (iii) growth modelling. Error sources (ii) and (iii) stem from the calculations used for forest planning, and were combined in the investigations. Our research area, Evo, is located in southern Finland. In all, 34 forest sample plots (300 m(2)) have been measured twice tree-by-tree. First measurements have been carried out in 2007 and the second measurements in 2014 which leads to 7 year updating period. Respectively, ALS-based forest inventory data were available for 2007. The results showed that prediction of theoretical stem distribution and forest growth modelling affected only slightly to the quality of the predicted stem volume in short-term information update when compared to forest inventory error.
  • Jenal, Alexander; Hueging, Hubert; Ahrends, Hella Ellen; Bolten, Andreas; Bongartz, Jens; Bareth, Georg (2021)
    UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R-2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R-2: 0.75 to 0.84). These results indicate the imaging system's potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.
  • Räsänen, Aleksi; Juutinen, Sari; Kalacska, Margaret; Aurela, Mika; Heikkinen, Pauli; Mäenpää, Kari; Rimali, Aleksi; Virtanen, Tarmo (2020)
    There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.
  • Junior, Celso; Aragão, Luiz; Anderson, Liana; Fonseca, Marisa; Shimabukuro, Yosio E.; Vancutsem, Christelle; Achard, Fredéric; Beuchle, René; Numata, Izaya; Silva, Carlos; Maeda, Eduardo; Longo, Marcos; Saatchi, Sassan S. (2020)
    Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year(-1) in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 +/- 8 Tg C year(-1). Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement's bold targets.
  • Larjavaara, Markku; Berninger, Frank; Palviainen, Marjo; Prokushkin, Anatoly; Wallenius, Tuomo (2017)
    Improved understanding of carbon (C) accumulation after a boreal fire enables more accurate quantification of the C implications caused by potential fire regime shifts. We coupled results from a fire history study with biomass and soil sampling in a remote and little-studied region that represents a vast area of boreal taiga. We used an inventory approach based on predefined plot locations, thus avoiding problems potentially causing bias related to the standard chronosequence approach. The disadvantage of our inventory approach is that more plots are needed to expose trends. Because of this we could not expose clear trends, despite laborious sampling. We found some support for increasing C and nitrogen (N) stored in living trees and dead wood with increasing time since the previous fire or time since the previous stand-replacing fire. Surprisingly, we did not gain support for the well-established paradigm on successional patterns, beginning with angiosperms and leading, if fires are absent, to dominance of Picea. Despite the lack of clear trends in our data, we encourage fire historians and ecosystem scientists to join forces and use even larger data sets to study C accumulation since fire in the complex Eurasian boreal landscapes.
  • Vastaranta, Mikko; Niemi, Mikko; Karjalainen, Mika; Peuhkurinen, Jussi; Kankare, Ville; Hyyppa, Juha; Holopainen, Markus (2014)
  • Amara, Edward; Heiskanen, Janne; Aynekulu, Ermias; Pellikka, Petri Kauko Emil (2019)
    Global sustainable development goals include reducing greenhouse gas emissions from land-use change and maintaining biodiversity. Many studies have examined carbon stocks and tree species diversity, but few have studied the humid Guinean savanna ecosystem. This study focuses on a humid savanna landscape in northern Sierra Leone, aiming to assess carbon stocks and tree species diversity and compare their relationships in different vegetation types. We surveyed 160 sample plots (0.1 ha) in the field for tree species, aboveground carbon (AGC) and soil organic carbon (SOC). In total, 90 tree species were identified in the field. Gmelina arborea, an exotic tree species common in the foothills of the Kuru Hills Forest Reserve, and Combretum glutinosum, Pterocarpus erinaceous and Terminaria glaucescens, which are typical savanna trees, were the most common species. At landscape level, the mean AGC stock was 29.4 Mg C ha(-1) (SD 21.3) and mean topsoil (0-20 cm depth) SOC stock was 42.2 Mg C ha(-1) (SD 20.6). Mean tree species richness and Shannon index per plot were 7 (SD 4) and 1.6 (SD 0.6), respectively. Forests and woodlands had significantly higher mean AGC and tree species richness than bushland, wooded grassland or cropland (p <0.05). In the forest and bushland, a small number of large diameter trees covered a large portion of the total AGC stocks. Furthermore, a moderate linear correlation was observed between AGC and tree species richness (r = 0.475, p <0.001) and AGC and Shannon index (r = 0.375, p <0.05). The correlation between AGC and SOC was weak (r = 0.17, p <0.05). The results emphasise the role of forests and woodlands and large diameter trees in retaining AGC stocks and tree species diversity in the savanna ecosystem.
  • Calders, Kim; Adams, Jennifer; Armston, John; Bartholomeus, Harm; Bauwens, Sebastien; Bentley, Lisa Patrick; Chave, Jerome; Danson, F. Mark; Demol, Miro; Disney, Mathias; Gaulton, Rachel; Moorthy, Sruthi M. Krishna; Levick, Shaun R.; Saarinen, Ninni; Schaaf, Crystal; Stovall, Atticus; Terryn, Louise; Wilkes, Phil; Verbeeck, Hans (2020)
    Terrestrial laser scanning (TLS) was introduced for basic forest measurements, such as tree height and diameter, in the early 2000s. Recent advances in sensor and algorithm development have allowed us to assess in situ 3D forest structure explicitly and revolutionised the way we monitor and quantify ecosystem structure and function. Here, we provide an interdisciplinary focus to explore current developments in TLS to measure and monitor forest structure. We argue that TLS data will play a critical role in understanding fundamental ecological questions about tree size and shape, allometric scaling, metabolic function and plasticity of form. Furthermore, these new developments enable new applications such as radiative transfer modelling with realistic virtual forests, monitoring of urban forests and larger scale ecosystem monitoring through long-range scanning. Finally, we discuss upscaling of TLS data through data fusion with unmanned aerial vehicles, airborne and spaceborne data, as well as the essential role of TLS in validation of spaceborne missions that monitor ecosystem structure.