Anna Stephanie handed in her MSc thesis on “Impact of remote sensing characteristics for biodiversity monitoring”. Very impressive study on multi-scale, multi-model and multi-variable analysis of mangroves in Myanmar.
While Myanmar is one of the world’s hotspots for biodiversity and endemism, it is currently undergoing enormous political and economic transformations which are likely to increase the pressure on its already endangered forest ecosystems. In this context, mangrove forests are of particular relevance, as they are not only among the Earth’s most imperiled tropical environments, but also provide numerous ecosystem services to humanity. To ensure an ecologically worthwhile management of mangrove ecosystems, it is necessary that inventories are undertaken on a regular basis. Remote sensing offers a cost efficient and rapid method to periodically monitor mangrove forests. However, the current availability of various sensor types and different classification methods complicateswell-informed selection of the most appropriate methodology for an effective biodiversity monitoring. In order to assist applied ecologists in this highly complex decision-making process, this study compared the suitability of medium-resolution Landsat 8 and high-resolution RapidEye imagery to accurately monitor mangrove forests. Spatial and spectral resolution, classification algorithms and different predictor combinations were investigated as influencing elements. A multi-scale classification approach was developed to account for the fact that biodiversity monitoring for conservation is typically conducted on numerous spatial scales ranging from local to global perspectives. By formulating recommendation for practitioners, this study’s aim was to bridge the gap between research and its implementation in applied conservation. Results of the analysis showed that medium-resolution Landsat 8 imagery mostly leads to higher classification accuracies than high-resolution RapidEye data in the context of mangrove mapping in Southern Myanmar. The comparison of different predictor combinations suggested, that this difference is mainly attributable to the additional spectral bands provided by the Landsat 8 sensor. By investigating RapidEye images with spatial resolutions of 5 – 30 meters, it was discovered that overall classification accuracies increased with coarser spatial resolution regarding the majority of land cover classes. Moreover, the accuracy of land cover predictions was strongly influenced by the choice of specific classification algorithms as well as the number and characteristics of predictor layers. Referring to the main findings of this study, the application of medium-resolution Landsat 8 data is recommended to applied conservationists. This is based on its superior performance in most of the classifications as well as on its cost-free availability.
The book “Protected Areas: Are They Safeguarding Biodiversity?” edited by Nathalie Pettorelli, Martin Wegmann, L Gurney and Gregoire Dubois. We cover the importance of remote sensing data analysis to monitor protected areas and provide a good overview of applications and potential.+
The book covers much more topics.
From the webpage:
Protected areas spearhead our response to the rapidly accelerating biodiversity crisis. However, while the number of protected areas has been growing rapidly over the past 20 years, the extent to which the world’s protected areas are effectively conserving species, ecosystems, and ecosystem services is poorly understood.
- Highlights new techniques for better management and monitoring of protected areas
- Sets guidelines for the decision making processes involved in setting up and maintaining protected areas
- Fully international in scope and covering all ecosystems and biomes
a new article just got published on monitoring species diversity from satellite remote sensing highlighting the potential and pitfalls. Assessing the level of diversity in plant communities from field-based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost-effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth’s surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha-diversity is relatively straightforward, calculation of beta-diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma-diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments.
Rocchini, D., Boyd, D. S., Féret, J.-B., Foody, G. M., He, K. S., Lausch, A., Nagendra, H., Wegmann, M., Pettorelli, N. (2015), Satellite remote sensing to monitor species diversity: potential and pitfalls. Remote Sensing in Ecology and Conservation. doi: 10.1002/rse2.9
Our EGU session “Mapping, Monitoring & Modelling of Vegetation Characteristics using Earth Observation” got accepted and is now online.
The EGU, the General Assembly of the European Geosciences Union, is held at the Austria Center Vienna (ACV) in Vienna, Austria, from 17–22 April 2016.
Remote sensing, be it in the form of satellite imagery or aerial photography from manned aircrafts or UAVs, has proven its potential as a unique tool for retrieving vegetation properties at the local, the regional and global scales. Over the last decades, a substantial amount of work has been allocated to the retrieval of vegetation characteristics, e.g. mapping of the extent of vegetation cover, monitoring of vegetation condition using the NDVI or other indices, monitoring forest cover trends, monitoring the expansion of bushes in the expense of palatable grasses in the drylands, woody structure modelling and mapping using Synthetic Aperture Radar data, extracting structural vegetation components from LiDAR for biomass estimation, combining hyperspectral and LiDAR data for upscaling vegetation structural information, to mention but a few. Numerous satellite missions are currently being used to quantify such characteristics in a wide range of temporal and spatial resolutions; new missions with improved capacities are constantly becoming available or planned for the near future in an ever-increasing rate. However, the use of remote sensing for mapping, monitoring or modelling vegetation characteristics is clearly not problem-free: quite the contrary. Within this context, we welcome studies that present novel approaches of mapping, monitoring and modelling vegetation characteristics. We endeavour this session to provide the platform for the analysis of the benefits as well as the pitfalls of using aerial photography, UAVs, LiDAR, Radar, hyperspectral or multi-spectral satellite data in this field.
Compton J. Tucker by NASA will be the keynote speaker.
chairs: Elias Symeonakis, Hanna Meyer, Thomas Higginbottom, Martin Wegmann
more details here: