Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model

Multi-temporal remote sensing imagery can be used to explore how mangrove assemblages are changing over time and facilitate critical interventions for ecological sustainability and effective management. This study aims to explore the spatial dynamics of mangrove extents in Palawan, Philippines, specifically in Puerto Princesa City, Taytay and Aborlan, and facilitate future predictions for Palawan using the Markov Chain model. The multi-date Landsat imageries during the period 1988–2020 were used for this research. The support vector machine algorithm was sufficiently effective for mangrove feature extraction to generate satisfactory accuracy results (>70% kappa coefficient values; 91% average overall accuracies). In Palawan, a 5.2% (2693 ha) decrease was recorded during 1988–1998 and an 8.6% increase in 2013–2020 to 4371 ha. In Puerto Princesa City, a 95.9% (2758 ha) increase was observed during 1988–1998 and 2.0% (136 ha) decrease during 2013–2020. The mangroves in Taytay and Aborlan both gained an additional 2138 ha (55.3%) and 228 ha (16.8%) during 1988–1998 but also decreased from 2013 to 2020 by 3.4% (247 ha) and 0.2% (3 ha), respectively. However, projected results suggest that the mangrove areas in Palawan will likely increase in 2030 (to 64,946 ha) and 2050 (to 66,972 ha). This study demonstrated the capability of the Markov chain model in the context of ecological sustainability involving policy intervention. However, as this research did not capture the environmental factors that may have influenced the changes in mangrove patterns, it is suggested adding cellular automata in future Markovian mangrove modelling

Assessment of the Socio-Environmental Impacts of the Urban Expansion using GIS and Remote Sensing in the City of Guayaquil, Ecuador.

This thesis investigates the impacts of the urban expansion from 1990 to 2010 in Guayaquil, Ecuador using geospatial technologies. It incorporates census and land cover data to identify the social and environmental repercussions through Hot Spot Analysis, land cover classification, and Markov chains model

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Monitoring and modelling disturbances to the Niger Delta mangrove forests

The Niger River Delta provides numerous ecosystem services (ES) to local populations and holds a wealth of biodiversity. Nevertheless, they are under threat of degradation and loss mainly due to the population increase and oil and gas extraction activities. Monitoring mangrove vegetation change and understanding the dynamics related with these changes is crucial for the short and longer-term sustainability of the Niger Delta Region (NDR) and its mangrove forests. Over the last two decades, open access remote sensing data, together with technological and algorithmic advancements, have provided the ability to monitor land cover over large areas through space and time. However, the analysis of land cover dynamics over the NDR using freely available optical remote sensing data, such as Landsat, remains challenging due to the gaps in the archive associated with the West African region and the issue of cloud contamination over the wet tropics. This thesis applies state-art-of-the-art remote sensing techniques and integrated modelling approaches to provide reliable information relating to monitoring and modelling of land cover change in the NDR, focusing on its mangrove forests. Spectral-temporal metrics from all available Landsat images were used to accurately map land cover in three time points, using a Random Forests machine learning classification model. The performance of the classification was tested when L-band radar data are added to the Landsat-based metrics. Results showed that Landsat based metrics are sufficient in mapping land cover over the study region with high overall classification accuracies over the three time points (1988, 2000, and 2013) and degraded mangroves were accurately mapped for the first time. Two additional assessments: a change intensity analysis for the entire NDR and, fragmentation analysis focusing on mangrove land cover classes were carried out for the first time ever. The drivers of mangrove degradation were assessed using a Multi-layer Perceptron, Artificial Neutral Networks (MLP-ANN) algorithm. The results reveal that built-up infrastructure variables were the most important drivers of mangrove degradation between 1988 and 2000, whilst oil and gas infrastructure variables were the most important drivers between 2000 and 2013. Results also show that population density was the least important driver of mangrove degradation over the two study periods. Future land cover changes and mangrove degradation were predicted under two business-as-usual scenarios in the short (2026) and longer-term (2038) using a Multi-Layer Perceptron neutral network and Markov chain (MLP-ANN+MC) model. The model’s accuracy was assessed using the highly-accurate land cover classification of 2013. Results show that that mangrove forest and woodlands (lowland and freshwater forests) are demonstrating a net loss, whilst the built-up areas and agriculture are indicating a net increase in both the short and longer-term scenarios. However, degraded mangroves are demonstrating a net increase in the short-term scenario. Interestingly, in the longer-term scenario, more than double the net increase of mangroves degraded in the short-term scenario, are predicted to recover to their healthier state. The thesis results could provide useful information for planning conservation measures for sustainable mangrove forest management of the entire NDR

Recent Progress in Urbanisation Dynamics Research

This book is dedicated to urbanization, which is observed every day, as well as the methods and techniques of monitoring and analyzing this phenomenon. In the 21st century, urbanization has gained momentum, and the awareness of the significance and influence of this phenomenon on our lives make us take a closer look at it not only with curiosity, but also great attention. There are numerous reasons for this, among which the economy is of special significance, but it also has many results, namely, economic, social, and environmental. First of all, it is a spatial phenomenon, as all of the aspects can be placed in space. We would therefore like to draw special attention to the results of urbanization seen on the Earth's surface and in the surrounding space. The urbanization–land relation seems obvious, but is also interesting and multi-layered. The development of science and technology provides a lot of new tools for observing urbanization, as well as the analyses and inference of the phenomenon in space. This book is devoted to in-depth analysis of past, present and future urbanization processes all over the world. We present the latest trends of research that use experience in the widely understood geography of the area. This book is focused on multidisciplinary phenomenon, i.e., urbanization, with the use of the satellite and photogrammetric observation technologies and GIS analyses

Geographic Information Systems and Science

Geographic information science (GISc) has established itself as a collaborative information-processing scheme that is increasing in popularity. Yet, this interdisciplinary and/or transdisciplinary system is still somewhat misunderstood. This book talks about some of the GISc domains encompassing students, researchers, and common users. Chapters focus on important aspects of GISc, keeping in mind the processing capability of GIS along with the mathematics and formulae involved in getting each solution. The book has one introductory and eight main chapters divided into five sections. The first section is more general and focuses on what GISc is and its relation to GIS and Geography, the second is about location analytics and modeling, the third on remote sensing data analysis, the fourth on big data and augmented reality, and, finally, the fifth looks over volunteered geographic information.info:eu-repo/semantics/publishedVersio