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Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
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- Adaptation to climate change (1)
- Climate variability (1)
- GEOBIA (1)
- High Resolution (1)
- LIDAR (1)
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- Land-Cover Mapping (1)
- Livelihood vulnerability (1)
- Local perceptions (1)
- Machine Learning (1)
- Machine learning (1)
- Malawi (1)
- NAIP (1)
- Object-based (1)
- Phenology (1)
- Regional-Scale (1)
- Remote Sensing (1)
- Remote sensing (1)
- Satellite (1)
- Spatial vulnerability analysis (1)
- Supervised Classification (1)
- Tree health (1)
- Tree species (1)
- Urban trees (1)
Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Crown-Level Mapping Of Tree Species And Health From Remote Sensing Of Rural And Urban Forests, Fang Fang
Crown-Level Mapping Of Tree Species And Health From Remote Sensing Of Rural And Urban Forests, Fang Fang
Graduate Theses, Dissertations, and Problem Reports
Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms …
Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data, Christopher A. Ramezan
Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data, Christopher A. Ramezan
Graduate Theses, Dissertations, and Problem Reports
High spatial resolution (HR) (1m – 5m) remotely sensed data in conjunction with supervised machine learning classification are commonly used to construct land-cover classifications. Despite the increasing availability of HR data, most studies investigating HR remotely sensed data and associated classification methods employ relatively small study areas. This work therefore drew on a 2,609 km2, regional-scale study in northeastern West Virginia, USA, to investigates a number of core aspects of HR land-cover supervised classification using machine learning. Issues explored include training sample selection, cross-validation parameter tuning, the choice of machine learning algorithm, training sample set size, and feature selection. A …
Multidimensional Analysis Of Vulnerability: Methodological Advances And A Case Study From Malawi., Park Mcmillan Muhonda
Multidimensional Analysis Of Vulnerability: Methodological Advances And A Case Study From Malawi., Park Mcmillan Muhonda
Graduate Theses, Dissertations, and Problem Reports
Since 1990s rural households in Malawi, constituting 85% of the population, have experienced deepening livelihood vulnerability, often manifested as persistent food insecurity. Livelihood crises have since been blamed on or attributed directly to weather perturbations/climatic shocks i.e. El-Nino induced climate variability/drought conditions. This study revealed that persistent livelihood crisis in rural Malawi cannot be attributed to or squarely blamed on weather shocks alone, rather it is at the intersection of various livelihoods shocks that rural livelihood vulnerability in Malawi is exacerbated i.e. worsening and deepening.
Thus, rural livelihood vulnerability to climate shocks in Malawi is manifest not in isolation but …