Control of "blue carbon" storage by mangrove ageing: Evidence from a 66-year chronosequence in French Guiana.

The role of mangroves in the blue carbon stock is critical and requires special focus. Mangroves are carbon-rich forests that are not in steady-state equilibrium at the decadal time scale. Over the last decades, the structure and zonation of mangroves have been largely disturbed by coastal changes and land use conversions. The amount of time since the last disturbance is a key parameter determining forest structure, but it has so far been overlooked in mangrove carbon stock projections. In particular, the carbon sequestration rates among mangrove successional ages after (re)establishment are poorly quantified and not used in large-scale estimations of the blue carbon stock. Here, it is hypothesized that ecosystem age structure significantly modulates mangrove carbon stocks. We analysed a 66-year chronosequence of the aboveground and belowground biomass and soil carbon stock of mangroves in French Guiana, and we found that in the year after forest establishment on newly formed mud banks, the aboveground, belowground and soil carbon stocks averaged 23.56 ± 7.71, 13.04 ± 3.37 and 84.26 ± 64.14 (to a depth of 1 m) Mg C/ha, respectively. The mean annual increment (MAI) in the aboveground and belowground reservoirs was 23.56 × Age-0.52 and 13.20 × Age-0.64  Mg C ha-1  year-1 , respectively, and the MAI in the soil carbon reservoir was 3.00 ± 1.80 Mg C ha-1  year-1 . Our results show that the plant carbon sink capacity declines with ecosystem age, while the soil carbon sequestration rate remains constant over many years. We suggest that global projections of the above- and belowground reservoirs of the carbon stock need to account for mangrove age structures, which result from historical changes in coastal morphology. Our work anticipates joint international efforts to globally quantify the multidecadal mangrove carbon balance based on the combined use of age-based parametric equations and time series of mangrove age maps at regional scales.

Surveying shrimp aquaculture pond activity using multitemporal VHSR satellite images - case study from the Perancak estuary, Bali, Indonesia.

From the 1980's, Indonesian shrimp production has continuously increased through a large expansion of cultured areas and an intensification of the production. As consequences of diseases and environmental degradations linked to this development, there are currently 250,000ha of abandoned ponds in Indonesia. To implement effective procedure to undertake appropriate aquaculture ecosystem assessment and monitoring, an integrated indicator based on four criteria using very high spatial optical satellite images, has been developed to discriminate active from abandoned ponds. These criteria were: presence of water, aerator, feeding bridge and vegetation. This indicator has then been applied to the Perancak estuary, a production area in decline, to highlight the abandonment dynamic between 2001 and 2015. Two risk factors that could contribute to explain dynamics of abandonment were identified: climate conditions and pond locations within the estuary, suggesting that a spatial approach should be integrated in planning processes to operationalize pond rehabilitation.

Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images: A case study from the Perancak estuary, Bali, Indonesia.

Revegetation of abandoned aquaculture regions should be a priority for any integrated coastal zone management (ICZM). This paper examines the potential of a matchless time series of 20 very high spatial resolution (VHSR) optical satellite images acquired for mapping trends in the evolution of mangrove forests from 2001 to 2015 in an estuary fragmented into aquaculture ponds. Evolution of mangrove extent was quantified through robust multitemporal analysis based on supervised image classification. Results indicated that mangroves are expanding inside and outside ponds and over pond dykes. However, the yearly expansion rate of vegetation cover greatly varied between replanted ponds. Ground truthing showed that only Rhizophora species had been planted, whereas natural mangroves consist of Avicennia and Sonneratia species. In addition, the dense Rhizophora plantations present very low regeneration capabilities compared with natural mangroves. Time series of VHSR images provide comprehensive and intuitive level of information for the support of ICZM.

Assessing aboveground tropical forest biomass using Google Earth canopy images.

Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earth's very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 x 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted from free Google Earth images.