Affiliation:
1. Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of NSW, Sydney, NSW 2052, Australia
Abstract
A remote sensing method that integrates virtual sampling from formalized visual interpretations is proposed to facilitate land cover mapping and enhance its accuracy, with an emphasis on spatial and temporal scalability. Indices are widely used for mapping and monitoring surface water across space and time; however, they typically display some kind of limitation across different environments and seasons. A decision matrix framework based on observations derived from interpretation keys was designed to compare the performance of existing indices alongside a set of newly developed indices. This comparison helped to shortlist indices that warranted further evaluation and accuracy assessment to identify effective indices for global inter-seasonal surface water extent mapping. Additional visual inspections were conducted for criteria that remained unresolved by the decision matrix to examine index consistency across the seasons in a wide range of geographic settings around the world, and further reduce the shortlist. An accuracy assessment was performed for three new shortlisted indices. On a global scale, CAWI (Comprehensive Automatic Water Index) was the best-performing index. Its distinct binary data distribution provides the possibility of regional automatic Otsu thresholding. CAWI was determined to be compatible for Sentinel-2 and Landsat 8 sensors, providing the highest possible spatial resolution as well as the longest time series for retrospective analyses with freely available multispectral imagery. Two alternative indices were identified for sensors limited to the visible and NIR bands. The first index, CATWIC (Clear and Turbid Water Index Combination), split the classification of water into two components, with one index for generally clear water and another index for turbid water. The second, NDCHRWI (Normalized Difference Colourimetric High Resolution Water Index), applied the hue angle from a normalized difference RGB. Masking indices based on modified HSV Saturation equations were developed to reduce misclassification due to other high reflectance features. The indices’ overall accuracies, respectively, were: 94.97%, 94.51%, and 94.85%. This study concludes with recommendations for the application of different indices for sensors possessing shortwave infrared bands and for sensors limited to the visible and NIR bands, with a simple stratification of six zones for Global Surface Water monitoring.
Funder
Australian Research Council
Subject
General Earth and Planetary Sciences
Reference73 articles.
1. Willaarts, B.A., Garrido, A., and Llamas, M.R. (2014). Water for Food Security and Well-Being in Latin America and the Caribbean: Social and Environmental Implications for a Globalized Economy, Routledge.
2. FAO, IFAD, UNICEF, WFP, and WHO (2022). The State of Food Security and Nutrition in the World 2022. Repurposing Food and Agricultural Policies to Make Healthy Diets More Affordable, FAO.
3. Water privatization in developing countries: Principles, implementations and socio-economic consequences;Bhattacharya;World Sci. News,2015
4. Murky waters: The impact of privatizing water use on environmental degradation and the exclusion of local communities in the Caribbean;Arango;Int. J. Water Resour. Dev.,2021
5. Remote sensing of inland waters: Challenges, progress and future directions;Palmer;Remote Sens. Environ.,2015
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献