Testing the Potential Application of Simulated Multispectral Data in Discriminating Tree Species in Taita Hills

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Does topographic normalization of landsat images improve fractional tree cover mapping in tropical mountains?

Volume: Volume XL-7/W3Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methods have been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive to topographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The study area was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correction method. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) and tasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, we evaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and a regional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimum and maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightness and had very small effect on the other models. Cosine of the solar incidence angle (cos i) derived from SRTM DEM showed stronger relationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive models based on common vegetation indices are not sensitive to topographic effects.Peer reviewe

Relationship between carbon stocks and tree species diversity in a humid Guinean savanna landscape in northern Sierra Leone

Global sustainable development goals include reducing greenhouse gas emissions from land-use change and maintaining biodiversity. Many studies have examined carbon stocks and tree species diversity, but few have studied the humid Guinean savanna ecosystem. This study focuses on a humid savanna landscape in northern Sierra Leone, aiming to assess carbon stocks and tree species diversity and compare their relationships in different vegetation types. We surveyed 160 sample plots (0.1 ha) in the field for tree species, aboveground carbon (AGC) and soil organic carbon (SOC). In total, 90 tree species were identified in the field. Gmelina arborea, an exotic tree species common in the foothills of the Kuru Hills Forest Reserve, and Combretum glutinosum, Pterocarpus erinaceous and Terminaria glaucescens, which are typical savanna trees, were the most common species. At landscape level, the mean AGC stock was 29.4 Mg C ha(-1) (SD 21.3) and mean topsoil (0-20 cm depth) SOC stock was 42.2 Mg C ha(-1) (SD 20.6). Mean tree species richness and Shannon index per plot were 7 (SD 4) and 1.6 (SD 0.6), respectively. Forests and woodlands had significantly higher mean AGC and tree species richness than bushland, wooded grassland or cropland (p <0.05). In the forest and bushland, a small number of large diameter trees covered a large portion of the total AGC stocks. Furthermore, a moderate linear correlation was observed between AGC and tree species richness (r = 0.475, p <0.001) and AGC and Shannon index (r = 0.375, p <0.05). The correlation between AGC and SOC was weak (r = 0.17, p <0.05). The results emphasise the role of forests and woodlands and large diameter trees in retaining AGC stocks and tree species diversity in the savanna ecosystem.Peer reviewe

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

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Community and institutional perspectives on water management and environmental changes in the Taita Hills, Kenya

Water resources are declining in the Taita Hills, but what do local residents say about it? This report presents the outcome of the field research that was conducted in Kenya in 2013-2014, as part of the TAITAWATER project funded by the Academy of Finland. The study aims to understand the current status of water-related ecosystem services from the perspective of local people, to analyse the driving forces behind the declining water resources and to map the roles of different stakeholders and institutions involved in the management of water resources and related ecosystems. The study employs qualitative and participatory research methods, such as semistructured interviews, participatory mapping and timelines, targeting local water users, community groups and management institutions. The philosophy of this work is to enact inclusive science as a starting point for a more participatory dialogue between all stakeholders involved in natural resource management in the Taita Hills

Strong influence of trees outside forest in regulating microclimate of intensively modified Afromontane landscapes

Climate change is expected to have detrimental consequences on fragile ecosystems, threatening biodiversity, as well as food security of millions of people. Trees are likely to play a central role in mitigating these impacts. The microclimatic conditions below tree canopies usually differ substantially from the ambient macroclimate as vegetation can buffer temperature changes and variability. Trees cool down their surroundings through several biophysical mechanisms, and the cooling benefits occur also with trees outside forest. The aim of this study was to examine the effect of canopy cover on microclimate in an intensively modified Afromontane landscape in Taita Taveta, Kenya. We studied temperatures recorded by 19 microclimate sensors under different canopy covers, as well as land surface temperature (LST) estimated by Landsat 8 thermal infrared sensor. We combined the temperature records with high-resolution airborne laser scanning data to untangle the combined effects of topography and canopy cover on microclimate. We developed four multivariate regression models to study the joint impacts of topography and canopy cover on LST. The results showed a negative linear relationship between canopy cover percentage and daytime mean (R-2 = 0.65) and maximum (R-2 = 0.75) temperatures. Any increase in canopy cover contributed to reducing temperatures. The average difference between 0 % and 100 % canopy cover sites was 5.2 degrees C in mean temperatures and 10.2 degrees C in maximum temperatures. Canopy cover (CC) reduced LST on average by 0.05 degrees C per percent CC. The influence of canopy cover on microclimate was shown to vary strongly with elevation and ambient temperatures. These results demonstrate that trees have a substantial effect on microclimate, but the effect is dependent on macroclimate, highlighting the importance of maintaining tree cover particularly in warmer conditions. Hence, we demonstrate that trees outside forests can increase climate change resilience in fragmented landscapes, having strong potential for regulating regional and local temperatures.Peer reviewe

Clarifying the role of radiative mechanisms in the spatio-temporal changes of land surface temperature across the Horn of Africa

Vegetation plays an important role in the climate system. The extent to which vegetation impacts climate through its structure and function varies across space and time, and it is also affected by land cover changes. In areas with both multiple growing periods and significant land cover changes, such as the Horn of Africa, identifying vegetation influence on land surface temperature (LST) through radiative changes needs further investigation. In this study, we used a 13-year time series (2001−2013) of remotely sensed environmental data to estimate the contribution of radiative mechanism to LST change due to growing season albedo dynamics and land cover conversion. Our results revealed that in taller woody vegetation (forest and savanna), albedo increases during the growing period by up to 0.04 compared with the non-growing period, while it decreases in shorter vegetation (grassland and shrubland) by up to 0.03. The warming impact due to a decrease in albedo during the growing period in shorter vegetation is counteracted by a considerable increase in evapotranspiration, leading to net cooling. Analysis of land cover change impact on albedo showed a regional annual average instantaneous surface radiative forcing of −0.03 ± 0.02 W m−2. The land cover transitions from forest to cropland, and savanna to grassland, displayed the largest mean albedo increase across all seasons, causing an average instantaneous surface radiative forcing of −2.6 W m−2 and − 1.5 W m−2 and a decrease in mean LST of 0.12 K and 0.09 K, all in dry period (December, January, February), respectively. Despite the albedo cooling effect in these conversions, an average net warming of 1.3 K and 0.23 K was observed under the dominant influence of non-radiative mechanisms. These results show that the impact of radiative mechanism was small, highlighting the importance of non-radiative processes in understanding the climatic impacts of land cover changes, as well as in delineating effective mitigation strategies.Peer reviewe

Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy

We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms. A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.peerReviewe

Rainfall–vegetation interaction regulates temperature anomalies during extreme dry events in the Horn of Africa

Climate–vegetation interaction can be perturbed by human activities through deforestation and natural extreme climatic events. These perturbations can affect the energy and water balance, exacerbating heat stress associated with droughts. Such phenomena are particularly relevant in the Horn of Africa, given its economic and social vulnerability to environmental changes. In this paper, we used 16-year time series (2001–2016) of remotely sensed environmental data with the objective of 1) clarifying how rainfall–vegetation interaction affects land surface temperature (LST) seasonality across the Horn of Africa, and 2) evaluating how this interaction affects LST anomalies during forest loss and drought events. Our results showed that vegetation seasonality follows rainfall modality patterns in 81% of the region. On the other hand, seasonality of daytime LST was negatively related to vegetation greenness patterns across ecoregions, and rainfall modality. LST varied more strongly in grasslands and shrublands than over other vegetation classes. Comparison of LST before and after forest loss in three selected areas (two in Ethiopia and one in Kenya) revealed an annual average increase in LST of 0.7 °C, 1.8 °C, and 0.2 °C after climate variability correction, respectively. The average increase in LST was relatively high and consistent during dry months (1.5 °C, 3 °C, and 0.6 °C). As expected, the rainfall anomalies during droughts (2010/2011, 2015, and 2016) were positively correlated with vegetation greenness anomalies. Nonetheless, the degree with which vegetation cover is affected by extreme rainfall events has a strong influence in regulating the impact of droughts on temperature anomalies. This highlights the importance of vegetation resilience and land cover management in regulating the impact of extreme events.Peer reviewe

Land Cover Characterization in West Sudanian Savannas Using Seasonal Features from Annual Landsat Time Series

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