Browsing by Subject "Forest"

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  • Firozjaei, Mohammad Karimi; Sedighi, Amir; Firozjaei, Hamzeh Karimi; Kiavarz, Majid; Homaee, Mehdi; Arsanjani, Jamal Jokar; Makki, Mohsen; Naimi, Babak; Alavipanah, Seyed Kazem (2021)
    Mining activities and associated actions cause land-use/land-cover (LULC) changes across the world. The objective of this study were to evaluate the historical impacts of mining activities on surface biophysical characteristics, and for the first time, to predict the future changes in pattern of vegetation cover and land surface temperature (LST). In terms of the utilized data, satellite images of Landsat, and meteorological data of Sungun mine in Iran, Athabasca oil sands in Canada, Singrauli coalfield in India and Hambach mine in Germany, were used over the period of 1989-2019. In the first step, the spectral bands of Landsat images were employed to extract historical LULC changes in the study areas based on the homogeneity distance classification algorithm (HDCA). Thereafter, a CA-Markov model was used to predict the future of LULC changes based on the historical changes. In addition, LST and vegetation cover maps were calculated using the single channel algorithm, and the normalized difference vegetation index (NDVI), respectively. In the second step, the trends of LST and NDVI variations in different LULC change types and over different time periods were investigated. Finally, a CA-Markov model was used to predict the LST and NDVI maps and the trend of their variations in future. The results indicated that the forest and green space cover was reduced from 9.95 in 1989 to 5.9 Km(2) in 2019 for Sungun mine, from 42.14 in 1999 to 33.09 Km(2) in 2019 for Athabasca oil sands, from 231.46 in 1996 to 263.95 Km(2) in 2016 for Singrauli coalfield, and from 180.38 in 1989 to 133.99 Km(2) in 2017 for Hambach mine, as a result of expansion and development of of mineral activities. Our findings about Sungun revealed that the areal coverage of forest and green space will decrease to 15% of the total study area by 2039, resulting in reduction of the mean NDVI by almost 0.06 and increase of mean standardized LST from 0.52 in 2019 to 0.61 in 2039. our results further indicate that for Athabasca oil sands (Singrauli coalfield, Hambach mine), the mean values of standardized LST and NDVI will change from 0.5 (0.44 and 0.4) and 0.38 (0.38, 0.35) in 2019 (2016, 2017) to 0.57 (0.5, 0.47) and 0.33 (0.32, 0.28), in 2039 (2036, 2035), respectively. This can be mainly attributed to the increasing mining activities in the past as well as future years. The discussion and conclusions presented in this study can be of interest to local planners, policy makers, and environmentalists in order to observe the damages brought to the environment and the society in a larger picture.
  • Tanhuanpaa, Topi; Saarinen, Ninni; Kankare, Ville; Nurminen, Kimmo; Vastaranta, Mikko; Honkavaara, Eija; Karjalainen, Mika; Yu, Xiaowei; Holopainen, Markus; Hyyppa, Juha (Springer International Publishing AG, 2017)
    Lecture Notes in Geoinformation and Cartography
    During the past decade, airborne laser scanning (ALS) has established its status as the state-of-the-art method for detailed forest mapping and monitoring. Current operational forest inventory widely utilizes ALS-based methods. Recent advances in sensor technology and image processing have enabled the extraction of dense point clouds from digital stereo imagery (DSI). Compared with ALS data, the DSI-based data are cheap and the point cloud densities can easily reach that of ALS. In terms of point density, even the high-altitude DSI-based point clouds can be sufficient for detecting individual tree crowns. However, there are significant differences in the characteristics of ALS and DSI point clouds that likely affect the accuracy of tree detection. In this study, the performance of high-altitude DSI point clouds was compared with low-density ALS in detecting individual trees. The trees were extracted from DSI-and ALS-based canopy height models (CHM) using watershed segmentation. The use of both smoothed and unsmoothed CHMs was tested. The results show that, even though the spatial resolution of the DSI-based CHM was better, in terms of detecting the trees and the accuracy of height estimates, the low-density ALS performed better. However, utilizing DSI with shorter ground sample distance (GSD) and more suitable image matching algorithms would likely enhance the accuracy of DSI-based approach.
  • White, Joanne C.; Saarinen, Ninni; Wulder, Michael A.; Kankare, Ville; Hermosilla, Txomin; Coops, Nicholas C.; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2019)
    Information regarding the nature and rate of forest recovery is required to inform forest management, monitoring, and reporting activities. Delayed establishment or return of forests has implications to harvest rotations and carbon uptake, among others, creating a need for spatially-explicit, large-area, characterizations of forest recovery. Landsat time series (LTS) has been demonstrated as a means to quantitatively relate forest recovery, noting that there are gaps in our understanding of the linkage between spectral measures of forest recovery and manifestations of forest structure and composition. Field plots provide a means to better understand the linkage between forest characteristics and spectral recovery indices. As such, from a large set of existing field plots, we considered the conditions present for the year in which the co-located pixel was considered spectrally recovered using the Years to Recovery (Y2R) metric. Y2R is a long-term metric of spectral recovery that indicates the number of years required for a pixel to return to 80% of its pre-disturbance Normalized Burn Ratio value. Absolute and relative metrics of recovery at 5 years post-disturbance were also considered. We used these three spectral recovery metrics to predict the stand development class assigned by the field crew for 284 seedling plots with an overall accuracy of 73.59%, with advanced seedling stands more accurately discriminated (omission error, OE = 15.74%) than young seedling stands (OE = 49.84%). We then used field-measured attributes (e.g. height, stem density, dominant species) from the seedling plots to classify the plots into three spectral recovery groups, which were defined using the Y2R metric: spectral recovery in (1) 1–5 years, (2) 6–10 years, or (3) 11–15 years. Overall accuracy for spectral recovery groups was 61.06%. Recovery groups 1 and 3 were discriminated with greater accuracy (producer’s and user’s accuracies > 66%) than recovery group 2 (<50%). The top field-measured predictors of spectral recovery were mean height, dominant species, and percentage of stems in the plot that were deciduous. Variability in stand establishment and condition make it challenging to accurately discriminate among recovery rates within 10 years post-harvest. Our results indicate that the long-term metric Y2R relates to forest structure and composition attributes measured in the field and that spectral development post-disturbance corresponds with expectations of structural development, particularly height, for different species, site types, and deciduous abundance. These results confirm the utility of spectral recovery measures derived from LTS data to augment landscape-level assessments of post-disturbance recovery.
  • Larjavaara, Markku; Kanninen, Markku Tapani; Alam, Syed Ashraful; Mäkinen, Antti; Poeplau, Christopher (2017)
    Land use directly impacts ecosystem carbon and indirectly influences atmospheric carbon. Computing ecosystem carbon for an area experiencing changes in land use is not trivial, as carbon densities change slowly after land-use changes. We developed a tool, CarboScen, to estimate ecosystem carbon in landscapes. It is a simple tool typically used with an annual time step, and is based on carbon pools and densities. It assumes that carbon density asymptotically approaches a value, which is set for the land-use type in question. We recommend CarboScen for landscapes with spatially relatively homogenous soils and climate, multiple land uses, and changes between these leading to slow changes in carbon densities because either soil organic carbon is included in the analysis or afforestation occurs. Thanks to its simplicity, it is particularly suitable for participatory planning, rapid assessment of REDD+ project potential, and educational use.
  • Maharani, Cynthia; Moeliono, Moira; Wong, Grace Yee; Brockhaus, Maria; Carmenta, Rachel; Kallio, Maarit Helena (2019)
    Market-driven development is transforming swidden landscapes and having different impacts along intersections of gender, age and class. In Kapuas Hulu, West Kalimantan, Indonesia, Dayak communities practicing swidden agriculture are making choices on maintaining traditional land use systems, and engaging in rubber, oil palm and conservation (REDD + ) in their livelihood strategies. Although REDD + has been heralded as an alternative to oil palm as a sustainable development option, it is still far from full implementation. Meanwhile, oil palm has become a reality, with large scale plantations that offer job opportunities and produce new sources of prestige, but create contestations around traditional land use systems. We employ the gender asset agriculture project (GAAP) framework and apply an intersectional lens to highlight power relations underlying gendered differences in land, labor and social capital in this process of transformation. Our findings suggest that market interventions produce major changes for men and women, young and old, land cultivators and wage earners. This has created new opportunities for some and new risks for others, with those having power to access diverse types of knowledge, ranging from inheritance rights to market information and job opportunities, best able to exploit such opportunities.
  • Toivonen, Marjaana; Karimaa, Anna Elina; Herzon, Irina; Kuussaari, Mikko (2022)
    Non-bee insects have been identified as important crop pollinators globally. However, strategies to protect pollinators and enhance crop pollination usually focus on supporting bees. This study examined the effects of landscape structure, location within field, and floral resources on pollinators’ visits on mass-flowering caraway (Carum carvi L.) in boreal farmland, and the effects of the visits on caraway yield. Pollinator visits on caraway flowers were monitored and caraway yield measured in 30 fields at landscapes ranging from field-dominated to forest-dominated landscapes. Hoverflies were the most abundant flower-visitors of caraway, followed by honeybees. Hoverflies and other flies made more flower visits on caraway than all bee species combined. Pollinator groups differed in their responses to landscape and local factors. Flies were most abundant near field edges and in landscapes with high forest cover. Non-syrphid flies and solitary bees responded positively to the cover of flowering herbs in the adjacent field margins. Flower visits by honeybees, instead, were positively related to the flowering crop cover in the study fields. Caraway seed yield increased with increasing number of flower visits by honeybees, hoverflies and all pollinators together. Pollinator exclusion reduced caraway fruit set (i.e. the number of fruits per flower) by 13% and seed yield by 40%. Our study is the first to report the high importance of flies to crop pollination in boreal farmland, where caraway is an important export crop. The results highlight the need of taking flies and their habitat requirements into account when developing strategies to enhance crop pollination.
  • Di Gregorio, Monica; Gallemore, Caleb Tyrell; Brockhaus, Maria; Fatorelli, Leandra; Efrian, Muharrom (2017)
    This paper investigates the adoption of discourses on Reducing Emissions from Deforestation and forest Degradation (REDD +) across different national contexts. It draws on institutional theories to develop and test a number of hypotheses on the role of shared beliefs and politico-economic institutions in determining the discursive choices of policy actors. The results show that win win ecological modernization discourse, embraced by powerful government agencies and international actors, dominates national REDD + policy arenas. This discourse is challenged primarily by a minority reformist civic environmentalist discourse put forward primarily by domestic NGOs. We find evidence that countries with a less democratic political system and large-scale primary sector investments facilitate the adoption of reconciliatory ecological modernization discourse, which may not directly challenge the drivers of deforestation. Policy actors who believe in and are engaged in market-based approaches to REDD + are much more likely to adopt ecological modernization discourses, compared to policy actors who work on community development and livelihoods issues.
  • Liang, Xinlian; Hyyppä, Juha; Kaartinen, Harri; Lehtomäki, Matti; Pyörälä, Jiri; Pfeifer, Norbert; Holopainen, Markus; Brolly, Gábor; Francesco, Pirotti; Hackenberg, Jan; Huang, Huabing; Jo, Hyun-Woo; Katoh, Masato; Liu, Luxia; Mokroš, Martin; Morel, Jules; Olofsson, Kenneth; Poveda-Lopez, Jose; Trochta, Jan; Wang, Di; Wang, Jinhu; Xi, Zhouxi; Yang, Bisheng; Zheng, Guang; Kankare, Ville; Luoma, Ville; Yu, Xiaowei; Chen, Liang; Vastaranta, Mikko; Saarinen, Ninni; Wang, Yunsheng (2018)
    The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources.
  • Wang, Yunsheng; Kukko, Antero; Hyyppä, Eric; Hakala, Teemu; Pyörälä, Jiri; Lehtomäki, Matti; El Issaoui, Aimad; Yu, Xiaowei; Kaartinen, Harri; Liang, Xinlian; Hyyppä, Juha (2021)
    BackgroundCurrent automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost.ResultsIn the experiment, an approximately 0.5ha forest was covered in ca. 10min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2-4cm RMSE of the diameter at the breast height estimates, and a 4-7cm RMSE of the stem curve estimates.ConclusionsResults of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.
  • Wang, Yunsheng; Kukko, Antero; Hyyppä, Eric; Hakala, Teemu; Pyörälä, Jiri; Lehtomäki, Matti; El Issaoui, Aimad; Yu, Xiaowei; Kaartinen, Harri; Liang, Xinlian; Hyyppä, Juha (Springer Singapore, 2021)
    Abstract Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.
  • Holmberg, Maria; Akujärvi, Anu; Anttila, Saku; Autio, Iida; Haakana, Markus; Junttila, Virpi; Karvosenoja, Niko; Kortelainen, Pirkko; Mäkelä, Annikki; Minkkinen, Kari; Minunno, Francesco; Rankinen, Katri; Ojanen, Paavo; Paunu, Ville-Veikko; Peltoniemi, Mikko; Rasilo, Terhi; Sallantaus, Tapani; Savolahti, Mikko; Tuominen, Sakari; Tuominen, Seppo; Vanhala, Pekka; Forsius, Martin (2021)
    Climate change mitigation is a global response that requires actions at the local level. Quantifying local sources and sinks of greenhouse gases (GHG) facilitate evaluating mitigation options. We present an approach to collate spatially explicit estimated fluxes of GHGs (carbon dioxide, methane and nitrous oxide) for main land use sectors in the landscape, to aggregate, and to calculate the net emissions of an entire region. Our procedure was developed and tested in a large river basin in Finland, providing information from intensively studied eLTER research sites. To evaluate the full GHG balance, fluxes from natural ecosystems (lakes, rivers, and undrained mires) were included together with fluxes from anthropogenic activities, agriculture and forestry. We quantified the fluxes based on calculations with an anthropogenic emissions model (FRES) and a forest growth and carbon balance model (PREBAS), as well as on emission coefficients from the literature regarding emissions from lakes, rivers, undrained mires, peat extraction sites and cropland. Spatial data sources included CORINE land use data, soil map, lake and river shorelines, national forest inventory data, and statistical data on anthropogenic activities. Emission uncertainties were evaluated with Monte Carlo simulations. Artificial surfaces were the most emission intensive land-cover class. Lakes and rivers were about as emission intensive as arable land. Forests were the dominant land cover in the region (66%), and the C sink of the forests decreased the total emissions of the region by 72%. The region's net emissions amounted to 4.37 +/- 1.43 Tg CO2-eq yr(-1), corresponding to a net emission intensity 0.16 Gg CO2-eq km(-2) yr(-1), and estimated per capita net emissions of 5.6 Mg CO2-eq yr(-1). Our landscape approach opens opportunities to examine the sensitivities of important GHG fluxes to changes in land use and climate, management actions, and mitigation of anthropogenic emissions. (C) 2021 The Authors. Published by Elsevier B.V.
  • Liski, J.; Karjalainen, T.; Pussinen, A.; Nabuurs, G.-J.; Kauppi, P.E. (Elsevier, 2000)
    The carbon (C) sinks and sources of trees that may be accounted for under Article 3.3 of the Kyoto Protocol during the first commitment period from 2008 to 2012 were estimated for the countries of the European Union (EU) based on existing forest inventory data. Two sets of definitions for the accounted activities, afforestation, reforestation and deforestation, were applied. Applying the definitions by the Food and Agricultural Organization of the United Nations (FAO), the trees were estimated to be a C source in eight and a C sink in seven countries, and in the whole EU a C source of 5.4 Tg year-1. Applying the definitions by the Intergovernmental Panel of Climate Change (IPCC), the trees were estimated to be a C source in three and a C sink in 12 countries, and in the whole EU a C sink of 0.1 Tg year-1. These estimates are small compared with the C sink of trees in all EU forests, 63 Tg year-1, the anthropogenic CO2 emissions of the EU, 880 Tg C year-1, and the reduction target of the CO2 emissions, 8%. In individual countries, the estimated C sink of the trees accounted for under Article 3.3 was at largest 8% and the C source 12% compared with the CO2 emissions. 7 2000 Elsevier Science Ltd. All rights reserved.