Shivesh Karan
Research Scientist
Attachments
ResumeBiography
Shivesh holds a Ph.D. in Environmental Science and Engineering, specializing in sustainable land management through geospatial data analysis. With a foundation in Computer Science and Engineering, his expertise lies at the intersection of technology and environmental science. His research spans water resource vulnerability, bioeconomy strategies, biochar applications in agriculture, and he is particularly interested in contributing to research related to geographical data synthesis and analysis for climate change adaptation and mitigation.
Authors
Christian Pedersen Svein Olav Krøgli Shivesh Karan Svein Dale Grete Stokstad Diress Tsegaye AlemuAbstract
Over recent decades, farmland and meadow-breeding bird populations in Europe have markedly declined, attributed to factors like agricultural intensification and land abandonment. Parts of the Norwegian Monitoring Programme for Agricultural Landscapes explore the correlation between land use and bird species, aiming to understand how spatial heterogeneity and land use diversity affect the richness, abundance, and distribution of farmland birds. Between 2000 and 2023, we saw declining populations and reduced distributions of several farmland bird species within the monitoring squares. Additionally, we found that both spatial heterogeneity of land use and high land type diversity positively influenced farmland birds. This gives important insight on how to design biodiverse agricultural landscapes. We also examined the impact of agricultural intensity on 25 farmland bird species, using livestock density and pasture size as indicators. Larger pastures generally benefited a wide range of farmland bird species. Different bird species responded variably to livestock numbers, but high livestock density led to a decrease in overall farmland bird abundance. Many countries subsidize sustainable farming to protect biodiversity. We studied Norwegian agri-environmental schemes' impact on farmland and meadow-breeding birds. We found that bird observations rose when these measures were in place but often declined once the support ended. Furthermore, the schemes were geographically limited and relatively few farmers participated. While short-term benefits were evident, long-term effects remain uncertain, highlighting the need for improved conservation strategies. Emphasizing the importance of spatially heterogeneous agricultural landscapes with high land type diversity and natural areas, the study indicates the type of agricultural landscapes we should be aiming for to maintain and restore biodiversity.
Abstract
Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. Accurately mapping UGS is important for sustainable urban planning and management. Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. Object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. This paper reviews the application of OBIA on satellite images for UGS mapping, focusing on various data sources, popular segmentation methods, and classification techniques, highlighting their respective advantages and limitations. Key segmentation methodologies discussed include multi-resolution segmentation and watershed segmentation. For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. The review also addresses the challenges associated with OBIA, such as dealing with heterogenous urban landscapes, data sources and with OBIA methods itself. Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. By providing a detailed analysis of the current state-of-the-art in object-based UGS mapping, this review aims to guide future research and practical applications in UGS management.
Abstract
Studien undersøker hvordan vegetasjonsdekke (NDVI) og overflaterefleksjon (albedo) varierer gjennom året i norske utmarksområder som er beitet og ubeitet. Utmarkene har stor betydning for beitebruk, biologisk mangfold og karbonlagring, men endringer i landbruk og redusert beitepress påvirker vegetasjonen og kan ha klimakonsekvenser. Analysen bygger på satellittdata fra 18 lokaliteter i perioden 2019–2023. Resultatene viser tydelige sesongmønstre: NDVI er lav om vinteren og høy om sommeren, mens albedo er høy i snødekte perioder og lav når vegetasjon dominerer. Det ble ikke funnet signifikante forskjeller mellom beitede og ubeitede områder samlet sett, selv om enkelte lokaliteter viste små variasjoner. Dette tyder på at sesong og fenologi har større betydning enn beite, og at metodiske begrensninger – særlig grov oppløsning i albedodata – kan maskere lokale effekter. Studien anbefaler bruk av høyoppløselige data og mer avanserte metoder for å bedre forstå klimaeffektene av endret beitebruk.