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Petri Pellikka
Institute for Atmospheric and Earth System Research, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland

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Short Biography

Petri Pellikka is a professor in Earth observation at the University of Helsinki and director of multidisciplinary at Taita Research Station in Kenya. He leads the Earth Change Observation Lab within the Faculty of Science. His research interests are use of environmental sensing and remote sensing methods in environmental sustainability science.

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Journal article
Published: 01 July 2021 in International Journal of Environmental Research and Public Health
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Pogosta disease is a mosquito-borne infection, caused by Sindbis virus (SINV), which causes epidemics of febrile rash and arthritis in Northern Europe and South Africa. Resident grouse and migratory birds play a significant role as amplifying hosts and various mosquito species, including Aedes cinereus, Culex pipiens, Cx. torrentium and Culiseta morsitans are documented vectors. As specific treatments are not available for SINV infections, and joint symptoms may persist, the public health burden is considerable in endemic areas. To predict the environmental suitability for SINV infections in Finland, we applied a suite of geospatial and statistical modeling techniques to disease occurrence data. Using an ensemble approach, we first produced environmental suitability maps for potential SINV vectors in Finland. These suitability maps were then combined with grouse densities and environmental data to identify the influential determinants for SINV infections and to predict the risk of Pogosta disease in Finnish municipalities. Our predictions suggest that both the environmental suitability for vectors and the high risk of Pogosta disease are focused in geographically restricted areas. This provides evidence that the presence of both SINV vector species and grouse densities can predict the occurrence of the disease. The results support material for public-health officials when determining area-specific recommendations and deliver information to health care personnel to raise awareness of the disease among physicians.

ACS Style

Ruut Uusitalo; Mika Siljander; C. Culverwell; Guy Hendrickx; Andreas Lindén; Timothée Dub; Juha Aalto; Jussi Sane; Cedric Marsboom; Maija Suvanto; Andrea Vajda; Hilppa Gregow; Essi Korhonen; Eili Huhtamo; Petri Pellikka; Olli Vapalahti. Predicting Spatial Patterns of Sindbis Virus (SINV) Infection Risk in Finland Using Vector, Host and Environmental Data. International Journal of Environmental Research and Public Health 2021, 18, 7064 .

AMA Style

Ruut Uusitalo, Mika Siljander, C. Culverwell, Guy Hendrickx, Andreas Lindén, Timothée Dub, Juha Aalto, Jussi Sane, Cedric Marsboom, Maija Suvanto, Andrea Vajda, Hilppa Gregow, Essi Korhonen, Eili Huhtamo, Petri Pellikka, Olli Vapalahti. Predicting Spatial Patterns of Sindbis Virus (SINV) Infection Risk in Finland Using Vector, Host and Environmental Data. International Journal of Environmental Research and Public Health. 2021; 18 (13):7064.

Chicago/Turabian Style

Ruut Uusitalo; Mika Siljander; C. Culverwell; Guy Hendrickx; Andreas Lindén; Timothée Dub; Juha Aalto; Jussi Sane; Cedric Marsboom; Maija Suvanto; Andrea Vajda; Hilppa Gregow; Essi Korhonen; Eili Huhtamo; Petri Pellikka; Olli Vapalahti. 2021. "Predicting Spatial Patterns of Sindbis Virus (SINV) Infection Risk in Finland Using Vector, Host and Environmental Data." International Journal of Environmental Research and Public Health 18, no. 13: 7064.

Journal article
Published: 30 April 2021 in Water
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Model evaluation against experimental data is an important step towards accurate model predictions and simulations. Here, we evaluated an energy-balance model to predict dew formation occurrence and estimate its amount for East-African arid-climate conditions against 13 months of experimental dew harvesting data in Maktau, Kenya. The model was capable of predicting the dew formation occurrence effectively. However, it overestimated the harvestable dew amount by about a ratio of 1.7. As such, a factor of 0.6 was applied for a long-term period (1979–2018) to investigate the spatial and temporal variation of the dew formation in Kenya. The annual average of dew occurrence in Kenya was ~130 days with dew yield > 0.1 L/m2/day. The dew formation showed a seasonal cycle with the maximum yield in winter and minimum in summer. Three major dew formation zones were identified after cluster analysis: arid and semi-arid regions; mountain regions; and coastal regions. The average daily and yearly maximum dew yield were 0.05 and 18; 0.9 and 25; and 0.15 and 40 L/m2/day; respectively. A precise prediction of dew occurrence and dew yield is very challenging due to inherent limitations in numerical models and meteorological input parameters.

ACS Style

Nahid Atashi; Juuso Tuure; Laura Alakukku; Dariush Rahimi; Petri Pellikka; Martha Zaidan; Henri Vuollekoski; Matti Räsänen; Markku Kulmala; Timo Vesala; Tareq Hussein. An Attempt to Utilize a Regional Dew Formation Model in Kenya. Water 2021, 13, 1261 .

AMA Style

Nahid Atashi, Juuso Tuure, Laura Alakukku, Dariush Rahimi, Petri Pellikka, Martha Zaidan, Henri Vuollekoski, Matti Räsänen, Markku Kulmala, Timo Vesala, Tareq Hussein. An Attempt to Utilize a Regional Dew Formation Model in Kenya. Water. 2021; 13 (9):1261.

Chicago/Turabian Style

Nahid Atashi; Juuso Tuure; Laura Alakukku; Dariush Rahimi; Petri Pellikka; Martha Zaidan; Henri Vuollekoski; Matti Räsänen; Markku Kulmala; Timo Vesala; Tareq Hussein. 2021. "An Attempt to Utilize a Regional Dew Formation Model in Kenya." Water 13, no. 9: 1261.

Journal article
Published: 25 April 2021 in Remote Sensing
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Understanding the response of vegetation and ecosystem resilience to climate variability and drought conditions is essential for ecosystem planning and management. In this study, we assessed the vegetation changes and ecosystem resilience in the Horn of Africa (HOA) since 2000 and detected their drivers based mainly on analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) products. We found that the annual and seasonal trends of NDVI (Normalized Difference Vegetation Index) generally increased during the last two decades over the Horn of Africa particularly in western parts of Ethiopia and Kenya. The weakest annual and seasonal NDVI trends were observed over the grassland cover and tropical arid agroecological zones. The NDVI variation negatively correlated with Land Surface Temperature (LST) and positively correlated with precipitation at a significant level (p < 0.05) account for 683,197 km2 and 533,385 km2 area, respectively. The ecosystem Water Use Efficiency (eWUE) showed overall increasing trends with larger values for the grassland biome. The precipitation had the most significant effect on eWUE variation compared to LST and annual SPEI (Standardized Evapotranspiration Index). There were about 54.9% of HOA resilient to drought disturbance, whereas 32.6% was completely not-resilient. The ecosystems in the humid agroecological zones, the cropland, and wetland were slightly not-resilient to severe drought conditions in the region. This study provides useful information for policy makers regarding ecosystem and dryland management in the context of climate change at both national and regional levels.

ACS Style

Simon Measho; Baozhang Chen; Petri Pellikka; Lifeng Guo; Huifang Zhang; Diwen Cai; Shaobo Sun; Alphonse Kayiranga; Xiaohong Sun; Mengyu Ge. Assessment of Vegetation Dynamics and Ecosystem Resilience in the Context of Climate Change and Drought in the Horn of Africa. Remote Sensing 2021, 13, 1668 .

AMA Style

Simon Measho, Baozhang Chen, Petri Pellikka, Lifeng Guo, Huifang Zhang, Diwen Cai, Shaobo Sun, Alphonse Kayiranga, Xiaohong Sun, Mengyu Ge. Assessment of Vegetation Dynamics and Ecosystem Resilience in the Context of Climate Change and Drought in the Horn of Africa. Remote Sensing. 2021; 13 (9):1668.

Chicago/Turabian Style

Simon Measho; Baozhang Chen; Petri Pellikka; Lifeng Guo; Huifang Zhang; Diwen Cai; Shaobo Sun; Alphonse Kayiranga; Xiaohong Sun; Mengyu Ge. 2021. "Assessment of Vegetation Dynamics and Ecosystem Resilience in the Context of Climate Change and Drought in the Horn of Africa." Remote Sensing 13, no. 9: 1668.

Journal article
Published: 09 March 2021 in Sustainability
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With the simultaneous rise of concern about the climate crisis and the growing internationalization of research institutions, academic mobility poses an “academic paradox”: knowledge of the environmental harm of aviation does not necessarily translate into action. Universities must make changes to their mobility habits if they wish to comply with governmental carbon neutrality targets and lead with example. This research looks at Finland’s 14 universities and identifies the patterns and trends of academic mobility from a series of reports provided by the universities and their travel agencies. Moreover, we mapped the travel destinations to understand the scope of Finnish academic travel. The data revealed that Finnish universities are in different states of sustainability: some acting as clear trendsetters and others lagging. The results show that although the universities are performing well in some areas, as in preferring European destinations over intercontinental ones, there are still areas of improvement related to stopover reduction, the number of 1- and 2-day trips, and alternative transport forms to aviation. There is also a need for the standardization of targets and emission calculators. These key development areas are posed as recommendations through which the universities could easily reduce the carbon footprint of their mobility.

ACS Style

Veronica Ahonen; Mika Siljander; Petri Pellikka; Tino Johansson; Mikko Rask. The Sustainability of Academic Air Mobility in Finnish Universities. Sustainability 2021, 13, 2948 .

AMA Style

Veronica Ahonen, Mika Siljander, Petri Pellikka, Tino Johansson, Mikko Rask. The Sustainability of Academic Air Mobility in Finnish Universities. Sustainability. 2021; 13 (5):2948.

Chicago/Turabian Style

Veronica Ahonen; Mika Siljander; Petri Pellikka; Tino Johansson; Mikko Rask. 2021. "The Sustainability of Academic Air Mobility in Finnish Universities." Sustainability 13, no. 5: 2948.

Journal article
Published: 12 January 2021 in Remote Sensing
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Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

ACS Style

Ilja Vuorinne; Janne Heiskanen; Petri Pellikka. Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices. Remote Sensing 2021, 13, 233 .

AMA Style

Ilja Vuorinne, Janne Heiskanen, Petri Pellikka. Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices. Remote Sensing. 2021; 13 (2):233.

Chicago/Turabian Style

Ilja Vuorinne; Janne Heiskanen; Petri Pellikka. 2021. "Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices." Remote Sensing 13, no. 2: 233.

Journal article
Published: 13 December 2020 in Diversity
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Three poorly known nocturnal mammal species from the montane forests of the Taita Hills in Kenya, were studied via vocalization analysis. Here, their acoustic behaviour is described. The studied animals were the tree hyrax (Dendrohyrax sp.), the small-eared greater galago (Otolemur garnettii), and the dwarf galago (Paragalago sp.). High-quality loud calls were analysed using RAVEN PRO, and compared to calls of presumed closest relatives. Our findings include the first detailed descriptions of tree hyrax songs. Moreover, our results suggest that the tree hyrax of Taita Hills may be a taxon new to science, as it produces a characteristic call, the ‘strangled thwack’, not previously known from other Dendrohyrax populations. Our data confirms that the small-eared greater galago subspecies living in the Taita Hills is Otolemur garnettii lasiotis. The loud calls of the elusive Taita Hills dwarf galago closely resemble those of the Kenya coast dwarf galago (Paragalago cocos). Thus, the population in the Taita Hills probably belongs to this species. The Taita Hills dwarf galagos are geographically isolated from other dwarf galago populations, and live in montane cloud forest, which is an unusual habitat for P. cocos. Intriguingly, two dwarf galago subpopulations living in separate forest patches in the Taita Hills, Ngangao and Mbololo, have clearly different contact calls. The Paragalagos in Mbololo Forest may represent a population of P. cocos with a derived call repertoire, or, alternatively, they may actually be mountain dwarf galagos (P. orinus). Hence, differences in habitat, behaviour, and contact call structure suggest that there may be two different Paragalago species in the montane forests of Taita Hills.

ACS Style

Hanna Rosti; Henry Pihlström; Simon Bearder; Petri Pellikka; Jouko Rikkinen. Vocalization Analyses of Nocturnal Arboreal Mammals of the Taita Hills, Kenya. Diversity 2020, 12, 473 .

AMA Style

Hanna Rosti, Henry Pihlström, Simon Bearder, Petri Pellikka, Jouko Rikkinen. Vocalization Analyses of Nocturnal Arboreal Mammals of the Taita Hills, Kenya. Diversity. 2020; 12 (12):473.

Chicago/Turabian Style

Hanna Rosti; Henry Pihlström; Simon Bearder; Petri Pellikka; Jouko Rikkinen. 2020. "Vocalization Analyses of Nocturnal Arboreal Mammals of the Taita Hills, Kenya." Diversity 12, no. 12: 473.

Technical note
Published: 12 October 2020 in Remote Sensing
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Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated. Band selection is a technique that provides clear physical-meaning results for hyperspectral dimensional reduction, alleviating the difficulty for transferring and processing hyperspectral images caused by a property of hyperspectral images: large data volumes. In this study, a simple and efficient band ranking via extended coefficient of variation (BRECV) is proposed for unsupervised hyperspectral band selection. The naive idea of the BRECV algorithm is to select bands with relatively smaller means and lager standard deviations compared to their adjacent bands. To make this simple idea into an algorithm, and inspired by coefficient of variation (CV), we constructed an extended CV matrix for every three adjacent bands to study the changes of means and standard deviations, and accordingly propose a criterion to allocate values to each band for ranking. A derived unsupervised band selection based on the same idea while using entropy is also presented. Though the underlying idea is quite simple, and both cluster and optimization methods are not used, the BRECV method acquires qualitatively the same level of classification accuracy, compared with some state-of-the-art band selection methods

ACS Style

Peifeng Su; Sasu Tarkoma; Petri Pellikka. Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection. Remote Sensing 2020, 12, 3319 .

AMA Style

Peifeng Su, Sasu Tarkoma, Petri Pellikka. Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection. Remote Sensing. 2020; 12 (20):3319.

Chicago/Turabian Style

Peifeng Su; Sasu Tarkoma; Petri Pellikka. 2020. "Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection." Remote Sensing 12, no. 20: 3319.

Journal article
Published: 09 October 2020 in Land
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Savannahs provide valuable ecosystem services and contribute to continental and global carbon budgets. In addition, savannahs exhibit multiple land uses, e.g., wildlife conservation, pastoralism, and crop farming. Despite their importance, the effect of land use on woody aboveground biomass (AGB) in savannahs is understudied. Furthermore, fences used to reduce human–wildlife conflicts may affect AGB patterns. We assessed AGB densities and patterns, and the effect of land use and fences on AGB in a multi-use savannah landscape in southeastern Kenya. AGB was assessed with field survey and airborne laser scanning (ALS) data, and a land cover map was developed using Sentinel-2 satellite images in Google Earth Engine. The highest woody AGB was found in riverine forest in a conservation area and in bushland outside the conservation area. The highest mean AGB density occurred in the non-conservation area with mixed bushland and cropland (8.9 Mg·ha−1), while the lowest AGB density (2.6 Mg·ha−1) occurred in overgrazed grassland in the conservation area. The largest differences in AGB distributions were observed in the fenced boundaries between the conservation and other land-use types. Our results provide evidence that conservation and fences can create sharp AGB transitions and lead to reduced AGB stocks, which is a vital role of savannahs as part of carbon sequestration.

ACS Style

Edward Amara; Hari Adhikari; Janne Heiskanen; Mika Siljander; Martha Munyao; Patrick Omondi; Petri Pellikka. Aboveground Biomass Distribution in a Multi-Use Savannah Landscape in Southeastern Kenya: Impact of Land Use and Fences. Land 2020, 9, 381 .

AMA Style

Edward Amara, Hari Adhikari, Janne Heiskanen, Mika Siljander, Martha Munyao, Patrick Omondi, Petri Pellikka. Aboveground Biomass Distribution in a Multi-Use Savannah Landscape in Southeastern Kenya: Impact of Land Use and Fences. Land. 2020; 9 (10):381.

Chicago/Turabian Style

Edward Amara; Hari Adhikari; Janne Heiskanen; Mika Siljander; Martha Munyao; Patrick Omondi; Petri Pellikka. 2020. "Aboveground Biomass Distribution in a Multi-Use Savannah Landscape in Southeastern Kenya: Impact of Land Use and Fences." Land 9, no. 10: 381.

Journal article
Published: 16 May 2020 in Ticks and Tick-borne Diseases
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The numbers of reported human tick-borne encephalitis (TBE) cases in Europe have increased in several endemic regions (including Finland) in recent decades, indicative of an increasing threat to public health. As such, it is important to identify the regions at risk and the most influential factors associated with TBE distributions, particularly in understudied regions. This study aimed to identify the risk areas of TBE transmission in two different datasets based on human TBE disease cases from 2007 to 2011 (n = 86) and 2012–2017 (n = 244). We also examined which factors best explain the presence of human TBE cases. We used ensemble modelling to determine the relationship of TBE occurrence with environmental, ecological, and anthropogenic factors in Finland. Geospatial data including these variables were acquired from several open data sources and satellite and aerial imagery and, were processed in GIS software. Biomod2, an ensemble platform designed for species distribution modelling, was used to generate ensemble models in R. The proportion of built-up areas, field, forest, and snow-covered land in November, people working in the primary sector, human population density, mean precipitation in April and July, and densities of European hares, white-tailed deer, and raccoon dogs best estimated distribution of human TBE disease cases in the two datasets. Random forest and generalized boosted regression models performed with a very good to excellent predictive power (ROC = 0.89–0.96) in both time periods. Based on the predictive maps, high-risk areas for TBE transmission were located in the coastal regions in Southern and Western Finland (including the Åland Islands), several municipalities in Central and Eastern Finland, and coastal municipalities in Southern Lapland. To explore potential changes in TBE distributions in future climate, we used bioclimatic factors with current and future climate forecast data to reveal possible future hotspot areas. Based on the future forecasts, a slightly wider geographical extent of TBE risk was introduced in the Åland Islands and Southern, Western and Northern Finland, even though the risk itself was not increased. Our results are the first steps towards TBE-risk area mapping in current and future climate in Finland.

ACS Style

Ruut Uusitalo; Mika Siljander; Timothée Dub; Jussi Sane; Jani J. Sormunen; Petri Pellikka; Olli Vapalahti. Modelling habitat suitability for occurrence of human tick-borne encephalitis (TBE) cases in Finland. Ticks and Tick-borne Diseases 2020, 11, 101457 .

AMA Style

Ruut Uusitalo, Mika Siljander, Timothée Dub, Jussi Sane, Jani J. Sormunen, Petri Pellikka, Olli Vapalahti. Modelling habitat suitability for occurrence of human tick-borne encephalitis (TBE) cases in Finland. Ticks and Tick-borne Diseases. 2020; 11 (5):101457.

Chicago/Turabian Style

Ruut Uusitalo; Mika Siljander; Timothée Dub; Jussi Sane; Jani J. Sormunen; Petri Pellikka; Olli Vapalahti. 2020. "Modelling habitat suitability for occurrence of human tick-borne encephalitis (TBE) cases in Finland." Ticks and Tick-borne Diseases 11, no. 5: 101457.

Regular article
Published: 25 April 2020 in Plant and Soil
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Aims Decomposition of manure deposited onto pasture from grazing animals represents an important process for carbon (C) and nitrogen (N) cycles in grassland systems. However, studies investigating manure decomposition are scarce; especially in sub-Saharan Africa (SSA). Methods In this study, we measured decomposition of three types of animal manure (cattle, sheep, goat) over >1 year using litter bags at four climatically different sites across Kenya. Results Manure dry matter, total C, total N and ammonium concentrations decreased exponentially, with the most rapid decrease occurring during the first few weeks following application, followed by slower changes during the following 2–3 months. Rates of N mineralization were lower than those for C mineralization, resulting in decreasing C/N ratios over time. Generally, cattle manure decomposed faster than sheep or goat manure despite having a higher initial C/N ratio and lower N concentration, with decomposition rates for dry matter ranging from 0.200 to 0.989 k year−1. Cellulose decomposed first, while lignin concentrations increased among all manure types and at all sites. Conclusions We found that total manure decomposition rates were positively correlated with cumulative precipitation and aridity index, but negatively correlated with mean temperature. Our results show much slower decomposition rates of manures in semi-arid tropical environments of East Africa as compared to the few previous studies in temperate climates.

ACS Style

Yuhao Zhu; Lutz Merbold; Sonja Leitner; David E. Pelster; Sheila Abwanda Okoma; Felix Ngetich; Alice Anyango Onyango; Petri Pellikka; Klaus Butterbach-Bahl. The effects of climate on decomposition of cattle, sheep and goat manure in Kenyan tropical pastures. Plant and Soil 2020, 451, 325 -343.

AMA Style

Yuhao Zhu, Lutz Merbold, Sonja Leitner, David E. Pelster, Sheila Abwanda Okoma, Felix Ngetich, Alice Anyango Onyango, Petri Pellikka, Klaus Butterbach-Bahl. The effects of climate on decomposition of cattle, sheep and goat manure in Kenyan tropical pastures. Plant and Soil. 2020; 451 (1-2):325-343.

Chicago/Turabian Style

Yuhao Zhu; Lutz Merbold; Sonja Leitner; David E. Pelster; Sheila Abwanda Okoma; Felix Ngetich; Alice Anyango Onyango; Petri Pellikka; Klaus Butterbach-Bahl. 2020. "The effects of climate on decomposition of cattle, sheep and goat manure in Kenyan tropical pastures." Plant and Soil 451, no. 1-2: 325-343.

Preprint content
Published: 23 March 2020
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Climate variability, drought, and deforestation are increasing in the Horn of Africa (HOA). Evaluating land use/land cover (LULC) changes and their impacts on water availability and variation are vital actions for regional land-use planning and water resources management. LULC changes during 2000-2015 were estimated using high resolution Landsat images and Google Earth Engine cloud platform, and land-use dynamics index (K). The impact of LULC change on water yield was evaluated using the InVEST model. The results at regional scale show that there were rapid decreases in the area of forests and barren lands (-K) while there was a drastic increase in built-up area (+K values). The transition was found to decrease from forested land to low biomass with highest and lowest values of 51.13% and 16.7%, respectively. There were similar LULC changes in the Mereb-Gash river basin. The total annual water yield increased for all the catchments during 2000-2015, and reached the peak in 2010. The highest annual water yield decreased in the forested lands from 43.18 million m3 in 2000 to 4.1 million m3 in 2015. There was a strong positive correlation between areal changes (%) and the annual water yield variations (%) in all the LULC types except for the water body, and the correlation was significantly positive for the forested areas (p<0.01). The study demonstrates that the decrease in forested areas and expansion in the built-up areas had large impact on water yield. The impacts may further increase pressure on the ecosystem services, exacerbate water scarcity, and food insecurity unless basic measures are planned and implemented.

Key words: LULC; climate variability; InVEST; annual water yield; K-index

 

ACS Style

Simon Measho; Baozhang Chen; Petri Pellikka; Lifeng Guo; Huifang Zhang. Land use/Land cover Changes and Associated Impacts on Water Yield Availability and Variation, Mereb-Gash River Basin in Horn of Africa. 2020, 1 .

AMA Style

Simon Measho, Baozhang Chen, Petri Pellikka, Lifeng Guo, Huifang Zhang. Land use/Land cover Changes and Associated Impacts on Water Yield Availability and Variation, Mereb-Gash River Basin in Horn of Africa. . 2020; ():1.

Chicago/Turabian Style

Simon Measho; Baozhang Chen; Petri Pellikka; Lifeng Guo; Huifang Zhang. 2020. "Land use/Land cover Changes and Associated Impacts on Water Yield Availability and Variation, Mereb-Gash River Basin in Horn of Africa." , no. : 1.

Journal article
Published: 14 March 2020 in Applied Geography
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Human–wildlife conflict (HWC) is a growing concern for local communities living in the vicinity of protected areas. These conflicts commonly take place as attack by wild animals and crop-raiding events, among other forms. We studied crop-raiding patterns by non-human primates in forest–agricultural landscape mosaic in the Taita Hills, southeast Kenya. The study applies both qualitative and quantitative methods. Semi-structured questionnaire was used in the primary data collection from the households, and statistical tests were performed. We used applied geospatial methods to reveal spatial patterns of crop-raiding by primates and preventive actions by farmers. The results indicate most of the farms experienced crop-raiding on a weekly basis. Blue monkey (Cercopithecus mitis) was the worst crop-raiding species and could be found in habitats covered by different land use/land cover types. Vervet monkey (Chlorocebus pygerythrus) and galagos crop-raided farms in areas with abundant tree canopy cover. Only few baboons (Papio cynocephalus) were reported to raid crops in the area. Results also show that the closer a farm is to the forest boundary and the less neighbouring farms there are between the farm and the forest, the more vulnerable it is for crop-raiding by blue monkeys, but not by any other studied primate species. The study could not show that a specific type of food crop in a farm or type of land use/land cover inside the wildlife corridor between the farmland and the forest boundary explain households’ vulnerability to crop-raiding by primates. Preventive actions against crop-raiding by farmers where taken all around the studied area in various forms. Most of the studied households rely on subsistence farming as their main livelihood and therefore crop-raiding by primates is a serious threat to their food security in the area.

ACS Style

Mika Siljander; Toini Kuronen; Tino Johansson; Martha Nzisa Munyao; Petri K.E. Pellikka. Primates on the farm – spatial patterns of human–wildlife conflict in forest-agricultural landscape mosaic in Taita Hills, Kenya. Applied Geography 2020, 117, 102185 .

AMA Style

Mika Siljander, Toini Kuronen, Tino Johansson, Martha Nzisa Munyao, Petri K.E. Pellikka. Primates on the farm – spatial patterns of human–wildlife conflict in forest-agricultural landscape mosaic in Taita Hills, Kenya. Applied Geography. 2020; 117 ():102185.

Chicago/Turabian Style

Mika Siljander; Toini Kuronen; Tino Johansson; Martha Nzisa Munyao; Petri K.E. Pellikka. 2020. "Primates on the farm – spatial patterns of human–wildlife conflict in forest-agricultural landscape mosaic in Taita Hills, Kenya." Applied Geography 117, no. : 102185.

Journal article
Published: 23 August 2019 in International Journal of Applied Earth Observation and Geoinformation
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Three-quarters of Finland’s land surface area is filled with forests, which compose a great part of the country’s biomass, carbon pools and carbon sinks. In order to acquire up-to-date information on the forests, optical remote sensing techniques are commonly used. Moreover, in the future hyperspectral satellite missions will start providing data to support the needs of natural resource management practices, such as forestry. It is, however, unclear what would be the additional value from using hyperspectral data compared to multispectral in quantifying forest variables of Finnish boreal forest. In this study, we used the remote sensing data by hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m) and Sentinel-2 (10 bands, resolution 10 m) to assess the possible benefits of higher spectral resolution. As reference data, we used a new nationwide forest resource dataset (stand-level data), which has a high potential in further remote sensing applications. In addition, we used a set of independent in situ measurements (plot-level data) for validation. We applied two kernel-based machine learning regression algorithms (Gaussian process and support vector regression) to relate boreal forest variables with the remote sensing data. The variables of interest were mean height, basal area, leaf area index (LAI), stem biomass and main tree species. The regression algorithms were trained with stand-level data and estimations were evaluated with stand- and plot-level holdout sets. The estimation accuracies were examined with absolute and relative root-mean-square errors. Successful variable estimations showed that kernel-based regression algorithms are suitable tools for forest structure estimation. Based on the results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as mean height, basal area and stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.

ACS Style

Eelis Halme; Petri Pellikka; Matti Mõttus. Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest. International Journal of Applied Earth Observation and Geoinformation 2019, 83, 101942 .

AMA Style

Eelis Halme, Petri Pellikka, Matti Mõttus. Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest. International Journal of Applied Earth Observation and Geoinformation. 2019; 83 ():101942.

Chicago/Turabian Style

Eelis Halme; Petri Pellikka; Matti Mõttus. 2019. "Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest." International Journal of Applied Earth Observation and Geoinformation 83, no. : 101942.

Journal article
Published: 26 March 2019 in Remote Sensing
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There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services.

ACS Style

Simon Measho; Baozhang Chen; Yongyut Trisurat; Petri Pellikka; Lifeng Guo; Sunsanee Arunyawat; Venus Tuankrua; Woldeselassie Ogbazghi; Tecle Yemane. Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea. Remote Sensing 2019, 11, 724 .

AMA Style

Simon Measho, Baozhang Chen, Yongyut Trisurat, Petri Pellikka, Lifeng Guo, Sunsanee Arunyawat, Venus Tuankrua, Woldeselassie Ogbazghi, Tecle Yemane. Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea. Remote Sensing. 2019; 11 (6):724.

Chicago/Turabian Style

Simon Measho; Baozhang Chen; Yongyut Trisurat; Petri Pellikka; Lifeng Guo; Sunsanee Arunyawat; Venus Tuankrua; Woldeselassie Ogbazghi; Tecle Yemane. 2019. "Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea." Remote Sensing 11, no. 6: 724.

Journal article
Published: 25 November 2018 in International Journal of Applied Earth Observation and Geoinformation
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Mosquitoes are vectors for numerous pathogens, which are collectively responsible for millions of human deaths each year. As such, it is vital to be able to accurately predict their distributions, particularly in areas where species composition is unknown. Species distribution modeling was used to determine the relationship between environmental, anthropogenic and distance factors on the occurrence of two mosquito genera, Culex Linnaeus and Stegomyia Theobald (syn. Aedes), in the Taita Hills, southeastern Kenya. This study aims to test whether any of the statistical prediction models produced by the Biomod2 package in R can reliably estimate the distributions of mosquitoes in these genera in the Taita Hills; and to examine which factors best explain their presence. Mosquito collections were acquired from 122 locations between January–March 2016 along transects throughout the Taita Hills. Environmental-, anthropogenic- and distance-based geospatial data were acquired from the Taita Hills geo-database, satellite- and aerial imagery and processed in GIS software. The Biomod2 package in R, intended for ensemble forecasting of species distributions, was used to generate predictive models. Slope, human population density, normalized difference vegetation index, distance to roads and elevation best estimated Culex distributions by a generalized additive model with an area under the curve (AUC) value of 0.791. Mean radiation, human population density, normalized difference vegetation index, distance to roads and mean temperature resulted in the highest AUC (0.708) value in a random forest model for Stegomyia distributions. We conclude that in the process towards more detailed species-level maps, with our study results, general assumptions can be made about the distribution areas of Culex and Stegomyia mosquitoes in the Taita Hills and the factors which influence their distribution.

ACS Style

Ruut Uusitalo; Mika Siljander; C. Lorna Culverwell; Noah C. Mutai; Kristian M. Forbes; Olli Vapalahti; Petri K.E. Pellikka. Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya. International Journal of Applied Earth Observation and Geoinformation 2018, 76, 84 -92.

AMA Style

Ruut Uusitalo, Mika Siljander, C. Lorna Culverwell, Noah C. Mutai, Kristian M. Forbes, Olli Vapalahti, Petri K.E. Pellikka. Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya. International Journal of Applied Earth Observation and Geoinformation. 2018; 76 ():84-92.

Chicago/Turabian Style

Ruut Uusitalo; Mika Siljander; C. Lorna Culverwell; Noah C. Mutai; Kristian M. Forbes; Olli Vapalahti; Petri K.E. Pellikka. 2018. "Predictive mapping of mosquito distribution based on environmental and anthropogenic factors in Taita Hills, Kenya." International Journal of Applied Earth Observation and Geoinformation 76, no. : 84-92.

Journal article
Published: 22 August 2018 in Applied Sciences
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Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR705), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60°. Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

ACS Style

Xiaochen Zou; Iina Haikarainen; Iikka P. Haikarainen; Pirjo Mäkelä; Matti Mõttus; Petri Pellikka. Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy. Applied Sciences 2018, 8, 1435 .

AMA Style

Xiaochen Zou, Iina Haikarainen, Iikka P. Haikarainen, Pirjo Mäkelä, Matti Mõttus, Petri Pellikka. Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy. Applied Sciences. 2018; 8 (9):1435.

Chicago/Turabian Style

Xiaochen Zou; Iina Haikarainen; Iikka P. Haikarainen; Pirjo Mäkelä; Matti Mõttus; Petri Pellikka. 2018. "Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy." Applied Sciences 8, no. 9: 1435.

Journal article
Published: 05 April 2018 in Applied Geography
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Land cover change takes place in sub-Saharan Africa as forests and shrublands are converted to agricultural lands in order to meet the needs of growing population. Changes in land cover also impact carbon sequestration in vegetation cover with an influence on climate on continental scale. The impact of land cover change on tree aboveground carbon stocks was studied in Taita Hills, Kenya. The land cover change between 1987 and 2011 for four points of time was assessed using SPOT satellite imagery, while the carbon density in various land cover types was assessed with field measurements, allometric biomass functions and airborne laser scanning data. Finally, the mean carbon densities of land cover types were combined with land cover maps resulting in carbon stock values for given land cover types for each point of time studied. Expansion of croplands has been taking place since 1987 and before on the cost of thickets and shrublands, especially on the foothills and lowlands. Due to the land cover changes, the carbon stock of trees was decreasing until 2003, after which there has been an increase. The findings of the research is supported by forest transition model, which emphasizes increase of awareness of forests' role in providing ecosystem services, such as habitats for pollinators, water harvesting and storage at the same time when economic reasons in making land-use choices between cropland and woodland, and governmental legislation supports trees on farms.

ACS Style

P.K.E. Pellikka; V. Heikinheimo; J. Hietanen; E. Schäfer; Mika Siljander; J. Heiskanen. Impact of land cover change on aboveground carbon stocks in Afromontane landscape in Kenya. Applied Geography 2018, 94, 178 -189.

AMA Style

P.K.E. Pellikka, V. Heikinheimo, J. Hietanen, E. Schäfer, Mika Siljander, J. Heiskanen. Impact of land cover change on aboveground carbon stocks in Afromontane landscape in Kenya. Applied Geography. 2018; 94 ():178-189.

Chicago/Turabian Style

P.K.E. Pellikka; V. Heikinheimo; J. Hietanen; E. Schäfer; Mika Siljander; J. Heiskanen. 2018. "Impact of land cover change on aboveground carbon stocks in Afromontane landscape in Kenya." Applied Geography 94, no. : 178-189.

Journal article
Published: 23 August 2017 in Remote Sensing
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Airborne imaging spectroscopy (IS) and laser scanning (ALS) have been explored widely for tree species classification during the past decades. However, African agroforestry areas, where a few exotic tree species are dominant and many native species occur less frequently, have not yet been studied. Obtaining maps of tree species would provide useful information for the characterization of agroforestry systems and detecting invasive species. Our objective was to study tree species classification in a diverse tropical landscape using IS and ALS data at the tree crown level, with primary interest in the exotic tree species. We performed multiple analyses based on different IS and ALS feature sets, identified important features using feature selection, and evaluated the impact of combining the two data sources. Given that a high number of tree species with limited sample size (499 samples for 31 species) was expected to limit the classification accuracy, we tested different approaches to group the species based on the frequency of their occurrence and Jeffries–Matusita (JM) distance. Surface reflectance at wavelengths between 400–450 nm and 750–800 nm, and height to crown width ratio, were identified as important features. Nonetheless, a selection of minimum noise fraction (MNF) transformed reflectance bands showed superior performance. Support vector machine classifier performed slightly better than the random forest classifier, but the improvement was not statistically significant for the best performing feature set. The highest F1-scores were achieved when each of the species was classified separately against a mixed group of all other species, which makes this approach suitable for invasive species detection. Our results are valuable for organizations working on biodiversity conservation and improving agroforestry practices, as we showed how the non-native Eucalyptus spp., Acacia mearnsii and Grevillea robusta (mean F1-scores 76%, 79% and 89%, respectively) trees can be mapped with good accuracy. We also found a group of six fruit bearing trees using JM distance, which was classified with mean F1-score of 65%. This was a useful finding, as these species could not be classified with acceptable accuracy individually, while they all share common economic and ecological importance.

ACS Style

Rami Piiroinen; Janne Heiskanen; Eduardo Maeda; Arto Viinikka; Petri Pellikka. Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning. Remote Sensing 2017, 9, 875 .

AMA Style

Rami Piiroinen, Janne Heiskanen, Eduardo Maeda, Arto Viinikka, Petri Pellikka. Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning. Remote Sensing. 2017; 9 (9):875.

Chicago/Turabian Style

Rami Piiroinen; Janne Heiskanen; Eduardo Maeda; Arto Viinikka; Petri Pellikka. 2017. "Classification of Tree Species in a Diverse African Agroforestry Landscape Using Imaging Spectroscopy and Laser Scanning." Remote Sensing 9, no. 9: 875.

Journal article
Published: 11 August 2017 in Remote Sensing
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Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region.

ACS Style

Hari Adhikari; Janne Heiskanen; Mika Siljander; Eduardo Maeda; Vuokko Heikinheimo; Petri K. E. Pellikka. Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya. Remote Sensing 2017, 9, 827 .

AMA Style

Hari Adhikari, Janne Heiskanen, Mika Siljander, Eduardo Maeda, Vuokko Heikinheimo, Petri K. E. Pellikka. Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya. Remote Sensing. 2017; 9 (8):827.

Chicago/Turabian Style

Hari Adhikari; Janne Heiskanen; Mika Siljander; Eduardo Maeda; Vuokko Heikinheimo; Petri K. E. Pellikka. 2017. "Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya." Remote Sensing 9, no. 8: 827.

Journal article
Published: 07 November 2016 in The Professional Geographer
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ACS Style

Johanna Hohenthal; Paola Minoia; Petri Pellikka. Mapping Meaning: Critical Cartographies for Participatory Water Management in Taita Hills, Kenya. The Professional Geographer 2016, 69, 383 -395.

AMA Style

Johanna Hohenthal, Paola Minoia, Petri Pellikka. Mapping Meaning: Critical Cartographies for Participatory Water Management in Taita Hills, Kenya. The Professional Geographer. 2016; 69 (3):383-395.

Chicago/Turabian Style

Johanna Hohenthal; Paola Minoia; Petri Pellikka. 2016. "Mapping Meaning: Critical Cartographies for Participatory Water Management in Taita Hills, Kenya." The Professional Geographer 69, no. 3: 383-395.