V. Venus

Global Gradients of Coral Exposure to Environmental Stresses and Implications for Local Management

Background The decline of coral reefs globally underscores the need for a spatial assessment of their exposure to multiple environmental stressors to estimate vulnerability and evaluate potential counter-measures. Methodology/Principal Findings This study combined global spatial gradients of coral exposure to radiation stress factors (temperature, UV light and doldrums), stress-reinforcing factors (sedimentation and eutrophication), and stress-reducing factors (temperature variability and tidal amplitude) to produce a global map of coral exposure and identify areas where exposure depends on factors that can be locally managed. A systems analytical approach was used to define interactions between radiation stress variables, stress reinforcing variables and stress reducing variables. Fuzzy logic and spatial ordinations were employed to quantify coral exposure to these stressors. Globally, corals are exposed to radiation and reinforcing stress, albeit with high spatial variability within regions. Based on ordination of exposure grades, regions group into two clusters. The first cluster was composed of severely exposed regions with high radiation and low reducing stress scores (South East Asia, Micronesia, Eastern Pacific and the central Indian Ocean) or alternatively high reinforcing stress scores (the Middle East and the Western Australia). The second cluster was composed of moderately to highly exposed regions with moderate to high scores in both radiation and reducing factors (Caribbean, Great Barrier Reef (GBR), Central Pacific, Polynesia and the western Indian Ocean) where the GBR was strongly associated with reinforcing stress. Conclusions/Significance Despite radiation stress being the most dominant stressor, the exposure of coral reefs could be reduced by locally managing chronic human impacts that act to reinforce radiation stress. Future research and management efforts should focus on incorporating the factors that mitigate the effect of coral stressors until long-term carbon reductions are achieved through global negotiations.

Identifying Reefs of Hope and Hopeful Actions: Contextualizing Environmental, Ecological, and Social Parameters to Respond Effectively to Climate Change

Priorities for conservation, management, and associated activities will differ based on the interplay between nearness of ecosystems to full recovery from a disturbance (pristineness), susceptibility to climate change (environmental susceptibility [ES]), and capacity of human communities to cope with and adapt to change (social adaptive capacity [AC]). We studied 24 human communities and adjacent coral reef ecosystems in 5 countries of the southwestern Indian Ocean. We used ecological measures of abundance and diversity of fishes and corals, estimated reef pristineness, and conducted socioeconomic household surveys to determine the AC of communities adjacent to selected coral reefs. We also used Web-based oceanographic and coral mortality data to predict each sites ES to climate warming. Coral reefs of Mauritius and eastern Madagascar had low ES and consequently were not predicted to be affected strongly by warm water, although these sites were differentiated by the AC of the human community. The higher AC in Mauritius may increase the chances for successful self-initiated recovery and protective management of reefs of this island. In contrast, Madagascar may require donor support to build AC as a prerequisite to preservation efforts. The Seychelles and Kenya had high ES, but their levels of AC and disturbance differed. The high AC in the Seychelles could be used to develop alternatives to dependence on coral reef resources and reduce the effects of climate change. Pristineness weighted toward measures of fish recovery was greatest for Kenyas marine protected areas; however, most protected areas in the region were far from pristine. Conservation priorities and actions with realistic chances for success require knowledge of where socioecological systems lie among the 3 axes of environment, ecology, and society.

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (Ts) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–Ts space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.

Estimating land-surface temperature under clouds using MSG/SEVIRI observations

The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e. MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Contrasting to the neighboring pixel approach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for earth surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of −3.68 K and a RMSE of 5.55 K for the validation site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ‘neighboring pixel’ scheme itself. An error propagation model applied for the proposed temporal neighboring-pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it is promising to reconstruct diurnal LST cycles from geo-stationary satellite observations.

Mangrove vulnerability modelling in parts of Western Niger Delta, Nigeria using satellite images, GIS techniques and Spatial Multi-Criteria Analysis (SMCA)

Mangroves are known for their global environmental and socioeconomic value. Despite their importance, mangrove like other ecosystems is now being threatened by natural and human-induced processes that damage them at alarming rates, thereby diminishing the limited number of existing mangrove vegetation. The development of a spatial vulnerability assessment model that takes into consideration environmental and socioeconomic criteria, in spatial and non-spatial formats has been attempted in this study. According to the model, 11 different input parameters are required in modelling mangrove vulnerability. These parameters and their effects on mangrove vulnerability were selected and weighted by experts in the related fields. Criteria identification and selection were mainly based on effects of environmental and socioeconomic changes associated with mangrove survival. The results obtained revealed the dominance of socioeconomic criteria such as population pressure and deforestation, with high vulnerability index of 0.75. The environmental criteria was broadly dispersed in the study area and represents vulnerability indices ranging from 0.00–0.75. This category reflects the greater influence of pollutant input from oil wells and pipelines and minimal contribution from climatic factors. This project has integrated spatial management framework for mangrove vulnerability assessment that utilises information technology in conjunction with expert knowledge and multi-criteria analysis to aid planners and policy/ decision makers in the protection of this very fragile ecosystem.

3D reconstruction of under water coral reef images using low cost multi-view cameras

This research describes a 3D reconstruction method of coral reefs using low-cost underwater cameras. We employed a multi-view camera system consisting of 3 identical waterproof cameras arrayed on a stereo-base, and collected footage of the seafloor in linear transects. To develop a 3D-representation of the seafloor image-pairs were first extracted from the video footage manually. Then corresponding points are automatically extracted from the stereo-pairs by the well known SIFT algorithm, which is invariant to scale, translation, and rotation. Based on the resultant x,y,z point cloud the 3D appearance of the coral reef is approximated by a Triangulation technique utilizing Delaunay Triangulation. The experimental result demonstrate robust 3D reconstruction with manual adjustment of camera A, B or C selection.

Hyper-temporal SPOT-NDVI dataset parameterization captures species distributions

Hyper-temporal SPOT NDVI images contain useful information about the environment in which a species occurs, including information such as the beginning, end, peak, and curvature of photosynthetically active vegetation (PAV) greenness signatures. This raises the question: can parameterization of hyper-temporal SPOT NDVI images be useful to predict species distribution? A set of SPOT-NDVI images for the whole of Ethiopia covering nine years was classified using the unsupervised ISODATA clustering algorithm to group similar NDVI pixel values. The HANTS (Harmonic ANalysis of Time Series) algorithm, that fits series of smoothing cosine waves, was then applied to the time series for each of the NDVI classes to generate seven output Fourier components. These components, together with the topographic parameters slope and elevation, were used as predictors in a species distribution model using MAXENT. Presence-only data of one test species, Boswellia papyrifera, were modelled. This species is diminishing at an alarming rate and requires conservation. The performance of the model was evaluated by the area under curve (AUC) of the receiver-operating characteristics value. The output distribution map was tested for its agreement with the NDVI-clustering approach and conventional B. papyrifera distribution map using Kappa. The relative contributions of the first four predictors to the MAXENT in sequence were: 2nd harmonic phase, elevation, amplitude of the 1st harmonics, and amplitude of the 2nd harmonics. The average AUC test result for the 100 runs was 0.98 with a standard deviation of 0.002. The probability distribution map clearly shows high correlation with the B. papyrifera occurrence data. In addition, the distribution map was found to be in agreement with the NDVI-clustered and conventional map with improved details. Classifying hyper-temporal NDVI images and extracting their parameters through the use of the HANTS algorithm captures the PAV greenness behaviour (parameters) of the environment of the species studied. These parameters have proved successful in predicting the distribution of B. papyrifera.