Pekka Hurskainen

ORCID: 0000-0003-1039-3357
Publications
Citations
Views
---
Saved
---
About
Contact & Profiles
Research Areas
  • Remote Sensing and LiDAR Applications
  • Forest Ecology and Biodiversity Studies
  • Land Use and Ecosystem Services
  • Remote Sensing in Agriculture
  • Rangeland Management and Livestock Ecology
  • Species Distribution and Climate Change
  • Forest Management and Policy
  • Conservation, Biodiversity, and Resource Management
  • Soil erosion and sediment transport
  • Wildlife Ecology and Conservation
  • Urban Heat Island Mitigation
  • Ecology and Vegetation Dynamics Studies
  • Remote-Sensing Image Classification
  • Fire effects on ecosystems
  • Forest Insect Ecology and Management
  • Plant Ecology and Soil Science
  • Environmental Impact and Sustainability
  • Hydrology and Drought Analysis
  • Agriculture and Rural Development Research
  • Climate variability and models
  • Hydrology and Watershed Management Studies
  • 3D Surveying and Cultural Heritage
  • Earth Systems and Cosmic Evolution
  • Agriculture, Land Use, Rural Development
  • Urban Green Space and Health

Finnish Environment Institute
2019-2023

University of Helsinki
2014-2021

During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend focus on economically significant components of forests, thus leaving out many factors vital for biodiversity, such as occurrence species with low economical but high ecological values. Airborne hyperspectral imagery has shown potential tree classification, most common analysis methods, random support vector machines, require manual feature...

10.1016/j.rse.2021.112322 article EN cc-by Remote Sensing of Environment 2021-02-15

Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification inclusion of features from auxiliary geospatial datasets models applied since 1980s. However, the method mostly limited to pixel-based classifications, and coverage, resolution free open-access have been poor until recent years. We evaluated how global coverage object-based LULC compared spectral texture images. feature sets...

10.1016/j.rse.2019.111354 article EN cc-by Remote Sensing of Environment 2019-09-03

African mountains are characterized by high levels of biodiversity and provide ecosystem services to millions people. Due steep environmental gradients, growing human populations geographical isolation, these coupled socio-ecological systems highly vulnerable climate change impacts. The capacity local stakeholders anticipate future changes assess their potential impacts is paramount for enhancing adaptation resilience. Here we apply a participatory scenario development framework in two parts...

10.1007/s11625-018-0622-x article EN cc-by Sustainability Science 2018-08-29

The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize target management actions against multiple criteria, while also enabling cost-effective land use planning impact scenario assessment. This paper synthesises the development application an integrated multidisciplinary modelling evaluation framework for carbon in forest systems. By analysing spatio-temporally...

10.1016/j.scitotenv.2021.145847 article EN cc-by The Science of The Total Environment 2021-02-15

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on occurrence distribution key ecological features in environments. European aspen (Populus tremula L.) is one feature boreal forests contributing significantly to landscapes. However, due their sparse scattered northern Europe, explicit spatial data remain scarce incomprehensive, which hampers biodiversity conservation efforts. Our objective was study tree-level...

10.3390/rs12162610 article EN cc-by Remote Sensing 2020-08-13

The use of indicator species in forest conservation and management planning can facilitate enhanced preservation biodiversity from the negative effects forestry other uses land. However, this requires detailed spatially comprehensive knowledge habitat preferences distributions selected focal species. Unfortunately, due to limited resources for field surveys, only a small proportion occurrences is usually known. This shortcoming be circumvented by using modeling techniques predict spatial...

10.1002/eap.2505 article EN Ecological Applications 2021-12-06

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests. Large-diameter aspens maintain the diversity hundreds species, many which are threatened in Fennoscandia. Due to low economic value and relatively sparse scattered occurrence forests, there lack information spatial temporal distribution aspen, hampers efficient planning implementation sustainable forest management practices conservation efforts. Our objective was assess identification at individual...

10.3390/rs13091723 article EN cc-by Remote Sensing 2021-04-29

Urban ecosystem accounting can provide the structure for systematically integrating value of urban green spaces into management and decision making to support resilience sustainability. However, there are very few instructive examples accounting, particularly those created collaboratively with a municipality. Therefore, aim this study was develop co-created accounts using Tampere, Finland, as case study. By discussing concrete political planning-related needs, priorities, data availability,...

10.1016/j.ecoser.2023.101576 article EN cc-by Ecosystem Services 2023-12-06

Abstract. The Taita Hills, located in south-eastern Kenya, is one of the world’s biodiversity hotspots. Despite recognized ecological importance this region, landscape has been heavily fragmented due to hundreds years human activity. Most natural vegetation converted for agroforestry, croplands and exotic forest plantations, resulting a very heterogeneous landscape. Given complex agro-ecological context, characterizing land cover using traditional remote sensing methods extremely...

10.5194/isprsarchives-xl-7-w3-1277-2015 article EN cc-by ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences 2015-04-30

Up-to-date knowledge of key ecological features that maintain boreal forest biodiversity is essential part sustainable management and conservation measures. However, there only a limited amount spatial data available, as the detection ecologically significant elements using remote sensing challenging due to their low frequency, scattered occurrence, small size and/or location in field layer. We studied tree-level discrimination keystone species aspen (Populus tremula L.) from other common...

10.30677/terra.143654 article EN cc-by-nc-sa Terra 2024-12-15

<p>Importance of biodiversity is increasingly highlighted as an essential part sustainable forest management. As direct monitoring not possible, proxy variables have been used to indicate site’s species richness and quality. In boreal forests, European aspen (Populus tremula L.) one the most significant proxies for biodiversity. Aspen a keystone species, hosting range endangered hence having high importance in maintaining Still, reliable fine-scale spatial data on...

10.5194/egusphere-egu2020-21268 article EN 2020-03-10

<p>Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on occurrence distribution key ecological features in environments. Different proxy variables indicating species richness quality sites are essential for efficient detecting monitoring biodiversity. European aspen (Populus tremula L.) is a minor deciduous tree with high importance maintaining biodiversity boreal forests. Large trees host hundreds...

10.5194/egusphere-egu21-16273 article EN 2021-03-04
Coming Soon ...