Land use and land cover change analysis in predominantly man-made coastal wetlands: towards a methodological framework

Camilleri, Sarah; Giglio, Michaela De; Stecchi, Francesco; Pérez-Hurtado, Alejandro

doi:10.1007/s11273-016-9500-4

Cited by 31 publications

(26 citation statements)

References 27 publications

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“…In order to understand how land use and land cover change make and interact with global world schemes, it is necessary to provide information on what changes occur, when and where they seem, the values at which they occur, and the natural and social drivers of these changes [31]. Satellite remote sensing data may provide useful information on the geographical dispersion of land use and land cover change, along with price and time savings for information gathering over traditional inventories and studies across large regions [32]. In addition, data on remote sensing may provide information on the temporal trends and spatial distributions of urbanised regions involved in understanding, modelling, and designing land change.…”

Section: Land Use Change Detection and Land Coveringmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

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Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified runoff , water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

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Section: Land Use Change Detection and Land Coveringmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

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“…But, in practice, telling the difference based on RS data alone is not always possible. Use of object-based classifications or of a predefined matrix to discriminate natural from man-made wetlands have been proposed (Camilleri et al, 2017). However, natural and artificial wetlands are often similar in shapes and updating an artificial wetland matrix can pose a problem for routine monitoring over large geographical scale comprising several hundreds of sample sites.…”

Section: Characteristic 2: Artificial Vs Natural Wetland Habitats Andmentioning

confidence: 99%

“…A confusion (or error) matrix is an important requirement (e.g. Camilleri et al, 2017). It should be developed using ancillary data to quantify for each habitat class, or aggregation of classes, the risk of confusion with other classes and the overall error rate (both omission and commission).…”

Section: Box 1 a Quality Control Exercise Of Mediterranean Wetlandmentioning

confidence: 99%

“…Mapping the water dynamics of wetlands requires that the flood extent is mapped at different dates throughout the annual hydrological cycle (e.g. Camilleri et al, 2017). This information is often overlaid to produce a single map providing flooding duration of different wetlands according to classes, e.g., as "Never," "Seasonally" and "Permanently flooded" areas (GlobWetland-II, 2011) over a given cycle, or as flood/submersion frequencies, i.e., percentages of the analysed images in which a given pixel was flooded (e.g.…”

Section: Images Covering the Whole Hydroperiodmentioning

confidence: 99%

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Mapping Mediterranean Wetlands With Remote Sensing: A Good-Looking Map Is Not Always a Good Map

Perennou

Guelmami

Paganini

et al. 2018

Advances in Ecological Research

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This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

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“…Increased vegetation greenness may potentially restore the ecosystem's food web, but at very high densities it may also obstruct waterbird foraging and movement [69]. Hence the benefits of the increased greenness from gradual NDVI change and land cover changes remain open to further study [70].…”

Section: Impacts Of Land Cover Change On Vegetation Greennessmentioning

confidence: 99%

Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985–2016 Using Landsat Time-Series Data

et al. 2017

Remote Sensing

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Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land cover and vegetation growth of coastal areas. However, rarely has research investigated these frequently changed coastal areas. In this study, Landsat Normalized Difference Vegetation Index time series (1984-2016) data and the Breaks For Additive Seasonal and Trend algorithm were used to detect the intensity and dates of abrupt changes in a typical coastal area-Hangzhou Bay, China. The prior and posterior land cover categories of each change were classified using phenology information through a Random Forest model. The impacts of land cover change on vegetation greenness trends of the inland and reclaimed areas were analyzed through distinguishing gradual and abrupt changes. The results showed that the intensity and date of land cover change were detected successfully with overall accuracies of 88.7% and 86.1%, respectively. The continuous land cover dynamics were retrieved accurately with an overall accuracy of 91.0% for ten land cover classifications. Coastal reclamation did not alleviate local cropland occupation, but prompted the vegetation greenness of the reclaimed area. Most of the inland area showed a browning trend. The main contributors to the greenness and browning trends were also quantified. These findings will help the natural resource management community generate better understanding of coastal reclamation and make better management decisions.

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Land use and land cover change analysis in predominantly man-made coastal wetlands: towards a methodological framework

Cited by 31 publications

References 27 publications

Detection of Urban and Environmental Changes via Remote Sensing

Detection of Urban and Environmental Changes via Remote Sensing

Mapping Mediterranean Wetlands With Remote Sensing: A Good-Looking Map Is Not Always a Good Map

Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985–2016 Using Landsat Time-Series Data

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