Metoder for estimering av sentrale skoglige egenskaper basert på fjernmålte data til støtte for REDD+ måling, rapportering og verifisering (MRV)

There is limited information about the status of forest resources in Ethiopia. The available reports based on field sampling are inconsistent and lack precision due to limited sample sizes. Remotely sensed data, which cover a larger area, could be used to supplement the field sampling to improve precision. Therefore, this thesis tried to assess the improvement in estimation efficiency by using different data and methods for assessing forest area, canopy cover and aboveground biomass variables. In paper-I, suitability of unmanned aerial systems for forest monitoring in the Ethiopian context was explored using literature review on existing experiences in different fields across different geographic regions. The review results indicated that unmanned aerial systems have huge potential to contribute to forest assessment in Ethiopia due to the growing technological availability, which offers choices based on the intended purpose and costs. Global experiences indicated the need for preparing operational guidelines and the legal framework within which the system works. In paper-II, manual interpretation of satellite images was used to evaluate the suitability of PlanetScope, RapidEye and Sentinel-2 images for forest area and canopy cover assessment. The results indicated that RapidEye and PlanetScope images, which have relatively high resolution, had similar patterns of estimates for both variables while estimates using the Sentinel-2 images with coarser resolution were dependent on the forest density. Evaluation of sensitivity to sample size showed that the high-resolution images were less sensitive to change in sample size and therefore best suited for studying forest area and CC using manual image interpretation. In paper-III, model-assisted estimation technique was applied to compare the contribution of Landsat-8, Sentinel-2 and PlanetScope image-derived variables for aboveground biomass estimation in a dry Afromontane forest in Ethiopia. Simple models were developed for the variables from each image type and their estimation efficiencies compared. The results indicated that the model-assisted estimates were more precise than that of the field survey. Lastly, in paper-IV, a photo processing method was developed to estimate forest canopy cover from digital photos. The use of Otsu thresholding for photo segmentation offered objective assessment of canopy fractions. The results revealed that the canopy fraction of the central distortion-free 1-3% of the full photo size with thresholds of 10 and 20% estimated canopy cover more precisely than the point-based method. Therefore, the use of a narrow-angle camera for canopy photography is recommended.Informasjon om status for skogressurser i Etiopia er begrenset. De tilgjengelige rapportene basert på feltprøver er inkonsekvente og mangler presisjon på grunn av begrensede prøvestørrelser. Fjernmålingsdata som dekker et større område kan brukes til å supplere feltprøvetakingen for å forbedre presisjonen. Jeg har derfor prøvd i dette arbeidet å vurdere forbedringen i estimeringseffektivitet ved å bruke ulike data og metoder for å vurdere skogareal, trekronedekke og biomasse over jord. I artikkel-I ble egnetheten til ubemannede luftfartøy (droner) for skogovervåking i etiopisk sammenheng undersøkt ved hjelp av litteraturgjennomgang om eksisterende erfaringer innen forskjellige felt på tvers av forskjellige geografiske regioner. Gjennomgangsresultatene indikerte at ubemannede luftfartøy har et enormt potensiale til å bidra til skogvurdering i Etiopia på grunn av den økende teknologiske tilgjengeligheten, som tilbyr valg basert på det tiltenkte formålet og kostnadene. Globale erfaringer indikerte behovet for å utarbeide operasjonelle retningslinjer og det juridiske rammeverket systemet fungerer innenfor. I artikkel II ble manuell tolkning av satellittbilder brukt for å evaluere egnetheten til PlanetScope, RapidEye og Sentinel-2 bilder for skogareal og trekronedekke. Resultatene indikerte at RapidEye- og PlanetScope-bilder, som har relativt høy oppløsning, ga lignende estimat for begge variable mens estimatene basert på Sentinel-2-bildene med grovere oppløsning var avhengige av skogtettheten. Evaluering av følsomhet for utvalgsstørrelse viste at bilder med høy oppløsning var mindre følsomme for endring i utvalgsstørrelse og derfor best egnet for å studere skogsområde og trekronedekke ut ifra manuell bildetolkning. I artikkel-III ble modellassistert estimeringsteknikk anvendt for å sammenligne bidraget fra Landsat-8, Sentinel-2 og PlanetScope bildeavledede variabler for estimering av biomasse over bakkenivå i en tørr Afromontane-skog i Etiopia. Enkle modeller ble utviklet for variablene fra hver bildetype og deres estimerte effektivitet sammenlignet. Resultatene indikerte at de modellassisterte estimatene var mer presise enn feltundersøkelsen. Til slutt ble det i artikkel-IV utviklet en bildebehandlingsmetode for å estimere trekronedekke fra digitale bilder og sammenlignet med punktbaserte data. Bruk av Otsu-terskelverdi for fotosegmentering ga objektiv vurdering av trekroneandel. Resultatene avslørte at bestemmelsen av kronedekke fra den sentrale 1-3% av full fotostørrelse, med terskler på 10% og 20% estimert trekroneandel, var mer presis enn den punktbaserte metoden. Derfor anbefales bruk av et smalvinkelkamera til fotografering av trekronedekke

Application of unmanned aerial vehicles in earth resources monitoring: Focus on evaluating potentials for forest monitoring in Ethiopia

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Carbon Sequestration Potentials of Different Land Uses in Wondo Genet Sub-Catchment, Southern Ethiopia

Forests play an important role in combating the challenges posed by changing climate through sequestering carbon in their living biomasses and the soil. Tropical forests, which harbour a large number of species, are anticipated to play a great role in this regard due to the favourable growing environments. However, there is limited knowledge of the variability in carbon stock among land use types and its relationship with biodiversity. Therefore, this study assessed the variability in storing the different carbon pools among natural forest, woodland and khat plantation land use types. It also explored the relationship between biodiversity and carbon storage in the different carbon pools. Plant inventory and sample collection were undertaken following standard methods. In addition, soil samples were taken at three depth profile classes of 0–30 cm (top layer), 30–60 cm (middle layer) and 60–100 cm (bottom layer). Results of the study revealed that there was no statistically significant relationship between biodiversity and total biomass carbon, soil organic carbon or total carbon stock at a 95% level of confidence. The results indicated that the natural forest had the highest plant biomass (456.93 Mg ha−1) followed by woodland (19.78 Mg ha−1) and khat plantation (2.46 Mg ha−1). Consequently, the total carbon stock estimate of the natural forest (366.47 Mg ha−1) was significantly larger than that of the woodland (141.85 Mg ha−1) and khat plantation (125.86 Mg ha−1). The variation in total carbon stock among land use types arises from the variation in the total biomass carbon stock. The study results also revealed that soil organic carbon stock decreased with soil depth in all the land-use types. The findings of this study have implication of improving topsoil management in monoculture crops such as khat plantation and conserving natural forests for enhancing carbon sequestration potentials

Carbon Sequestration Potentials of Different Land Uses in Wondo Genet Sub-Catchment, Southern Ethiopia

Forests play an important role in combating the challenges posed by changing climate through sequestering carbon in their living biomasses and the soil. Tropical forests, which harbour a large number of species, are anticipated to play a great role in this regard due to the favourable growing environments. However, there is limited knowledge of the variability in carbon stock among land use types and its relationship with biodiversity. Therefore, this study assessed the variability in storing the different carbon pools among natural forest, woodland and khat plantation land use types. It also explored the relationship between biodiversity and carbon storage in the different carbon pools. Plant inventory and sample collection were undertaken following standard methods. In addition, soil samples were taken at three depth profile classes of 0–30 cm (top layer), 30–60 cm (middle layer) and 60–100 cm (bottom layer). Results of the study revealed that there was no statistically significant relationship between biodiversity and total biomass carbon, soil organic carbon or total carbon stock at a 95% level of confidence. The results indicated that the natural forest had the highest plant biomass (456.93 Mg ha−1) followed by woodland (19.78 Mg ha−1) and khat plantation (2.46 Mg ha−1). Consequently, the total carbon stock estimate of the natural forest (366.47 Mg ha−1) was significantly larger than that of the woodland (141.85 Mg ha−1) and khat plantation (125.86 Mg ha−1). The variation in total carbon stock among land use types arises from the variation in the total biomass carbon stock. The study results also revealed that soil organic carbon stock decreased with soil depth in all the land-use types. The findings of this study have implication of improving topsoil management in monoculture crops such as khat plantation and conserving natural forests for enhancing carbon sequestration potentials

Woodland cover change in the Central Rift Valley of Ethiopia

Woodlands, which are part of the landscape and an important source of livelihood for smallholders living in the environmentally vulnerable Central Rift Valley (CRV) of Ethiopia, are experiencing rapid changes. Detecting and monitoring these changes is essential for better management of the resources and the benefits they provide to people. The study used a combination of both quantitative and qualitative methods to analyze the extent and pattern of woodland cover changes from 1973 to 2013. Pixel-based supervised image classification with maximum likelihood classification algorithm was used for land cover classification and change detection analyses. Local peoples’ perceptions were used to explain the patterns of change and their possible reasons. Four major land cover classes were identified, with an overall accuracy of 88.3% and a Kappa statistic of 0.81 for the latest image. The analysis revealed a major land cover reversal, where woodland (92.4%) was the dominant land cover in 1973, while it was agriculture (44.7%) in 2013. A rapid reduction in woodland (54%) and forest (99%) covers took place between 1973 and 2013, with the majority of the conversions being made during the government transition period (1973 to 1986). Agriculture (3878%) and grassland (11,117%) increased tremendously during the 40-year period at the expense of woodlands and forests. Bare land increased moderately (40%). Thus, woodlands are under increasing pressure from other land uses, particularly agriculture, and declining faster. If the current trends of land cover change remain unabated it is likely that woodlands will disappear from the landscape of the area in the near future. Therefore, better forest policy and implementation tools, as well as better woodland management strategies and practices, need to be in place for woodlands to continue providing vital ecosystem goods and services to the local people, as well as to the environment

Use of remotely sensed data to enhance estimation of aboveground biomass for the dry afromontane forest in south-central Ethiopia

Periodic assessment of forest aboveground biomass (AGB) is essential to regulate the impacts of the changing climate. However, AGB estimation using field-based sample survey (FBSS) has limited precision due to cost and accessibility constraints. Fortunately, remote sensing technologies assist to improve AGB estimation precisions. Thus, this study assessed the role of remotely sensed (RS) data in improving the precision of AGB estimation in an Afromontane forest in south-central Ethiopia. The research objectives were to identify RS variables that are useful for estimating AGB and evaluate the extent of improvement in the precision of the remote sensing-assisted AGB estimates beyond the precision of a pure FBSS. Reference AGB data for model calibration and estimation were collected from 111 systematically distributed circular sample plots (SPs) of 1000 m2 area. Independent variables were derived from Landsat-8, Sentinel-2 and PlanetScope images acquired in January 2019. The area-weighted mean and standard deviation of the spectral reflectance, spectral index and texture (only for PlanetScope) variables were extracted for each SP. A maximum of two independent variables from each image type was fitted to a generalized linear model for AGB estimation using model-assisted estimators. The results of this study revealed that the Landsat-8 model with the predictor variable of shortwave infrared band reflectance and the PlanetScope model with the predictor variable of green band reflectance had estimation efficiency of 1.40 and 1.37, respectively. Similarly, the Sentinel-2 model, which had predictor variables of shortwave infrared reflectance and standard deviation of green leaf index, improved AGB estimation with the relative efficiency of 1.68. Utilizing freely available Sentinel-2 data seems to enhance the AGB estimation efficiency and reduce cost and extensive fieldwork in inaccessible areas

Relationship of Attributes of Soil and Topography with Land Cover Change in the Rift Valley Basin of Ethiopia

Understanding the spatiotemporal trend of land cover (LC) change and its impact on humans and the environment is essential for decision making and ecosystem conservation. Land degradation generally accelerates overland flow, reducing soil moisture and base flow recharge, and increasing sediment erosion and transport, thereby affecting the entire basin hydrology. In this study, we analyzed watershed-scale processes in the study area, where agriculture and natural shrub land are the dominant LCs. The objective of this study was to assess the time series and spatial patterns of LCC using remotely-sensed data from 1973 to 2018, for which we used six snapshots of satellite images. The LC distribution in relation to watershed characteristics such as topography and soils was also evaluated. For LCC detection analysis, we used Landsat datasets accessed from the United States Geological Survey (USGS) archive, which were processed using remote sensing and Geographic Information System (GIS) techniques. Using these data, four major LC types were identified. The findings of an LC with an overall accuracy above 90% indicates that the area experienced an increase in agricultural LC at the expense of other LC types such as bushland, grazing land, and mixed forest, which attests to the semi-continuous nature of deforestation between 1973 and 2018. In 1973, agricultural land covered only 10% of the watershed, which later expanded to 48.4% in 2018. Bush, forest, and grazing land types, which accounted for 59.7%, 16.7%, and 13.5% of the watershed in 1973, were reduced to 45.2%, 2.3%, and 4.1%, respectively in 2018. As a result, portions of land areas, which had once been covered by pasture, bush, and forest in 1973, were identified as mixed agricultural systems in 2018. Moreover, spatial variability and distribution in LCC is significantly affected by soil type, fertility, and slope. The findings showed the need to reconsider land-use decision tradeoffs between social, economic, and environmental demands

Simulating Spatiotemporal Changes in Land Use and Land Cover of the North-Western Himalayan Region Using Markov Chain Analysis

Spatial variabilities and drivers of land use and land cover (LULC) change over time and are crucial for determining the region’s economic viability and ecological functionality. The North-Western Himalayan (NWH) regions have witnessed drastic changes in LULC over the last 50 years, as a result of which their ecological diversity has been under significant threat. There is a need to understand how LULC change has taken place so that appropriate conservation measures can be taken well in advance to understand the implications of the current trends of changing LULC. This study has been carried out in the Baramulla district of the North-Western Himalayas to assess its current and future LULC changes and determine the drivers responsible for future policy decisions. Using Landsat 2000, 2010, and 2020 satellite imagery, we performed LULC classification of the study area using the maximum likelihood supervised classification. The land-use transition matrix, Markov chain model, and CA-Markov model were used to determine the spatial patterns and temporal variation of LULC for 2030. The CA-Markov model was first used to predict the land cover for 2020, which was then verified by the actual land cover of 2020 (Kappa coefficient of 0.81) for the model’s validation. After calibration and validation of the model, LULC was predicted for the year 2030. Between the years 2000 and 2020, it was found that horticulture, urbanization, and built-up areas increased, while snow cover, forest cover, agricultural land, and water bodies all decreased. The significant drivers of LULC changes were economic compulsions, climate variability, and increased human population. The analysis finding of the study highlighted that technical, financial, policy, or legislative initiatives are required to restore fragile NWH regions experiencing comparable consequences

Strengthening climate change adaptation capacity in Africa- case studies from six major African cities and policy implications

Africa is one of the most vulnerable regions in respect of climate change. As the African continent struggles to adapt to climate change, a variety of measures are being pursued to alleviate the resultant pressures on people, properties and their livelihoods in several African cities. Collectively, they show that climate change adaptation in Africa is not as hopeless as widely claimed, and that there are some promising prospects. The literature shows a deficiency on studies which examine the extent to which climate change adaptation is being pursued in African cities. This paper addresses this need, and outlines some of the most important climate threats (e.g. increasing temperatures, droughts, sea level rise, sea and river flooding) and synergic non-climate factors, as well as recent progress made in respect of implementing climate change adaptation in African cities. Rather than adopt a general description of trends, this research focuses on concrete case studies from six major cities across the central, western, and eastern regions of the African continent (Douala, Lagos City, Dar-es-Salaam, Accra, Addis Ababa and Mombasa). The vulnerability and adaptive capacity status of the studied cities are discussed. Difficulties and challenges encountered in implementing adaptation policies in these areas are also highlighted. Furthermore, some successful examples of climate change adaptation initiatives in the surveyed cities are provided. Finally, the paper outlines some of the policy measures which can be implemented towards strengthening the capacity of African cities to adapt to a changing climate