Diress Tsegaye Alemu
Forsker
(+47) 930 53 761
diress.alemu@nibio.no
Sted
Ås - Bygg O43
Besøksadresse
Oluf Thesens vei 43, 1433 Ås (Varelevering: Elizabeth Stephansens vei 21)
Vedlegg
CVBiografi
Jeg har en doktorgrad i naturressursforvaltning (2010) ved Norges miljø- og biovitenskapelige universitet (NMBU), Norge. Jeg har over 20 års internasjonal forsknings- og undervisningserfaring innen økologi og forvaltning av naturressurser. Tidligere har jeg jobbet som forsker ved Universitetet i Oslo og NMBU fra juli 2010- februar 2023. Forskningen min fokuserer på mønstre og prosesser i landbruks- og naturlandskap, klimaendringer, analyse arealbruk/arealdekke, bevaring av biologisk mangfold og viltforvaltning. Svært god og lang erfaring i å organisere og analysere økologiske data og presentere resultater på høyt nivå. Jeg har publisert mer enn 50 artikler og tekniske rapporter, hvorav 42 er fagfellevurderte vitenskapelige tidsskriftartikler. Jeg har veldig gode ferdigheter i modellering/statistisk dataanalysebakgrunn med meget gode ferdigheter innen R/Rstudio programmering, ArcGIS og QGIS programvarer. Jeg har også prosjektledelse med tverrfaglige flerkulturelle prosjektgrupper.
Forfattere
Misganu Debella-Gilo Diress Tsegaye Alemu Richard Adade Deo D. Shirima Belachew Gizachew ZelekeSammendrag
Remote sensing is essential for mapping mangrove forest ecosystems, yet accuracy and spatial resolution remain limited, particularly in Africa, where mangroves are fragmented, reference data and cloud-free images are scarce. The state-of-the-art of mangrove forest mapping relies on native-resolution imagery using machine learning. This study evaluates deep learning methods using aggregated multi-date satellite imagery to improve mangrove mapping and national-scale area estimation. Super-resolution and semantic segmentation techniques were applied to Sentinel-2 (S2) and Sentinel-1 (S1) images, with training data derived from field observations and high-resolution drone and satellite imagery. Coastal regions of Tanzania and Ghana were used as representative study areas for eastern and western Africa, respectively. Results show strong classification performance, with S2 imagery alone achieving an Intersection over Union (IoU) of 85.8%. Adding S1 provided modest gains (IoU 86.3%), while super-resolution significantly improved the accuracy (IoU 88.5%). The combined super-resolved S2 and S1 dataset yielded the highest performance (IoU 89.2%). Wall-to-wall mapping and statistically rigorous validation and area estimation produced accurate results with quantified uncertainty. Tanzania showed high consistency (F1 = 96%; 1207 km2, 95% CI [1151.7, 1263]), while Ghana exhibited slightly higher uncertainty (F1 = 92%; 92.6 km2, 95% CI [87.5, 97.7]). Integrating deep learning with multi-date, multi-sensor fusion and super-resolution enables accurate mangrove mapping and area estimation for improved coastal management and restoration.
Forfattere
Desalegn Chala Diress Tsegaye Alemu Habtamu Alem Belachew Asalf Tadesse Melesse Eshetu_Moges Nega Tassie Abate Ayalew Wondie Aklilu Tilahun Tadesse Abebayehu Aticho Alemu Gonsamo Lanhui Wang Erick Lundgren Jeffrey Kerby Jens Christian SvenningSammendrag
Abstract Water hyacinth is among the world’s most damaging aquatic invasive plants, forming dense mats that disrupt ecosystem functioning, fisheries, navigation, and livelihoods across tropical and subtropical freshwater systems. Its rapid spread is driven by clonal propagation, short life cycles, and prolific seed production, particularly under nutrient-enriched conditions. Although mechanical, chemical, and biological control methods are widely applied, their long-term effectiveness remains uncertain when underlying eutrophication persists. Here, we present a large-scale, one-time water hyacinth removal campaign in Lake Tana, Ethiopia’s largest lake and a UNESCO Biosphere Reserve, as a representative nutrient-rich tropical freshwater system. Using high-resolution satellite imagery, we quantified coverage one month before removal, one month after removal, and one year later. We integrated SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis with a socio-ecological system map to assess mitigation mechanisms and identify sustainable management pathways capable of providing long-term solutions to halt water hyacinth proliferation in freshwater bodies. The campaign removed over 75% (~1271 ha) of water hyacinth, yet within one year the plant resurged to levels ~18% higher than pre-removal. This rebound highlights the ecological resilience of water hyacinth and the limitations of short term, noncontinuous control strategies. Our analysis identifies unmanaged catchment nutrient inputs as the primary driver of proliferation. Lake Tana serves as a model system demonstrating that water hyacinth functions less as a traditional invader and more as a bioindicator of eutrophication. We propose a transferable conceptual and methodological framework combining continuous removal, catchment-based nutrient management, and circular bioeconomy approaches, offering globally relevant lessons for sustainable management of nutrient-enriched tropical freshwater systems.
Sammendrag
Fuglebestandene i det norske jordbrukslandskapet går tilbake. Nye tall fra 3Q-programmet viser en nedgang på rundt 25 prosent siden 2000. Utviklingen tyder på at endringer i arealbruk og driftsformer påvirker leveområdene til mange arter, og viser behovet for målrettede tiltak for et mer variert jordbrukslandskap.
Divisjon for skog og utmark
Co-developing Decision Support System for Coastal Mangrove-based Socio-ecological Systems in Eastern and Western Africa (CoastMan)
Coastal Mangroves in Eastern and Western Africa offer coastal communities much-needed ecosystem services and protection against key climate change risks, including risks to low-lying coastal-ecological systems, risks to terrestrial and ocean ecosystems, and as a result, risks to food security.
Divisjon for skog og utmark
Optimizing Carbon, Soil Health and Yield in Coffee-Forest Systems as a Climate-Smart Land Management in Ethiopia (CoffeeLand)
CoffeeLand is an interdisciplinary research project aimed at advancing climate-smart land management in Ethiopia’s coffee-forest systems, which are critical for biodiversity, livelihoods, and global Arabica coffee genetic resources. These systems support millions of smallholder farmers but are increasingly threatened by climate change, land-use pressure, and declining productivity.