SELECTED PAPER ABSTRACTS

Teaching/Communication/Extension/Profession,

Classification Methods for Mapping Mangrove Extents and Drivers of Change in Thanh Hoa Province, Vietnam during 2005-2018

Mangrove forests have been globally recognised as their vital functions in preventing coastal erosion, mitigating effects of wave actions and protecting coastal habitats and adjacent shoreline land-uses from extreme coastal events. However, these functions are under severe threats due to the rapid growth of population, intensive shrimp farming and the increased intensity of severe storms in Hau Loc and Nga Son districts, Thanh Hoa province. This research was conducted to monitor spatial-temporal changes in mangrove extents using Landsat and Sentinel imageries from 2005 to 2018. Unsupervised and supervised classification methods and vegetation indices were tested to select the most suitable classification method for study sites, then to quantify mangrove extents and their changes in selected years. The findings show that supervised classification was the most suitable in study sites compared to vegetation indices and unsupervised classification. Mangrove forest extents increased by 7.5 %, 38.6 %, and 47.8 % during periods of 2005 - 2010, 2010 - 2015 and 2015 - 2018, respectively. An increase of mangrove extents resulted from national programs of mangrove rehabilitation and restoration during 2005- 2018, increased by 278.0 ha (123.0 %)

Monitoring Changes in Coastal Mangrove Extents Using Multi-Temporal Satellite Data in Selected Communes, Hai Phong City, Vietnam

Mangrove forests are important and known as one of the most productive ecosystems in the tropics. They reduce the impacts of extreme events, provide important breeding grounds for aquatic species and build the resilience of ecosystem-dependent coastal communities. On the contrary, they are also known as one of the most threatened and vulnerable ecosystems worldwide, which have experienced a dramatic decline due to extensive coastal development during the last half-century. Remote sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and its changes, which is reflected by a large number of scientific papers published on this topic. The aim of this study was to investigate the multi-decadal changes of mangrove forests selected communes in Hai Phong city, North Vietnam, based on using Landsat and Sentinel 2 data from 2000 to 2018. The study used these continuous steps: 1) data pre-processing; 2) image classification using Normalized Difference Vegetation Index; 3) accuracy assessments; and 4) multi-temporal change detection and spatial analysis of mangrove forests. The classification maps in comparison with the ground reference data showed the satisfactory agreement with the overall accuracy was higher than 80.0%. From 2000 to 2018, the areas of mangrove forests in the study regions  increased by 584.2 ha in Dai Hop and Bang La communes (Region 1) and by 124.2 ha in Tan Thanh, Ngoc Xuyen and Ngoc Hai communes (Region 2), mainly due to the boom of mangrove planting projects and good mangrove management at the local community level

Optical and radar remote sensing of land use and land cover change in the tropics: An assessment of deforestation and secondary vegetation

Quantification of the direct impact of land use in the tropics on net biotic carbon flux requires estimates of rates of deforestation, pre- and post-disturbance biomass, and fate of the cleared land. Synoptic observations of the extent, persistence, rates of secondary succession, and structure or biomass of regrowing forests would also help constrain estimates of net carbon flux due to tropical land use. While remote sensing applications can provide estimates of the rates of deforestation and the fate of the cleared land (pasture, croplands, or secondary vegetation), techniques for estimating persistence, rates of succession, and biomass of secondary vegetation are needed. We documented the spatial and inter-annual variability in the rates of forest clearing, formation rates and persistence of secondary vegetation for 3 sites in Amazonia and 4 sites in Southeast Asia using Landsat TM data from mid-1980s to late-1990s. Secondary vegetation was a large, rapidly changing pool. Variability in the observed annual rates of deforestation and secondary vegetation formation was high. The transition probabilities of both the formation and clearing of secondary vegetation decreased with age. Persistence of the secondary vegetation pool was also highly variable, likely indicating two distinct land use trajectories: rotational agriculture/pasture maintenance versus abandonment. We also evaluated the spatial, temporal, and noise constraints of JERS SAR data for mapping and monitoring biomass of secondary vegetation in Rondonia, Brazil. Results indicate that quantitative estimates of biomass using single date JERS-1 imagery is problematic because of temporal variability in backscatter due to intrinsic texture, system noise, and environmental effects. However, JERS-1 data are still useful for distinguishing of secondary vegetation stands at different stages of development. Multi-temporal analysis significantly improves biomass estimates to the point where it is possible to map changes in biomass. Slight reductions in the variability in estimates of normalized radar cross-section greatly improve biomass estimation. Merging JERS-1 SAR data with Landsat TM derived age estimates improved characterization of clearings and secondary vegetation in Rondonia by providing information on the relative differences in secondary vegetation development and residual slash with age

Spartan Daily, November 11, 1991

Volume 97, Issue 50https://scholarworks.sjsu.edu/spartandaily/8187/thumbnail.jp

Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska

As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by Whitcomb et al. (2009) using Japanese Earth Resources Satellite (JERS-1) data, we applied the “random forests” classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation) and locational information (latitude, longitude) to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas) to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009): (1) more consistent training data in upland areas; (2) better distribution of training data across all classes by taking a stratified random sample of all available training pixels; and (3) a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles), which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km2, or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA’s wetland monitoring website

The rise of East and Southeast Asians tourists in Europe: the case of Vienna

Travelers from Asia have become a potential alternative for some of the traditional European tourist markets that have stagnated due to the economic crisis. The remarkable recovery of the Japanese market, the exponential growth of Chinese and Korean markets in addition to the high spending power of Thai and Chinese tourists have painted a highly positive scenario for the tourism industry in Vienna. This paper utilizes Vienna as a case study to explore the rise of Asian tourists in Europe. Tourism statistics, media reports and materials of destination marketing organization were analyzed for evaluation of the trends and growth of Asian outbound market to Vienna. The current study contributes to the tourism industry of Austria by highlighting the fragmentations in tourist consumption patterns of tourists from East and Southeast Asia

A habitat assessment to locate tree of heaven [Ailanthus altissima, (Mill.) Swingle] in Mammoth Cave National Park

Invasive, nonnative plants pose a significant threat to national parks. Effective and efficient tools are needed to help managers detect, prioritize and target nonnative plants for control. I used spatial modeling techniques to predict the occurrence of tree of heaven (Ailanthus altissima, (Mill.) Swingle) in Mammoth Cave National Park (MACA), Kentucky. Tree of heaven is known to be a problematic invasive, nonnative plant species and was identified as a priority for control at MACA. I developed a multivariate habitat model to determine optimal habitat for tree of heaven within MACA. Habitat characteristics of 135 known tree of heaven locations were used in combination with seven environmental variables to calculate the predicted probability of occurrence of tree of heaven in MACA using logistic regression analysis. Variables for predicting habitat were created from public records, MACA databases, and a geographic information system (GIS).Twenty-seven a priori models were developed based on the biological requirements of the species and observations of invasion pattern in MACA and the most parsimonious model was selected using Akaike̕s Information Criteria. The seven variables included in the optimal model were derived from soil, site classification, geology, topography, and canopy coverage. I tested the predictive power of the model with independently collected presence and absence data. Ninety seven percent of test locations for tree of heaven were associated with predicted probabilities in the 0-0.30 range. The model improved the probability of finding tree of heaven compared with random searches by approximately 10%. It had poor discrimination (false positive = 0.31, false negative = 0.38, overall reliability = 0.41) and was not well calibrated. Based on its low predictive power, this habitat model could not be recommended for use in managing tree of heaven populations at MACA.Model failure could be attributed to a number of factors and/or combinations of factors including insufficient data, inappropriate scale and the generalist nature of the species. However, results from this study elucidate areas for future research into the applicability of habitat modeling to invasive, nonnative species at local scales

Remote Sensing Applications in Monitoring of Protected Areas

Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue