The Mangroves of the Zambezi Delta: Increase in Extent Observed via Satellite from 1994 to 2013

Shapiro, Aurélie; Trettin, Carl C.; Kuchly, Helga; Alavinapanah, Sadroddin; Bandeira, Salomão

doi:10.3390/rs71215838

Cited by 56 publications

(53 citation statements)

References 23 publications

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“…The applicability of Landsat data for mapping the distribution and ecological characteristics of mangroves at global [20], national [24,[38][39][40] and-despite its moderate, 30 m spatial resolutionlocalised [27,41,42] scales is well proven. With an archive stretching back to the 1970s [43], Landsat data are also ideal for analysing and monitoring the dynamics of wetlands and their surrounding ecosystems [44][45][46][47].…”

Section: Remote Sensing Methodsmentioning

confidence: 99%

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Mangrove Carbon Stocks and Ecosystem Cover Dynamics in Southwest Madagascar and the Implications for Local Management

Benson

Glass

Jones

et al. 2017

Forests

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Abstract:Of the numerous ecosystem services mangroves provide, carbon storage is gaining particular attention for its potential role in climate change mitigation strategies. Madagascar contains 2% of the world's mangroves, over 20% of which is estimated to have been deforested through charcoal production, timber extraction and agricultural development. This study presents a carbon stock assessment of the mangroves in Helodrano Fagnemotse in southwest Madagascar alongside an analysis of mangrove land-cover change from 2002 to 2014. Similar to other mangrove ecosystems in East Africa, higher stature, closed-canopy mangroves in southwest Madagascar were estimated to contain 454.92 (±26.58) Mg·C·ha −1 . Although the mangrove extent in this area is relatively small (1500 ha), these mangroves are of critical importance to local communities and anthropogenic pressures on coastal resources in the area are increasing. This was evident in both field observations and remote sensing analysis, which indicated an overall net loss of 3.18% between 2002 and 2014. Further dynamics analysis highlighted widespread transitions of dense, higher stature mangroves to more sparse mangrove areas indicating extensive degradation. Harnessing the value that the carbon stored within these mangroves holds on the voluntary carbon market could generate revenue to support and incentivise locally-led sustainable mangrove management, improve livelihoods and alleviate anthropogenic pressures.

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Section: Remote Sensing Methodsmentioning

confidence: 99%

“…This protocol is well tested and published for mangrove mapping applications in Madagascar [26,41] and as such is only summarized here.…”

Section: Land-use and Land Cover Classificationsmentioning

confidence: 99%

Mangrove Carbon Stocks and Ecosystem Cover Dynamics in Southwest Madagascar and the Implications for Local Management

Benson

Glass

Jones

et al. 2017

Forests

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“…Change detection is important for detecting dynamic changes of the Earth. Change detection attempts to identify land cover differences in the same geographical area across a period of time [2] and can be applied to various domains, including urban expansion [3], disaster monitoring [4], land cover map updating [5], forest degradation survey [6] and glacier melting [7]. In this context, various types of multi-temporal images are exploited to resolve the above problems.…”

Section: Introductionmentioning

confidence: 99%

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

2016

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When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM) model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1) the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2) the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3) to the authors' best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.

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“…A similar increase was observed by Shapiro et al . (), who attributed this increase to mangrove expansion happening mostly within mudflats and upstream areas. Our mangrove cover estimates were, however, smaller than previously reported in Sofala and Maputo, with our Maputo cover estimate down by 60% from previous ones (Fatoyinbo et al .…”

Section: Discussionmentioning

confidence: 99%

A New Framework to Assess Relative Ecosystem Vulnerability to Climate Change

Lee

Duncan

Owen

et al. 2017

CONSERVATION LETTERS

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Climate change poses a growing risk to global biodiversity. To prioritize conservation efforts, identification of the species and ecosystems most at risk from further changes in climatic conditions is critically needed. Although frameworks are available to assess species vulnerability to climate change, we still lack an easily implementable, ecosystem-level perspective to inform landscape management. Here, we introduce a novel, spatially explicit vulnerability framework able to generate assessments at the ecosystem scale and apply it to Mozambican forest mangroves, which are under growing pressures from climate change. Results show that most of these ecosystems are currently highly vulnerable to sea level rise, while mangroves in the Zambezia and Nampula districts are highly vulnerable to both sea level rise and tropical storms. Altogether, we believe the introduced assessment framework has clear potential to inform conservation planning and management at various spatial scales, and help achieve adaptive management in the face of climatic uncertainties.

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The Mangroves of the Zambezi Delta: Increase in Extent Observed via Satellite from 1994 to 2013

Cited by 56 publications

References 23 publications

Mangrove Carbon Stocks and Ecosystem Cover Dynamics in Southwest Madagascar and the Implications for Local Management

Mangrove Carbon Stocks and Ecosystem Cover Dynamics in Southwest Madagascar and the Implications for Local Management

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

A New Framework to Assess Relative Ecosystem Vulnerability to Climate Change

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