The measurement of mangrove characteristics in southwest Florida using spot multispectral data

Jensen, John R.; Lin, Hongyue; Yang, Xinghe; Ramsey, Elijah W.; Davis, Bruce A.; Thoemke, Chris W.

doi:10.1080/10106049109354302

Cited by 86 publications

(45 citation statements)

References 13 publications

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“…Jensen et al [126] found that NDVI data derived from SPOT XS correlated to a high degree (r = 0.913) with the percentage of mangrove canopy closure. Canopy-closure charts or density maps provide additional information on the dynamics of mangrove vegetation and their health status [3,5,75,112,114].…”

Section: Methodsmentioning

confidence: 99%

Remote Sensing of Mangrove Ecosystems: A Review

et al. 2011

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Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high-and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the OPEN ACCESSRemote Sens. 2011, 3 879 services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations.

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Section: Methodsmentioning

confidence: 99%

Remote Sensing of Mangrove Ecosystems: A Review

et al. 2011

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“…Remotely sensed spectral differences correlate with biophysical properties of the mangrove canopy. In particular, numerous studies have shown that mangrove normalized difference vegetation index (NDVI) values correlate with biomass, canopy cover, and leaf area index (LAI) [20][21][22][23][24][25]. Other studies have utilized RS data to analyze the relationship between changes in coastal land use and mangrove distribution [26][27][28][29][30][31][32] and to characterize changes in wetland vegetation with alterations in areal coverage [33,34].…”

Section: Introductionmentioning

confidence: 99%

Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery

et al. 2011

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Abstract:In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non-mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category OPEN ACCESSRemote Sens. 2011, 3 1569 obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.

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“…Landsat and SPOT images have been used for visual interpretation (Gang and Agatsiva, 1992), determining vegetation index (Blasco et al, 1986;Chaudhury 1990;Jensen et al, 1991), classification (Aschbacher et al, 1995), and band rationing (Long and Skewes, 1994) of all types of mangrove vegetation. Remote sensing applications have been applied mainly for mangrove inventory, mapping, and change detection.…”

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confidence: 99%

Mangroves in peril: unprecedented degradation rates of peri-urban mangroves in Kenya

et al. 2014

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Abstract. Marine ecosystems are experiencing unprecedented degradation rates higher than any other ecosystem on the planet, which in some instances are up to 4 times those of rainforests. Mangrove ecosystems have especially been impacted by compounded anthropogenic pressures leading to significant cover reductions of between 35 and 50 % (equivalent to 1-2 % loss pa) for the last half century. The main objective of this study was to test the hypothesis that peri-urban mangroves suffering from compounded and intense pressures may be experiencing higher degradation rates than the global mean (and/or national mean for Kenya) using Mombasa mangroves (comprising Tudor and Mwache creeks) as a case study. Stratified sampling was used to sample along 22 and 10 belt transects in Mwache and Tudor respectively, set to capture stand heterogeneity in terms of species composition and structure in addition to perceived human pressure gradients using proximity to human habitations as a proxy. We acquired SPOT (HRV/ HRVIR/ HRS) images of April 1994, May 2000 and January 2009 and a vector mangrove map of 1992 at a scale of 1 : 50 000 for cover change and species composition analysis. Results from image classification of the 2009 image had 80.23 % overall accuracy and Cohen's kappa of 0.77, thus proving satisfactory for use in this context. Structural data indicate that complexity index (CI) which captures stand structural development was higher in Mwache at 1.80 compared to Tudor at 1.71. From cover change data, Tudor lost 86.9 % of the forest between 1992 and 2009, compared to Mwache at 45.4 %, representing very high hitherto undocumented degradation rates of 5.1 and 2.7 % pa respectively. These unprecedentedly high degradation rates, which far exceed not only the national mean (for Kenya of 0.7 % pa) but the global mean as well, strongly suggest that these mangroves are highly threatened due to compounded pressures. Strengthening of governance regimes through enforcement and compliance to halt illegal wood extraction, improvement of land-use practices upstream to reduce soil erosion, restoration in areas where natural regeneration has been impaired, provision of alternative energy sources/building materials and a complete moratorium on wood extraction especially in Tudor Creek to allow recovery are some of the suggested management interventions.

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The measurement of mangrove characteristics in southwest Florida using spot multispectral data

Cited by 86 publications

References 13 publications

Remote Sensing of Mangrove Ecosystems: A Review

Remote Sensing of Mangrove Ecosystems: A Review

Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery

Mangroves in peril: unprecedented degradation rates of peri-urban mangroves in Kenya

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