Browsing by Subject "remote sensing"

Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.

Agroforestry is a collective name for agricultural land-use practices where combinations of woody perennials such as trees and shrubs are intentionally managed with crops and/or livestock in same land units for various environmental and economic benefits. As a sustainable farming practice, agroforestry is used to increase food production without adding harmful impacts of agriculture on natural environment. Agroforestry is a common farming practice in Taita Hills, Kenya, where it is motivated by Kenyan policies supporting tree planting in the fields. This study aims to find out how canopy height and canopy cover have changed during the last ten years in the croplands of Taita Hills to get more knowledge on the state and trends of agroforestry in the study area. Changes in canopy height and canopy cover in croplands are approached by multitemporal airborne laser scanning (ALS) data. ALS is an active remote sensing method used to acquire three-dimensional point cloud data of a target landscape. Canopy height models (CHM), 99th percentile canopy height and canopy cover data were derived from two ALS data sets from 2014/2015 and 2022 and used for the change detection of canopy height and canopy cover during the study period. Field data from 2013 and 2022 containing tree measurements from 28 field plots were used in the validation of ALS-based analyses. The results indicate that there has been a slight increase in canopy height and canopy cover during the study period. It is acknowledged that the study period is quite short to detect changes in tree growth. Hence, only slight positive changes in canopy height and canopy cover were expected. Based on CHM changes, almost 20% of the area outside forests had ≥ 2 m increase in the canopy height. Furthermore, 7% of the area outside forests had ≤ -5 m decrease in the canopy height, which corresponds to tree loss. Results for CHM based canopy height were supported by 99th percentile canopy height changes. The area outside forest with ≥ 10% canopy cover increased from 67.4% to 68.0%. Even though canopy height and canopy cover had a slight increase in the croplands, forest cover was detected to be increasing during the study period. ALS and field measurements matched well with each other. In the tree height measurements, there were more variance with taller trees, probably caused by difficulties in measuring taller trees in the field. Moreover, ALS data was found to underestimate tree height changes. The average absolute deviation for tree height changes was 1.3 m shorter for ALS-measured tree heights than field measurements. Number of trees in field plots has mainly decreased during 20132022. ALS-based mean canopy height and canopy cover changes in the plots explain the actual changes well if large number of trees have been cut down during the study period. The thesis provides valuable information on the state and trends of agroforestry in Taita Hills. However, more exact land cover classification could have enhanced the accuracy of the results even more. All in all, the results were mainly positive, indicating that there has been an increasing trend in canopy height and canopy cover in the croplands in Taita Hills.

Northern peatlands are a large source of methane (CH4) to the atmosphere and can vary strongly depending on local environmental conditions. However, few studies have mapped fine-grained CH4 fluxes at the landscape-level. The aim of this study was to predict land cover and CH4 flux patterns in Pallastunturi, Finland, in a study area dominated by forests, peatlands, fells, and lakes. I used random forest models to map land cover types and CH4 fluxes with multi-source remote sensing data and upscaled CH4 fluxes based on land cover maps. The random forest classifier reliably detected the same land cover patterns as the CORINE Land Cover maps. The main differences between the land cover maps were forest type classification, misclassification between neighboring peatland types, and detection of sparsely vegetated areas on fells. The upscaled CH4 fluxes of sinks were very robust to changes in land cover classification, but shrub tundra and peatland CH4 fluxes were sensitive to the level of detail in the land cover classification. The random forest regression performed well (NRMSE 6.6%, R2 82%) and predicted similar CH4 flux patterns as the upscaled CH4 flux maps, despite predicting larger areas that act as CH4 sources than the upscaled CH4 flux maps. The random forest regressor also better predicted CH4 fluxes in peatlands due to added information about soil moisture content from the remote sensing data. Random forests are a good model choice to detect landscape patterns and predict CH4 patterns in northern peatlands based on remote sensing and topographic data.

Arctic ecosystems face drastic changes in community structure due to warming, shrubification, permafrost loss, and other environmental changes. Due to the spatial heterogeneity of these ecosystems, understanding such changes on a local scale requires high-resolution data. Earth observation using satellite imagery and aerial photography has become a staple in mapping large areas and general patterns. Advances in sensor technology, the proliferation of unmanned aerial vehicles (UAVs), and increases in processing capacity enable the use of higher spatial and spectral resolutions. As a result, more detailed ecological observations can be made using remote sensing methods. In this thesis, I assess how increased spectral resolution affects the remote-sensing based modelling of plant communities in low-growth oroarctic tundra heaths. Based on a large field observation dataset, I estimate biomass, leaf area index, species richness, Shannon's biodiversity index, and fuzzy community clusters. I then build random forest models of these with image data of varying spectral, spatial, and temporal specifications and topographical data. Finally, I create maps of the vegetation. Leaf area index and biomass are best estimated of the response variables, with R2 values of 0.64 and 0.59, respectively, with multispectral data proving the most important explanatory dataset. Biodiversity metrics are best estimated with R2 values of 0.40–0.50 with the most important explanatory variables being topographical and hyperspectral, and community cluster with R2 values of 0.27–0.53, with the importance of various explanatory variables depending on the cluster being estimated. These results can help choose a suitable high-resolution remote sensing approach for modelling plant communities in similar conditions.

Structural complexity of trees is related to various ecological processes and ecosystem services. It can also improve the forests’ ability to adapt to environmental changes. In order to implement the management for complexity and to estimate its functionality, the level of structural complexity must be defined. The fractal-based box dimension (Db) provides an objective and holistic way to define the structural complexity for individual trees. The aim of this study was to compare structural complexity of Scots pine (Pinus sylvestris) trees measured by two remote sensing techniques, namely, terrestrial laser scanning (TLS) and aerial imagery acquired with unmanned aerial vehicle (UAV). Structural complexity for each Scots pine tree (n=2065) was defined by Db-values derived from the TLS and UAV measured point clouds. TLS produced the point clouds directly whereas UAV imagery was converted into point clouds with structure from motion (SfM) technology. The premise was that TLS provides the best available information on Db-values as the point density is higher in TLS than in UAV, and be-cause TLS is able to penetrate vegetation. TLS and UAV measured Db-values were found to significantly differ from each other and, thus, the techniques did not provide comparable information on structural complexity of individual Scots pine trees. On average, UAV measured Db-values were 5% bigger than TLS measured. The divergence between the TLS and UAV measured Db-values was found to be explained by the differences in the number and distribution of the points in the point clouds and by the differences in the estimated tree heights and number of boxes in the box dimension method. Forest structure (two thinning intensities, three thinning types and a control group) significantly affected the variation of both TLS and UAV measured Db-values. However, the divergence between TLS and UAV measured Db-values remained in all the treatments. In terms of the individual tree detection, the number of obtained points in the point cloud, and the distribution of these points, UAV measurements were better when the forest structure was sparser. In conclusion, while aerial imaging is a continuously developing study area and provides many advantageous attributes, at the moment, the TLS methods still dominate in accuracy when measuring the structural complexity at the tree-level. In the future, it should be studied whether TLS and UAV could be used as complementary techniques to provide more accurate and holistic view of the structural complexity in the perspective of both tree- and stand-level.

Aapa mires are northern peatlands of high conservation value that are currently threatened by hydrological changes caused by land use and climate change. In pristine hydrological state they are characterized by wet fens and waterflows from surrounding landscape, particularly during the snow melt time in spring. In future climate conditions, increased summer evapotranspiration and earlier spring floods can possibly reduce summertime water availability in aapa mires. It is however unclear, how strong effect the changes in the different climatic hydrological components exactly have on aapa mire wetness on regional level. Particularly spatial information of climatic sensitivity of mires is lacking. Current knowledge and large-scale hydrological predictions are based mainly on measurements at individual sites, modeling and generalization, even though climate-wetness relationships seem to vary largely from site to site. In this study, Sentinel-2 satellite imagery from 2017-2020 was used to produce regionally representative information of interannual summer wetness variability in Finnish aapa mires, and to quantify with statistical modeling the relationships to interannual climatic variation based on the observations. Monthly values for optical metrics of surface moisture and areal extent of the wettest mire surfaces were extracted for wet fen mires of the Natura 2000 conservation network (n=2201), covering the whole aapa mire zone of Finland. Wetness metrics from June and July in a regionally representative sample (n=400) of mires were linked to the respective yearly climatic data of summer-time water balance (WAB, precipitation - evaporation), day of snowmelt and snow water equivalent maximum. Climate-wetness relationships were estimated regionally for the eight aapa mire zone sub-regions with mixed effects models. The resulting satellite-derived metrics revealed high interannual variability of the surface moisture conditions and the areal extent of wet surfaces in aapa mires, and this variability was shown to be significantly connected to variation in the climatic variables. Regional variation in climatic sensitivity was remarkable. Mires in the most sensitive regions had twice as strong responses to varying WAB conditions and drought-periods than mires in the least sensitive regions. If the short-term responses are assumed to reflect the sensitivity to climatic changes in the long-term, these effects imply that aapa mires might experience remarkable hydrological changes in the future, especially in the most sensitive areas in the southern and the western parts of the Finnish aapa mire zone. The results emphasize the important role of yearly snow conditions alongside summertime WAB as a driver of aapa mire wetness, especially in June but also later in the summer.

As a result of urbanization and climate change, cities are facing various ecological and social challenges. For instance, flooding, pollution, urban heat island, decreased biodiversity, and mental stress of city dwellers are well recognized challenges of urban spaces. Urban green spaces are increasingly important in mitigating the adverse effects of climate change, such as flooding due to precipitation extremes, and also providing various other ecosystem services. In order to ensure sustainable land use and provision of ecosystem services, it is essential to develop methods for effective urban green space mapping. As a result, there is a growing demand for micro-scale land cover maps for urban areas. Emerging technologies, such as Object Based Image Analysis, OBIA, and light detection and ranging, LiDAR, offer promising possibilities for efficient mapping of green spaces in the urban environment. The aim of this thesis was to develop a semi-automatic method for urban green space mapping and classification. The other major task was to study the added benefits of light detection and ranging technology. Three research sites of varying degree of urbanization from the city of Helsinki were chosen for the study; from the city core in Itä-Pasila to appartment area with blocks of flats in Pihlajamäki and small-house residential area in Veräjämäki. The classification process was executed with an image analysis program called Definiens Developer. Main input data for classification was LiDAR data and VHR (very high resolution) aerial images. In the classification process, normalized vegetation index (NDVI) was used to detect live vegetation; assignation to different classes was based on height information derived from LIDAR data. Finally, an accuracy assessment was performed on the classified images to determine how well the classification process accomplished the task. The accuracy was assessed by comparing the classification images to the reference images of each catchment. Results demonstrate well the potential of OBIA for extracting urban green spaces. The downtown area of high land use intensity (Itä-Pasila) had the smallest green space coverage (31%), consisting mostly of urban parks and planted trees along the streets. The small-house area of low land use intensity (Veräjämäki) had the highest proportion (65%) of green spaces, consisting of forests and gardens. In the intermediate land use intensity with block of flats (Pihlajamäki)ts, a little under half of the coverage is green spaces. The highest accuracy of detecting green spaces was reached in low land use intensity area (92%), followed by the high and intermediate land use areas with 82% and 78%, respectively. The most common problem for classification was shaded areas, which reflect only limited spectral information and therefore the calculating of NDVI index becomes impossible. I found the object-based image analysis together with LiDAR data fusion to provide good means for urban green space mapping and classification. The presented method allowed a quick data acquisition with good overall accuracy, while avoiding the problems previously related to more traditional pixel-based methods. The addition of LiDAR data created the possibility of extracting vegetation height and using it in the classification process in order to divide vegetation into four different classes.

As an internationally important topic for forestry, climate change has long been a topic of concern, as well as the ability of the forests to accumulate carbon. In addition, in Finland, these values have essentially been associated with the economic, cultural and social value of forests. In view of these values, it is important to be able to maintain forest resources at a sustainable level for all the different sectors. As far as sustainability is concerned, knowing the current state of forests is significant. This information is collected through the inventory of forests, and today it is mainly based on different remote sensing methods. In order to support reliable decisionmaking, forest information needs to be up-to-date and accurate. The aim of the thesis was to examine the accuracy of different tree attribute estimates and compare them between themselves and to investigate the accuracy of growth models in producing the estimates. In addition, the aim was to evaluate the effects of the accuracy of the remote sensing estimates on the determination of the timing harvests. The research area was located in boreal coniferous forest zone in Southern Finland, Evo (61.19˚N, 25.11˚E). The area comprised a 5 km x 5 km area, comprising about 2000 hectares of forest treated in different ways. Field measurements, aerial imagery, and airborne laser scanning data were generated using estimates for forest inventory attributes based on three different statistical features derived from the remote sensing data in the preparation of estimates. The forest inventory attributes were volume V, basal area-weighted mean diameter Dg, basal area-weighted mean height, number of the stems per hectare, and basal area G. In the prediction of the forest inventory attributes a non-parametric k-NN method was used, and random forest -algorithm was used in the selection of the nearest neighbors. Growth modeling was carried out using SIMO software. It can be seen from the results that, as a rule, more accurate results are obtained by producing airborne lasers canning estimates than by aerial imagery estimates. In addition, prediction precisions were better in coniferous trees than in deciduous trees. In forest inventory attribute estimates, especially the basal area G and volume V are generally underestimated, which is likely to delay the scheduled timing of harvests. Updating remote sensing estimates with growth models would appear to yield more biased estimates compared to the new remote sensing inventory.

Remote sensing brings new potential to complement environmental sampling and measuring traditionally conducted in the field. Satellite images can bring spatial coverages and accurately repeated time-series data collection to a whole new level. While developing methos for doing ecological assessment from space in situ sampling is still in key role. Satellite images of relatively coarser pixel size where individual plants or trees are not possible to separate usually utilize vegetation indices as proxies for environmental qualities and measures. One of the most extensively used and studied vegetation index is Natural Difference Vegetation Index (NDVI). It is calculated as normalized ratio between red light and near-infra-red radiation with formula: NDVI=NIR- RED/NIR+RED. Index functions as a measure for plant productivity, that has also been linked to species-level diversity. In this thesis MODIS NDVI (MOD13Q1, 250 m x 250 m resolution) and selected additional variables were examined through their predictive power for explaining variation in tree species richness in six different types of moist tropical evergreen forests in the province of West Kalimantan, on the island Borneo in Indonesia. Simple and multiple regression models were built and tested with main focus on 20- year mean-NDVI. Additional variables used were aboveground carbon, elevation stem count, tree height and DBH. Additional variables were examined initially on individual basis and subsequently potential variables were then combined with NDVI. Results indicate statistically significant, but not very strong predictable power for NDVI (R2=0.25, p-value=2.11e-07). Elevation and number of stems outperformed NDVI in regression analyses (R2=0.64, p-value=2.2e-16 and R2=0.36, p-value=4.5e-11, respectively). Aboveground biomass carbon explained 19% of the variation in tree species richness (p-value=6.136e-06) and thus was the worst predictor selected for multiple regression models. Tree height (R2=0.062, p-value=0.0137) and DBH (R2=0.003, p-value=0.6101) did not show any potential in predicting tree species richness. Best variable combination was NDVI, elevation and stem count (R2=0.71, p-value=2.2e-16). Second best was NDVI, elevation and aboveground biomass carbon (R2=0.642, p-value=2.2e-16), which did not promote for biomass carbon as a potential predictor as model including only NDVI and elevation resulted nearly identically (R2=0.639, p-value=2.2e-16). Model including NDVI and stem count explained 54% of the variation in tree species richness (p-value=2.2e-16) suggesting elevation and stem count being potential variables combined with NDVI for this type of analysis. Problems with MODIS NDVI are mostly linked to the relatively coarse spectral scale which seems to be too coarse for predicting tree species richness. Spectral scale also caused spatial mismatch with field plots as being significantly of different sizes. Applicability in other areas is also limited due to the narrow ecosystem spectrum covered as only tropical evergreen forests were included in this study. For future research higher resolution satellite data is a relevant update. In terms methodology, alternative approach known as Spectral Variability Hypothesis (SVH), which takes into account heterogeneity in spectral reflectance, seems more appropriate method for relating spectral signals to tree species richness.

Tropical montane forests are important environmental factors globally as they preserve biodiversity, carbon, and moisture. That is why it is necessary to have knowledge on the distribution and condition of these forests. Acknowledging what kind of changes happen and in what timescale, assist in forest management and planning. Remote sensing based change detection is one of the ways of investigating these changes. In this study change detection is conducted with multitemporal airborne laser scanning (ALS) data from the years 2014/2015 and 2022. ALS produces three-dimensional point data which can be further processed into different elevation models and canopy height model (CHM). This study focuses on observing changes in tropical montane forests in the Taita Hills, in Kenya. The study area included two peaks, Yale and Ngangao, which are part of the Eastern Arc Mountains, which is a bigger mountain chain stretching from Tanzania to Kenya. Both forest areas include native montane and exotic vegetation. Both positive and negative canopy height changes (i.e., tree height growth and tree loss) between these different forest segments were studied. The forest segmentation used in this study is based on earlier mapping in the area, but it was updated with the help of field observations, orthophotos acquired in January 2022 and CHM. In addition, the effects of point density on canopy height metrics were studied. The results indicate that overall trend in the forests has been positive height change. The canopy height changes show that tree growth in the forests differ between the forest segments. Areas with exotic tree species grow faster than the areas with native montane vegetation. When comparing the different tree species, eucalyptus seems to grow fastest, followed by pine and then cypress. Furthermore, some spatial differences were also noted, as similar forest types had grown more in the southern parts of Ngangao forest. Also, negative changes were observed in the data and treefall gaps were identified from the CHM. Results indicate that there is no distinctive pattern between the different forest segments and tree species in treefall. Falling of a tree can be a result of many things. It can be done purposely, or it may happen due to natural causes. Inspections of the effects of point density proved that the attributes do affect canopy height metrics derived from the data. Higher point density differences between the data resulted in larger difference values in the canopy height difference models. In accordance with other studies, it could then be concluded that high point densities create overestimations and lower point densities create underestimations of vegetation heights. Point density differences are one of the issues that should be considered when working with multitemporal data. In addition, variations in the data acquisition create uncertainties to accurate comparison between the data. This thesis provides valuable information about the changes happening in tropical montane forests in the Taita Hills. As the results demonstrate that different forest types have different growth speeds, the information can be further applied in practises that recognise forest type segments. This is crucial in determining montane forest segments. These results are suitable for further analysis and research. When considering the environmental effects tropical montane forests have and how their changes effect the local and global climate, it is useful to know how different species grow and survive in different environments. As the results show, eucalyptus seems to thrive in the area but the effects of exotic species to the biodiversity should be noted as well. In the case of eucalyptus, it uses a lot of water resources to grow, and the undergrowth might not be as rich as in native montane tree species areas.