Climatic impacts of vegetation dynamics in Eastern Africa

The climate system responds to changes in the structure and physiology of vegetation. These changes can be induced by seasonal growing cycles, anthropogenic land cover changes (LCCs), and precipitation extremes. The extent to which vegetation changes impact the climate depends on the type of ecosystem, the season, and the intensity of perturbations from LCCs and precipitation extremes. Under the growing impacts of climate change and human modification of natural vegetation cover, understanding and monitoring the underlying biogeophysical processes through which vegetation affects the climate are central to the development and implementation of effective land use plans and mitigation measures. In Eastern Africa (EA) the vegetation is characterized by multiple growing cycles and affected by agricultural expansion as well as recurrent and severe drought events. Nonetheless, the degrees to which vegetation changes affect the surface energy budget and land surface temperature (LST) remain uncertain. Moreover, the relative contributions of various biogeophysical mechanisms to land surface warming or cooling across biomes, seasons, and scales (regional to local) are unknown. The objective of this thesis was to analyze and quantify the climatic impacts of land changes induced by vegetation seasonal dynamics, agricultural expansion, and precipitation extremes in EA. In particular, this thesis investigated these impacts across biomes and spatio-temporal scales. To address this objective, satellite observation and meteorological data were utilized along with empirical models, observation-based metrics, and statistical methods. The results showed that rainfall–vegetation interaction had a strong impact on LST seasonality across ecoregions and rainfall modality patterns. Furthermore, seasonal LST dynamics were largely controlled by evapotranspiration (ET) changes that offset the albedo impact on the surface radiation balance. Forest loss disturbed the LST dynamics and increased local LST consistently and notably during dry seasons, whereas during the wet season its impact was limited because of strong rainfall–vegetation interaction. Moreover, drought events affected LST anomalies; however, the impact of droughts on temperature anomalies was highly regulated by vegetation greening. In addition, the conversion of forest to cropland generated the highest net warming (1.3 K) compared with other conversion types (savanna, shrubland, grassland, and cropland). Warming from the reduction of ET and surface roughness was up to ~10 times stronger than the cooling effect from albedo increases (−0.12 K). Furthermore, large scale analysis revealed a comparable warming magnitude during bushland-to-cropland conversion associated with the dominant impact of latent heat (LE) flux reduction, which outweighed the albedo effect by up to ~5 times. A similar mechanism dominated the surface feedback during precipitation extremes; where LE flux anomalies dominated the energy exchange causing the strongest LST anomaly in grassland, followed by savanna. By contrast, the impact was negligible in forest ecosystems. In conclusion, the results of this thesis clarify the mechanics and magnitude of the impacts of vegetation dynamics on LST across biomes and seasons. These results are crucial for guiding land use planning and climate change mitigation efforts in EA. The methods and results of this thesis can assist in the development of ecosystem-based mitigation strategies that are tailored to EA biomes. Moreover, they can be used for assessing the performance of climate models and observation-based global scale studies that focus on the biogeophysical impacts of LCCs. Keywords: LST seasonality; Land cover change; Bushland (Acacia-Commiphora); Biophysical effects; Precipitation extremes; Satellite observation.Ilmastojärjestelmä reagoi kasvillisuuden rakenteen ja fysiologian muutoksiin. Muutokset voivat johtua kasvukauden vaiheesta, ihmistoiminnan vaikutuksesta maanpeitteeseen ja sään ääri-ilmiöistä. Se missä määrin kasvillisuuden muutokset vaikuttavat ilmastoon riippuu ekosysteemistä ja vuodenajasta sekä maanpeitemuutosten ja sään ääri-ilmiöiden voimakkuudesta. Ilmastonmuutoksen ja maanpeitteen muokkaamisen vaikutusten voimistuessa on keskeistä ymmärtää ja seurata biogeofysikaalisia prosesseja, joiden kautta kasvillisuus vaikuttaa ilmastoon. Tällä tiedolla on keskeinen rooli tehokkaiden maankäyttösuunnitelmien kehittämisessä ja toteuttamisessa sekä ilmastonmuutoksen hillinnässä. Itä-Afrikassa kasvillisuudella on ominaisesti useita kasvukausia ja siihen vaikuttavat maatalouden laajentuminen sekä toistuvat ja vakavat kuivuusjaksot. Siitä huolimatta kasvillisuuden muutosten vaikutus energiataseeseen ja maanpinnan lämpötilaan on edelleen epävarmaa. Lisäksi eri biogeofysikaalisten mekanismien suhteellista vaikutusta maanpinnan lämpenemiseen tai jäähtymiseen eri biomien, vuodenaikojen ja mittakaavojen (alueellinen ja paikallinen) välillä ei tunneta. Tämän tutkielman tavoitteena oli analysoida ja kvantifioida kasvillisuuden vuodenaikaisvaihtelun, maatalouden laajentumisen ja sademäärän ääri-ilmiöiden aiheuttamien muutosten ilmastovaikutuksia Itä-Afrikassa. Erityisesti tutkielmassa tarkasteltiin vaikutuksia eri biomien ja mittakaavojen välillä. Tutkielmassa hyödynnettiin satelliittihavaintoja ja meteorologisia tietoja sekä empiirisiä malleja, havaintopohjaisia indeksejä ja tilastollisia menetelmiä. Tulokset osoittivat, että sademäärän ja kasvillisuuden vuorovaikutuksella oli voimakas vaikutus maanpinnan lämpötilan vuodenaikaisvaihteluun kasvillisuustyyppien ja sademoodien välillä. Maanpinnan lämpötilaa säätelivät suurelta osin evapotranspiraation muutokset, jotka kompensoivat albedon vaikutuksia pinnan säteilytasapainoon. Metsän häviäminen häiritsi maanpinnan lämpötilan dynamiikkaa ja lisäsi sitä paikallisesti, etenkin kuivina vuodenaikoina, kun taas sadekauden aikana sen vaikutus oli vähäinen sateen ja kasvillisuuden voimakkaan vuorovaikutuksen vuoksi. Lisäksi kuivuus vaikutti lämpötilan poikkeavuuksiin; kuivuuden vaikutusta sääteli kuitenkin voimakkaasti kasvillisuuden vihertyminen. Metsän muuntaminen viljelysmaaksi aiheutti suurimman nettolämmityksen (1.3 K) verrattuna muihin muutostyyppeihin (savanni, pensaikko, ruohostomaat ja viljelymaat). Evapotranspiraation vähenemisestä ja pinnan epätasaisuudesta aiheutuva lämpeneminen oli jopa noin 10 kertaa voimakkaampi kuin albedon jäähdytysvaikutus (−0.12 K). Lisäksi pensaikon muuntaminen viljelysmaaksi aiheutti vastaavan lämpenemisen. Lämpeneminen liittyi latentin lämpövuon merkityksen vähentymiseen, joka ylitti albedovaikutuksen jopa noin viisinkertaisesti. Samanlainen mekanismi hallitsi sademäärän ääripäiden aikana, jolloin latentin lämpövuon poikkeavuudet hallitsivat energianvaihtoa aiheuttaen voimakkaimman maanpinnan lämpötilan poikkeavuuden ruohostomailla ja savanneilla. Sitä vastoin metsissä vaikutus oli vähäinen. Yhteenvetona voidaan todeta, että tutkielman tulokset selventävät kasvillisuuden dynamiikan vaikutusten mekanismeja ja suuruutta maanpinnan lämpötilaan biomien ja vuodenaikojen välillä. Tulokset ovat tärkeitä Itä-Afrikan maankäytön suunnittelun ja ilmastonmuutoksen hillitsemistoimien ohjaamisessa. Tutkielman menetelmät ja tulokset voivat auttaa kehittämään Itä-Afrikan biomeille räätälöityjä ekosysteemipohjaisia lieventämisstrategioita. Lisäksi niitä voidaan käyttää arvioimaan ilmastomalleja ja havaintopohjaisia globaalin mittakaavan tutkimuksia, jotka keskittyvät maanpeitemuutosten biogeofysikaalisiin vaikutuksiin. Avainsanat: Maanpinnan lämpötilan vuodenaikaisvaihtelu; Maanpeitteen muutos; Pensaikko (AcaciaCommiphora); Biofysikaaliset vaikutukset; Sademäärä; Satelliittikaukokartoitus

Factors Affecting Profitability: An Emprical Study on Ethiopian Banking Industry

The main purpose of this study was to examine factors affecting profitabilityEthiopian banking industry. The study adopted quantitative research approach and the statistical tool was used to estimate the profitability, which was measured by returnon asset as a function of balance sheet, industry specific and macroeconomic explanatoryvariables. The finding of the study showed that loan and advance, current deposit, otherliabilities and gross domestic product have statistically significant and positiverelationship with banks’ profitability. On the other hand, variables like fixed deposit, market concentration have a negative and statistically significant relationship withbanks’ profitability. However, the relationship of deposit with other banks, sum ofinvestment, saving deposit and inflation is found to be statistically insignificant. As aresult, the study recommended that Ethiopian Banking Industry must focus on increasingpublic awareness to mobilize more savings this will enhance their performance inprovision of loans and advance to customers. Finally, Ethiopian Banking Industryshouldnot only be concerned about internal structures and policies, but they must consider boththe internal environment and the macroeconomic environment together in fashioning out strategies to improve their profitability

Image-to-Image Training for Spatially Seamless Air Temperature Estimation with Satellite Images and Station Data

Air temperature at approximately 2 m above the ground (T-a) is one of the most important environmental and biophysical parameters to study various earth surface processes. T-a measured from meteorological stations is inadequate to study its spatio-temporal patterns since the stations are unevenly and sparsely distributed. Satellite-derived land surface temperature (LST) provides global coverage, and is generally utilized to estimate T-a due to the close relationship between LST and T-a. However, LST products are sensitive to cloud contamination, resulting in missing values in LST and leading to the estimated T-a being spatially incomplete. To solve the missing data problem, we propose a deep learning method to estimate spatially seamless T-a from LST that contains missing values. Experimental results on 5-year data of mainland China illustrate that the image-to-image training strategy alleviates the missing data problem and fills the gaps in LST implicitly. Plus, the strong linear relationships between observed daily mean T-a (T-mean), daily minimum T-a (T-min), and daily maximum T-a (T-max) make the estimation of T-mean, T-min, and T(max )simultaneously possible. For mainland China, the proposed method achieves results with R-2 of 0.962, 0.953, 0.944, mean absolute error (MAE) of 1.793 degrees C, 2.143 degrees C, and 2.125 degrees C, and root-mean-square error (RMSE) of 2.376 degrees C, 2.808 degrees C, and 2.823 degrees C for T-mean, T-min, and T-max, respectively. OPeer reviewe

Land Surface Temperature Trend and Its Drivers in East Africa

Land surface temperature (LST) is affected by surface-atmosphere interaction. Yet, the degree to which surface and atmospheric factors impact the magnitude of LST trend is not well established. Here, we used surface energy balance, boosted regression tree model, and satellite observation and reanalysis data to unravel the effects of surface factors (albedo, sensible heat, latent heat, and ground heat) as well as incoming radiation (shortwave and longwave) on LST trends in East Africa (EA). Our result showed that 11% of EA was affected by significant (p <0.05) daytime annual LST trends, which exhibited both cooling of -0.19 K year(-1) (mainly in South Sudan and Sudan) and warming of 0.22 K year(-1) (mainly in Somalia and Kenya). The nighttime LST trends affected a large part of EA (31%) and were dominated by significant warming trend (0.06 K year(-1)). Influenced by contrasting daytime and nighttime LST trends, the diurnal LST range reduced in 15% of EA. The modeling result showed that latent heat flux (32%), incoming longwave radiation (30%), and shortwave radiation (23%) were stronger in explaining daytime LST trend. The effects of surface factors were stronger in both cooling and warming trends, whereas atmospheric factors had stronger control only on surface cooling trends. These results indicate the differential control of surface and atmospheric factors on warming and cooling trends, highlighting the importance of considering both factors for accurate evaluation of the LST trends in the future.Peer reviewe

Impact of rainfall extremes on energy exchange and surface temperature anomalies across biomes in the Horn of Africa

Precipitation extremes have a strong influence on the exchange of energy and water between the land surface and the atmosphere. Although the Horn of Africa has faced recurrent drought and flood events in recent decades, it is still unclear how these events impact energy exchange and surface temperature across different ecosystems. Here, we analyzed the impact of precipitation extremes on spectral albedo (total shortwave, visible, and near-infrared (NIR) broadband albedos), energy balance, and surface temperature in four natural vegetation types: forest, savanna, grassland, and shrubland. We used remotely sensed observations of surface biophysical properties and climate from 2001 to 2016. Our results showed that, in forests and savannas, precipitation extremes led to divergent spectral changes in visible and NIR albedos, which cancelled each other limiting shortwave albedo changes. An exception to this pattern was observed in shrublands and grasslands, where both visible and NIR albedo increased during drought events. Given that shrublands and grasslands occupy a large fraction of the Horn of Africa (52%), our results unveil the importance of these ecosystems in driving the magnitude of shortwave radiative forcing in the region. The average regional shortwave radiative forcing during drought events (-0.64 W m(-2), SD 0.11) was around twice that of the extreme wet events (0.33 W m(-2), SD 0.09). Such shortwave forcing, however, was too small to influence the surface-atmosphere coupling. In contrast, the surface feedback through turbulent flux changes was strong across vegetation types and had a significant (P <0.05) impact on the surface temperature and net radiation anomalies, except in forests. The strongest energy exchange and surface temperature anomalies were observed over grassland and the smallest over forest, which was shown to be resilient to precipitation extremes. These results suggest that land management activities that support forest preservation, afforestation, and reforestation can help to mitigate the impact of drought through their role in modulating energy fluxes and surface temperature anomalies in the region.Peer reviewe

Assessment of Rangeland Degradation in New Mexico Using Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND)

Rangelands provide significant socioeconomic and environmental benefits to humans. However, climate variability and anthropogenic drivers can negatively impact rangeland productivity. The main goal of this study was to investigate structural and productivity changes in rangeland ecosystems in New Mexico (NM), in the southwestern United States of America during the 1984-2015 period. This goal was achieved by applying the time series segmented residual trend analysis (TSS-RESTREND) method, using datasets of the normalized difference vegetation index (NDVI) from the Global Inventory Modeling and Mapping Studies and precipitation from Parameter elevation Regressions on Independent Slopes Model (PRISM), and developing an assessment framework. The results indicated that about 17.6% and 12.8% of NM experienced a decrease and an increase in productivity, respectively. More than half of the state (55.6%) had insignificant change productivity, 10.8% was classified as indeterminant, and 3.2% was considered as agriculture. A decrease in productivity was observed in 2.2%, 4.5%, and 1.7% of NM's grassland, shrubland, and ever green forest land cover classes, respectively. Significant decrease in productivity was observed in the northeastern and southeastern quadrants of NM while significant increase was observed in northwestern, southwestern, and a small portion of the southeastern quadrants. The timing of detected breakpoints coincided with some of NM's drought events as indicated by the self-calibrated Palmar Drought Severity Index as their number increased since 2000s following a similar increase in drought severity. Some breakpoints were concurrent with some fire events. The combination of these two types of disturbances can partly explain the emergence of breakpoints with degradation in productivity. Using the breakpoint assessment framework developed in this study, the observed degradation based on the TSS-RESTREND showed only 55% agreement with the Rangeland Productivity Monitoring Service (RPMS) data. There was an agreement between the TSS-RESTREND and RPMS on the occurrence of significant degradation in productivity over the grasslands and shrublands within the Arizona/NM Tablelands and in the Chihuahua Desert ecoregions, respectively. This assessment of NM's vegetation productivity is critical to support the decision-making process for rangeland management; address challenges related to the sustainability of forage supply and livestock production; conserve the biodiversity of rangelands ecosystems; and increase their resilience. Future analysis should consider the effects of rising temperatures and drought on rangeland degradation and productivity.Peer reviewe

Climatic impacts of bushland to cropland conversion in Eastern Africa

Bushlands (Acacia-Commiphora) constitute the largest and one of the most threatened ecosystems in East Africa. Although several studies have investigated the climatic impacts of land changes on local and global climate, the main focus has been on forest loss and the impacts of bushland clearing thus remain poorly understood. Measuring the impacts of bushland loss on local climate is challenging given that changes often occur at fragmented and small patches. Here, we apply high-resolution satellite imagery and land surface flux modeling approaches to unveil the impacts of bushland clearing on surface biophysical properties and its associated effects on surface energy balance and land surface temperature. Our results show that bushland clearing leads to an average reduction in evapotranspiration of 0.4 mm day(-1). The changes in surface biophysical properties affected the surface energy balance components with different magnitude. The reduction in latent heat flux was stronger than other surface energy fluxes and resulted in an average net increase in daytime land surface temperature (LST) of up to 1.75 K. These results demonstrate the important impact of bushland-to-cropland conversion on the local climate, as they reveal increases in LST of a magnitude comparable to those caused by forest loss. This finding highlights the necessity of bushland conservation for regulating the land surface temperature in East Africa and, at the same time, warns of the climatic impacts of clearing bushlands for agriculture. (c) 2020 The Authors. Published by Elsevier B.V.Peer reviewe

Improved detection of abrupt change in vegetation reveals dominant fractional woody cover decline in Eastern Africa

While cropland expansion and demand for woodfuel exert increasing pressure on woody vegetation in East Africa, climate change is inducing woody cover gain. It is however unclear if these contrasting patterns have led to net fractional woody cover loss or gain. Here we used non-parametric fractional woody cover (WC) predictions and breakpoint detection algorithms driven by satellite observations (Landsat and MODIS) and airborne laser scanning to unveil the net fractional WC change during 2001-2019 over Ethiopia and Kenya. Our results show that total WC loss was 4-times higher than total gain, leading to net loss. The contribution of abrupt WC loss (59%) was higher than gradual losses (41%). We estimated an annual WC loss rate of up to 5% locally, with cropland expansion contributing to 57% of the total loss in the region. Major hotspots of WC loss and degradation corridors were identified inside as well as surrounding protected areas, in agricultural lands located close to agropastoral and pastoral livelihood zones, and near highly populated areas. As the dominant vegetation type in the region, Acacia-Commiphora bushlands and thickets ecosystem was the most threatened, accounting 69% of the total WC loss, followed by montane forest (12%). Although highly outweighed by loss, relatively more gain was observed in woody savanna than in other ecosystems. These results reveal the marked impact of human activities on woody vegetation and highlight the importance of protecting endangered ecosystems from increased human activities for mitigating impacts on climate and supporting sustainable ecosystem service provision in East Africa.Peer reviewe

Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers

Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers