Browsing by Subject "AIRBORNE LIDAR"

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  • Saarinen, Ninni; Vastaranta, Mikko; Nasi, Roope; Rosnell, Tomi; Hakala, Teemu; Honkavaara, Eija; Wulder, Michael A.; Luoma, Ville; Tommaselli, Antonio M. G.; Imai, Nilton N.; Ribeiro, Eduardo A. W.; Guimaraes, Raul B.; Holopainen, Markus; Hyyppa, Juha (2018)
    Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.
  • Tanhuanpää, Topi; Yu, Xiaowei; Luoma, Ville; Saarinen, Ninni; Raisio, Juha; Hyyppä, Juha; Kumpula, Timo; Holopainen, Markus (2019)
    Urban forests consist of patches of recreational areas, parks, and single trees on roadsides and other forested urban areas. Large number of tree species and heterogeneous growing conditions result in diverse canopy structure. High variation can be found both at level of single tree crowns and in canopy characteristics of larger areas. As urban forests are typically managed with small-scale, even tree-level operations, there is a need for detailed forest information. In this study, the effect of varying canopy conditions was tested on nine individual tree detection (ITD) methods. All methods utilized airborne laser scanning (ALS)-derived canopy height models (CHM) and different modifications of watershed segmentation (WS). The performance of mapping methods was compared in three strata with varying mean height and canopy cover. The results showed considerable variation between the methods when tested in varying canopy conditions. Especially, presence of large broadleaved trees affected the accuracy of detecting individual trees. The best performing methods for the three strata were G0.7, F2 and Gadapt. The areas with low canopy cover turned out problematic for all ITD methods tested as co-occurrence of small trees and large deciduous trees affected the accuracy significantly. Overall, The results show that stratification can be used to enhance the quality of ITD in urban park areas. However, heterogeneous canopy structure and varying growth patterns typical for urban parks lower the accuracy of tree detection. Also, according to our results we suggest that canopy height and canopy cover alone are insufficient attributes for stratifying urban canopy conditions.
  • Rosti, Hanna; Heiskanen, Janne; Loehr, John; Pihlström, Henry; Bearder, Simon; Mwangala, Lucas; Maghenda, Marianne; Pellikka, Petri; Rikkinen, Jouko (2022)
    We studied a previously almost unknown nocturnal mammal, an apparently undescribed species of tree hyrax (Dendrohyrax sp.) in the moist montane forests of Taita Hills, Kenya. We used thermal imaging to locate tree hyraxes, observe their behavior, and to identify woody plants most frequently visited by the selective browsers. We also documented acoustic behavior in forest fragments of different sizes. Data on calling type and frequency were analyzed together with lidar data to estimate population densities and to identify forest stand characteristics associated with large populations. Viable populations were found only in the largest forest fragments (> 90 ha), where tree hyraxes preferred most pristine forest stands with high, multilayered canopies. The estimated population sizes in smaller forest fragments were very limited, and hyraxes were heard to call only during late night and early morning hours, presumably in order to avoid detection. While we frequently recorded tree hyrax songs in the largest forest fragments, we almost never heard songs in the small ones. All remaining subpopulations of the Taita tree hyrax are under threat of human disturbance and further habitat deterioration. Conservation efforts should include protection of all remaining habitat patches, but also reforestation of former habitat is urgently needed.
  • Vesakoski, Jenni-Mari; Alho, Petteri; Hyyppa, Juha; Holopainen, Markus; Flener, Claude; Hyyppa, Hannu (2014)
  • 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.
  • Lin, Yi; Jiang, Miao; Pellikka, Petri; Heiskanen, Janne (2018)
    Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology-light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Halle architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.
  • Molinier, Matthieu; Lopez-Sanchez, Carlos A.; Toivanen, Timo; Korpela, Ilkka; Corral-Rivas, Jose J.; Tergujeff, Renne; Häme, Tuomas (2016)
    Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland ( boreal zone) were in good agreement with reference forest inventory plot data on pine ( R-2 = 0.75, RMSE = 5.33 m(2)/ha), spruce ( R-2 = 0.75, RMSE = 6.73 m(2)/ha) and birch ( R-2 = 0.71, RMSE = 4.98 m(2)/ha), with total relative RMSE ( %) = 29.66%. In Durango, Mexico ( temperate zone), Relasphone stem volume measurements were best for pine ( R-2 = 0.88, RMSE = 32.46 m(3)/ha) and total stem volume ( R-2 = 0.87, RMSE = 35.21 m(3)/ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.
  • Maeda, Eduardo; Nunes, Matheus; Calders, Kim; Mendes de Moura, Yhasmin; Raumonen, Pasi; Tuomisto, Hanna; Verley, Philippe; Vincent, Gregoire; Zuquin, Gabriela; Camargo, José Luis (2022)
    Forest edges are an increasingly common feature of Amazonian landscapes due to human-induced forest frag-mentation. Substantial evidence shows that edge effects cause profound changes in forest biodiversity and productivity. However, the broader impacts of edge effects on ecosystem functioning remain unclear. Assessing the three-dimensional arrangement of forest elements has the potential to unveil structural traits that are scalable and closely linked to important functional characteristics of the forest. Using over 600 high-resolution terrestrial laser scanning measurements, we present a detailed assessment of forest structural metrics linked to ecosystem processes such as energy harvesting and light use efficiency. Our results show a persistent change in forest structural characteristics along the edges of forest fragments, which resulted in a significantly lower structural diversity, in comparison with the interior of the forest fragments. These structural changes could be observed up to 35 m from the forest edges and are likely to reflect even deeper impacts on other ecosystem variables such as microclimate and biodiversity. Traits related to vertical plant material allocation were more affected than traits related to canopy height. We demonstrate a divergent response from the forest understory (higher vegetation density close to the edge) and the upper canopy (lower vegetation density close to the edge), indicating that assessing forest disturbances using vertically integrated metrics, such as total plant area index, can lead to an erroneous interpretation of no change. Our results demonstrate the strong potential of terrestrial laser scanning for benchmarking broader-scale (e.g. airborne and space-borne) remote sensing assessments of forest distur-bances, as well as to provide a more robust interpretation of biophysical changes detected at coarser resolutions.
  • Vesala, Risto; Rikkinen, Aleksi; Pellikka, Petri; Rikkinen, Jouko; Arppe, Laura (2022)
    Fungus-growing termites and their symbiotic Termitomyces fungi are critically important carbon and nutrient recyclers in arid and semiarid environments of sub-Saharan Africa. A major proportion of plant litter produced in these ecosystems is decomposed within nest chambers of termite mounds, where temperature and humidity are kept optimal for the fungal symbionts. While fungus-growing termites are generally believed to exploit a wide range of different plant substrates, the actual diets of most species remain elusive. We studied dietary niches of two Macrotermes species across the semiarid savanna landscape in the Tsavo Ecosystem, southern Kenya, based on carbon (C) and nitrogen (N) stable isotopes in Termitomyces fungus combs. We applied Bayesian mixing models to determine the proportion of grass and woody plant matter in the combs, these being the two major food sources available for Macrotermes species in the region. Our results showed that both termite species, and colonies cultivating different Termitomyces fungi, occupied broad and largely overlapping isotopic niches, indicating no dietary specialization. Including laser scanning derived vegetation cover estimates to the dietary mixing model revealed that the proportion of woody plant matter in fungus combs increased with increasing woody plant cover in the nest surroundings. Nitrogen content of fungus combs was positively correlated with woody plant cover around the mounds and negatively correlated with the proportion of grass matter in the comb. Considering the high N demand of large Macrotermes colonies, woody plant matter seems to thus represent a more profitable food source than grass. As grass is also utilized by grazing mammals, and the availability of grass matter typically fluctuates over the year, mixed woodland-grasslands and bushlands seem to represent more favorable habitats for large Macrotermes colonies than open grasslands.