Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years

Abstract With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future

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

Applying multi-temporal landsat satellite data and markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.</jats:p

Vulnerability of mangroves to sea level rise in Qatar: Assessment and identification of vulnerable mangroves areas

Qatar is one of few countries in Arabian Gulf where mangrove ecosystem exist. They are essential number of ecosystem function; however, this valuable ecosystem is threatened by both anthropogenic and global climatic factors. This study is aimed at investigating the vulnerability of mangroves resulting from the rise in sea level. Remote sensing, GIS and soil analysis were used to achieve this assessment. Four main research questions including the change in mangrove area over time, the endangered area by sea level rise, the potentially expected migration area and the management strategies were answered. Thus the first objective of identifying potentially endangered mangrove areas by sea level rise in Qatar and second objective of enhancing the mangrove protection and resilience to sea level rise were achieved. The results of comparative analysis of satellite images show a 50 % increase of the growth of mangrove ecosystems. Comparison of soil within mangrove and outside mangrove area showed the same pH values with slightly different salinity, and similar soil Type. This will positively affect the migration process for existing mangroves. High exposure to sea level rise is estimated from overlaying recent mangrove layer over elevation layers of expected sea level rise scenarios. The result showed that endangered mangrove areas were 35% and 45% with 0.52 m and 0.74 m sea level rise respectively. Outward migration using spatial techniques was observed, while new conservation strategies are recommended to minimize the vulnerability of mangroves

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

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

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future