Incorporating knowledge uncertainty into species distribution modelling

Monitoring progress towards global goals and biodiversity targets require reliable descriptions of species distributions over time and space. Current gaps in accessible information on species distributions urges the need for integrating all available data and knowledge sources, and intensifying cooperations to more effectively support global environmental governance. For many areas and species groups, experts can constitute a valuable source of information to fill the gaps by offering their knowledge on species-environment interactions. However, expert knowledge is always subject to uncertainty, and incorporating that into species distribution mapping poses a challenge. We propose the use of the dempster–shafer theory of evidence (DST) as a novel approach in this field to extract expert knowledge, to incorporate the associated uncertainty into the procedure, and to produce reliable species distribution maps. We applied DST to model the distribution of two species of eagle in Spain. We invited experts to fill in an online questionnaire and express their beliefs on the habitat of the species by assigning probability values for given environmental variables, along with their confidence in expressing the beliefs. We then calculated evidential functions, and combined them using Dempster’s rules of combination to map the species distribution based on the experts’ knowledge. We evaluated the performances of our proposed approach using the atlas of Spanish breeding birds as an independent test dataset, and further compared the results with the outcome of an ensemble of conventional SDMs. Purely based on expert knowledge, the DST approach yielded similar results as the data driven SDMs ensemble. Our proposed approach offers a strong and practical alternative for species distribution modelling when species occurrence data are not accessible, or reliable, or both. The particular strengths of the proposed approach are that it explicitly accounts for and aggregates knowledge uncertainty, and it capitalizes on the range of data sources usually considered by an expert

Scenario-based analysis of the impacts of lake drying on food production in the Lake Urmia Basin of Northern Iran

In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well understood and known to influence the local and regional food production, other potential impacts by dying lakes are as yet unknown. The food production in the Urmia region is predominantly regional and relies on local water sources. To explore the current and projected impacts of the dying lake on food production, we investigated changes in the climatic conditions, land use, and land degradation for the period 1990–2020. We examined the environmental impacts of lake drought on food production using an integrated scenario-based geoinformation framework. The results show that the lake drought has significantly affected and reduced food production over the past three decades. Based on a combination of cellular automaton and Markov modeling, we project the food production for the next 30 years and predict it will reduce further. The results of this study emphasize the critical environmental impacts of the Urmia Lake drought on food production in the region. We hope that the results will encourage authorities and environmental planners to counteract these issues and take steps to support food production. As our proposed integrated geoinformation approach considers both the extensive impacts of global climate change and the factors associated with dying lakes, we consider it to be suitable to investigate the relationships between environmental degradation and scenario-based food production in other regions with dying lakes around the world

Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas

Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success