Anders Knudby

Remote sensing of seagrasses in a patchy multi-species environment

We tested the utility of IKONOS satellite imagery to map seagrass distribution and biomass in a 4.1 km2 area around Chumbe Island, Zanzibar, Tanzania. Considered to be a challenging environment to map, this area is characterized by a diverse mix of inter- and subtidal habitat types. Our mapped distribution of seagrasses corresponded well to field data, although the total seagrass area was underestimated due to spectral confusion and misclassification of areas with sparse seagrass patches as sparse coral and algae-covered limestone rock. Seagrass biomass was also accurately estimated (r 2 = 0.83), except in areas with Thalassodendron ciliatum (r 2 = 0.57), as the stems of T. ciliatum change the relationship between light interception and biomass from that of other species in the area. We recommend the use of remote sensing over field-based methods for seagrass mapping because of the comprehensive coverage, high accuracy and ability to estimate biomass. The results obtained with IKONOS imagery in our complex study area are encouraging, and support the use of this data source for seagrass mapping in similar areas.

Progress in the use of remote sensing for coral reef biodiversity studies

Coral reefs are hotspots of marine biodiversity, and their global decline is a threat to our natural heritage. Conservation management of these precious ecosystems relies on accurate and up-to-date information about ecosystem health and the distribution of species and habitats, but such information can be costly to gather and interpret in the field. Remote sensing has proven capable of collecting information on geomorphologic zones and substrate types for coral reef environments, and is cost-effective when information is needed for large areas. Remote sensing-based mapping of coral habitat variables known to influence biodiversity has only recently been undertaken and new sensors and improved data processing show great potential in this area. This paper reviews coral reef biodiversity, the influence of habitat variables on its local spatial distribution, and the potential for remote sensing to produce maps of these habitat variables, thus indirectly mapping coral reef biodiversity and fulfilling information needs of coral reef managers.

Estimating daily maximum air temperature from MODIS in British Columbia, Canada

Air temperature (Ta) is an important climatological variable for forest research and management. Due to the low density and uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Ta, especially in mountainous areas with complex terrain and high local variability. In this paper, the daily maximum Ta in British Columbia, Canada was estimated by satellite remote sensing. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the summer period (June to August) from 2003 to 2012 were collected to estimate Ta. Nine environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), latitude, longitude, distance to ocean, altitude, albedo, and solar radiation) were selected as predictors. Analysis of the relationship between observed Ta and spatially averaged remotely sensed LST indicated that 7 × 7 pixel size was the optimal window size for statistical models estimating Ta from MODIS data. Two statistical methods (linear regression and random forest) were used to estimate maximum Ta, and their performances were validated with station-by-station cross-validation. Results indicated that the random forest model achieved better accuracy (mean absolute error, MAE = 2.02°C, R2 = 0.74) than the linear regression model (MAE = 2.41°C, R2 = 0.64). Based on the random forest model at 7 × 7 pixel size, daily maximum Ta at a resolution of 1 km in British Columbia in the summer of 2003–2012 was derived, and the spatial distribution of summer Ta in this area was discussed. The satisfactory results suggest that this modelling approach is appropriate for estimating air temperature in mountainous regions with complex terrain.

Simple and effective monitoring of historic changes in nearshore environments using the free archive of Landsat imagery

The recently released archive of Landsat imagery can be used to detect historic changes in nearshore environments. We used a series of free Landsat images spanning the years from 1984 to 2009 to detect changes in the spatial extent of dominant substrate types, coral, algae, and seagrass, around Bawe and Chumbe islands in Zanzibar, and we compared the use of true-colour composites and supervised classifications. Results indicate temporal changes in the spatial extent of seagrass meadows are easily mapped with Landsat imagery, whereas temporal changes in algae cover and particularly coral cover pose greater challenges because of the similarities in spectral reflectance properties between the relevant substrate types. Supervised classification requires substantially more processing than the simple display of true-colour composites, but does not improve interpretation in our study. We suggest that historic Landsat imagery, obtained at no cost and processed minimally with free software, is the best available data source for studies of historic changes in the nearshore environments of East Africa.

Modeling the distribution of Geodia sponges and sponge grounds in the Northwest Atlantic.

Deep-sea sponge grounds provide structurally complex habitat for fish and invertebrates and enhance local biodiversity. They are also vulnerable to bottom-contact fisheries and prime candidates for Vulnerable Marine Ecosystem designation and related conservation action. This study uses species distribution modeling, based on presence and absence observations of Geodia spp. and sponge grounds derived from research trawl catches, as well as spatially continuous data on the physical and biological ocean environment derived from satellite data and oceanographic models, to model the distribution of Geodia sponges and sponge grounds in the Northwest Atlantic. Most models produce excellent fits with validation data although fits are reduced when models are extrapolated to new areas, especially when oceanographic regimes differ between areas. Depth and minimum bottom salinity were important predictors in most models, and a Geodia spp. minimum bottom salinity tolerance threshold in the 34.3-34.8 psu range was hypothesized on the basis of model structure. The models indicated two currently unsampled regions within the study area, the deeper parts of Baffin Bay and the Newfoundland and Labrador slopes, where future sponge grounds are most likely to be found.

Mapping fish community variables by Integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area

Abstract: The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km 2 ) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.

Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data

In the face of increasing climate-related impacts on coral reefs, the integration of ecosystem resilience into marine conservation planning has become a priority. One strategy, including resilient areas in marine protected area (MPA) networks, relies on information on the spatial distribution of resilience. We assess the ability to model and map six indicators of coral reef resilience—stress-tolerant coral taxa, coral generic diversity, fish herbivore biomass, fish herbivore functional group richness, density of juvenile corals and the cover of live coral and crustose coralline algae. We use high spatial resolution satellite data to derive environmental predictors and use these in random forest models, with field observations, to predict resilience indicator values at unsampled locations. Predictions are compared with those obtained from universal kriging and from a baseline model. Prediction errors are estimated using cross-validation, and the ability to map each resilience indicator is quantified as the percentage reduction in prediction error compared to the baseline model. Results are most promising (percentage reduction = 18.3%) for mapping the cover of live coral and crustose coralline algae and least promising (percentage reduction = 0%) for coral diversity. Our study has demonstrated one approach to map indicators of coral reef resilience. In the context of MPA network planning, the potential to consider reef resilience in addition to habitat and feature representation in decision-support software now exists, allowing planners to integrate aspects of reef resilience in MPA network development.

A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area

Apparent temperature is more closely related to mortality during extreme heat events than other temperature variables, yet spatial epidemiology studies typically use skin temperature (also known as land surface temperature) to quantify heat exposure because it is relatively easy to map from satellite data. An empirical approach to map apparent temperature at the neighborhood scale, which relies on publicly available weather station observations and spatial data layers combined in a random forest regression model, was demonstrated for greater Vancouver, Canada. Model errors were acceptable (cross-validated RMSE=2.04 °C) and the resulting map of apparent temperature, calibrated for a typical hot summer day, corresponded well with past temperature research in the area. A comparison with field measurements as well as similar maps of skin temperature and air temperature revealed that skin temperature was poorly correlated with both air temperature (R(2)=0.38) and apparent temperature (R(2)=0.39). While the latter two were more similar (R(2)=0.87), apparent temperature was predicted to exceed air temperature by more than 5 °C in several urban areas as well as around the confluence of the Pitt and Fraser rivers. We conclude that skin temperature is not a suitable proxy for human heat exposure, and that spatial epidemiology studies could benefit from mapping apparent temperature, using an approach similar to the one reported here, to better quantify differences in heat exposure that exist across an urban landscape.

Using multiple Landsat scenes in an ensemble classifier reduces classification error in a stable nearshore environment

Medium-scale land cover maps are traditionally created on the basis of a single cloud-free satellite scene, leaving information present in other scenes unused. Using 1309 field observations and 20 cloud- and error-affected Landsat scenes covering Zanzibar Island, this study demonstrates that the use of multiple scenes can both allow complete coverage of the study area in the absence of cloud-free scenes and obtain substantially improved classification accuracy. Automated processing of individual scenes includes derivation of spectral features for use in classification, identification of clouds, shadows and the land/water boundary, and random forest-based land cover classification. An ensemble classifier is then created from the single-scene classifications by voting. The accuracy achieved by the ensemble classifier is 70.4%, compared to an average of 62.9% for the individual scenes, and the ensemble classifier achieves complete coverage of the study area while the maximum coverage for a single scene is 1209 of the 1309 field sites. Given the free availability of Landsat data, these results should encourage increased use of multiple scenes in land cover classification and reduced reliance on the traditional single-scene methodology.

Assessing the effect of management zonation on live coral cover using multi-date IKONOS satellite imagery

Change in live coral cover within the nine different management zones of the Marine Protected Area (MPA) around Bunaken Island, Indonesia, was estimated using IKONOS satellite image data for 2001 and 2004. For both years, field data were used to classify image pixels on a semi-quantitative scale based mainly on coral cover. The resulting substrate maps had overall classification accuracies of 78% and 81%, respectively. Change was estimated for each zone using a post-classification comparison. Results indicated an increase in live coral cover in one zone, a decrease in two other zones, and no change in the remaining six zones. The results show no obvious influence of the MPAs zonation plan on changes in live coral cover in the three year period investigated, and the study demonstrates the use of remote sensing to determine change in live coral cover, to assess zonation effectiveness, and to guide management efforts.