Coastal Disasters and Remote Sensing Monitoring Methods

Coastal disaster is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Remote sensing technology has real-time and large-area advantages in promoting the monitoring and forecast ability of coastal disaster. Relative to natural disasters, ones caused by human factors are more likely to be monitored and prevented. In this paper, we use several remote sensing methods to monitor or forecast three kinds of coastal disaster cause by human factors including red tide, sea-level rise and oil spilling, and make proposals for infrastructure based on the research results. The chosen method of monitoring red tide by inversing chlorophyll-a concentration is improved OC3M Model, which is more suitable for the coastal zone and higher spatial resolution than the MODIS chlorophyll-a production. We monitor the sea-level rise in coastal zone through coastline changes without artificial modifications. The improved Lagrangian model can simulate the trajectory of oil slick efficiently. Making the infrastructure planning according the coastal disasters and features of coastline contributes to prevent coastal disaster and coastal ecosystem protection. Multi-source remote sensing data can effectively monitor and prevent coastal disaster, and provide planning advices for coastal infrastructure construction

Integrating remote sensing and geostatistics in mapping Seriphium plumosum (bankrupt bush) invasion.

Master of Science in of Geography. University of KwaZulu-Natal. Pietermaritzburg, 2016The impacts of plant species invasion in natural ecosystems have attracted geo-scientific studies globally. Several studies have demonstrated that the effects of invasive species can permanently alter an ecosystem structure and affect its provision of goods and services, e.g. the provision of food and fibre, aesthetics, recreation and tourism, and regulating the spread of diseases. Plant invasion causes transformation of ecosystems including replacement of native vegetation. This study focuses on invasive plant impacting on grasslands called Seriphium plumosum. The plant is known to have allelopathic effects, killing grass species and turning grazing lands into degraded shrublands. The major challenge in grassland management is the eradication and management of S. plumosum. Central to this challenge is locating, mapping and estimating the invasion status/cover over large areas. Remote sensing based earth observation approaches offer a viable method for invasion plants mapping. Moreover, mapping of vegetation requires robust statistical analysis to determine relationships between field and remotely sensed data. Such relationships can be achieved using spatial autocorrelation. In this study, Getis statistics transformed images and geostatistical techniques, which involve modelling the spatial autocorrelation of canopy variables have been used in mapping S. plumosum. Getis statistics was used to transform SPOT (Satellites Pour l’Observation de la Terre)-6 image bands into spatially dependent Getis indices layer variables for mapping S. plumosum. Stepwise multiple Regression, ordinary kriging and cokriging were used to evaluate the cross-correlated information between SPOT6-derived Getis indices transformed layer variables and field sampled S. plumosum canopy density and percentage. To select the best SPOT6-derived Getis indices to map S. plumosum, 308 spectral Getis indices transformed layer variables were statistically evaluated. Results indicated that Rook, Positive and Horizontal Getis indices are most suitable for mapping S. plumosum with 0.83, 0.828 and 0.828 importance. The most accurate Getis index obtained using 5x5 (Lag 5) moving window yielded 0.83 mapping importance. Cokriging with the most important Getis index yielded the best in S. plumosum density prediction with root mean square error (RMSE) of 25.8 compared to ordinary kriging with RMSE of 26.1 and regression with RMSE of 35.6. This study demonstrated that Getis statistics and geostatistics were successful in mapping and predicting S. plumosum. The current study provides insights critical for developing sound framework for planning and management of S. plumosum in agro-ecological systems

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages

As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (II) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (III) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Considering this, we suggest that the MSI&TIR dataset can essentially satisfy PWD identification requirements at various stages, and the RF algorithm provides the best choice, especially in actual forest investigations. In addition, the performance of thermal imaging in the early monitoring of PWD is worthy of further investigation. These findings are expected to provide insight into future research and actual surveys regarding the selection of both remote sensing datasets and data analysis algorithms for detection requirements of different PWD infection stages to detect the disease earlier and prevent losses

Informing reforestation practices : quantifying live forest above ground biomass of a randomly mixed natural forest plantation using GIS and remote sensing models.

Master of Science in Geography. University of KwaZulu-Natal, Pietermaritzburg, 2017.Restoration of natural forests is viewed as one of the effective and viable approaches for mitigating and adapting to climate change. However, maximising the carbon capture and storage of naturally mixed forest plantations is currently a challenge for forest managers, due to the complex nature of species interaction and environmental controls that inhibit the distribution and growth rates of certain species. Monitoring the amount of carbon captured and stored in natural forest ecosystem is vital in verifying their productivity and detecting areas of concern that could be unproductive. In this study the productivity of the Buffelsdraai reforestation site was monitored using above ground biomass (AGB) of planted trees. While there are traditional approaches for monitoring forest AGB with high accuracy, these approaches are unfavourable because they are timeous and spatially restricted. Fortunately, the inception of remote sensing has provided viable approaches for estimating forest AGB at a synoptic scale and with low cost. The purpose of this study was to apply remote sensing and GIS models to quantify the ecological benefits of the Buffelsdraai reforestation project on AGB productivity. The study investigated the potential of the spatially optimised three band texture combinations in predicting and mapping forest AGB and structural diversity. This research study has potential to contribute to the importance of spatial planning and design of naturally mixed forest plantations to improve their diversity and AGB productivity. The first part of the study focused on mapping the temporal and spatial distribution of forest AGB using spatially optimised three band texture combinations computed from SPOT-6 imagery and random forest regression algorithm. The results indicated that the three band texture combinations were superior in predicting forest AGB compared to raw texture bands and two band texture combinations. The second part of the thesis focussed on assessing the effects of forest structural diversity and topographic variables on forest AGB productivity using GIS and remotely sensed data. The forest structural diversity measures were predicted using three band texture combinations modelled using random forest and stochastic gradient boosting algorithms. The topographic variables were derived using the digital elevation model in ArcMap 10.3. Results indicated that random forest yielded overall higher accuracies in predicting the forest structural diversity measures compared to stochastic gradient boosting. More importantly, the study showed that forest diversity and topographic variables have significant influences on forest AGB variability. Overall the study provided insight into the management of natural forests and to the importance of spatial planning and design of these mixed forests

Modeling sediment movement and channel response to rainfall variability after a major earthquake

© 2018 Elsevier B.V. The 2008 Wenchuan Ms 8.0 earthquake caused severe destruction in the mountainous areas of Sichuan Province, China. Landslips and mass movements led to substantial amounts of loose sediment accumulating in valleys that subsequently led to widespread riverbed aggradation. In addition to erosion and deposition hazards, this aggradation produced rivers in earthquake affected areas that were more susceptible to flash floods under extreme rainfall events. However, fluvial processes and sediment movement after a major earthquake, as well as the re-working of sediments in future events, are not well studied. In this paper, we investigate the response of sediment and river channel evolution due to different rainfall scenarios after the Wenchuan earthquake by using the CAESAR-Lisflood model. This is the first time that this landscape evolution model has been employed to explore material migration processes in a post-earthquake area, and to test its applicability to real landform changes in the studied catchment. The CAESAR-Lisflood model is well suited to simulate sediment movement, particularly the fluvial processes driven by severe rainfall after an earthquake. We calibrated the model parameters to the 2013 extreme rainfall event using high-resolution satellite images. Under rainfall scenarios of different intensity and frequency over a 10-yr period, landform evolution and sediment migration in the post-earthquake area were simulated. The results showed that the sediment yield could be significantly increased under enhanced and intensified rainfall scenarios compared to a normal rainfall scenario. These findings are of importance for the planning of post-earthquake rehabilitation and regional sustainable development, which considers risk prevention and mitigation

Mapping of risk web-platforms and risk data: collection of good practices

A successful DRR results from the combination of top-down, strategies, with bottom-up, methodological approaches. The top–down approach refers more to administrative directives, organizations, and operational skills linked with the management of the risk and reflects more the policy component. The bottom-up approach is linked to the analyse of the causal factors of disasters, including exposure to hazards, vulnerability, coping capacity, and reflects more the practice component. In the context of disaster science, policy and practice are often disconnected. This is evident in the dominant top-down DRM strategies utilizing global actions on one hand and the context specific nature of the bottom-up approach based on local action and knowledge. A way to bridge the gap between practice and policy is to develop a spatial data infrastructure of the type of GIS web-platforms based on risk mapping. It is a way of linking data information and decision support system (DSS) on a common ground that becomes a “battlefield of knowledge and actions”. This report presents the results of an overview of the risk web-platforms and related risk data used in risk assessment at the level of EU-28. It allows the discovery of the current advancement for risk web infrastructures and capabilities in order to establish a pool of good practices and detection of needs. The outcome of the overview shows the needs in risk web platform developments and tries to recommend capacities that should be prioritized in order to strengthen the link between risk data information and decision support system (DSS). The assessment is based on web search and outcome of diverse disaster risk workshops and conference.JRC.E.1-Disaster Risk Managemen