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Major rivers from the Himalayas carry a high volume of sedimentation, and deposit it across the Bay of Bengal in Bangladesh. This has caused significant changes in the morphology of the bay, including the development of islands across the bay area. However, few studies have been carried out on the morphological changes, especially the development of new islands across the northern Bay of Bengal. This study, therefore, aimed to assess the coastal morphological changes and ecological succession of the newly formed islands of the bay area. We used state of the art cloud computing technologies, using the Google Earth Engine (GEE) platform. Publicly available annual composites of Landsat 8, Landsat ETM+, and TM data from 1989 to 2018 were used for analysis. The findings showed significant changes in the morphology of the coastal area over a period of 30 years. There was a 1.15% increase in land area between 1989 and 2018. New islands were formed across the bay, and a few old islands disappeared between 1989 and 2018. The majority of the offshore islands developed in the estuary of the Meghna River. Among the quickly grown islands, Bhashan Char, Char Nizam, Jahajerchar, and Urir Char are prominent. Initially, the islands appeared as barren areas without any vegetation, but different types of vegetation have been observed growing on the newly formed islands recently. The findings of this study are important for the conservation and development planning of newly formed islands.
Kabir Uddin; Nishanta Khanal; Sunita Chaudhary; Sajana Maharjan; Rajesh Bahadur Thapa. Coastal morphological changes: Assessing long-term ecological transformations across the northern Bay of Bengal. Environmental Challenges 2020, 1, 100001 .
AMA StyleKabir Uddin, Nishanta Khanal, Sunita Chaudhary, Sajana Maharjan, Rajesh Bahadur Thapa. Coastal morphological changes: Assessing long-term ecological transformations across the northern Bay of Bengal. Environmental Challenges. 2020; 1 ():100001.
Chicago/Turabian StyleKabir Uddin; Nishanta Khanal; Sunita Chaudhary; Sajana Maharjan; Rajesh Bahadur Thapa. 2020. "Coastal morphological changes: Assessing long-term ecological transformations across the northern Bay of Bengal." Environmental Challenges 1, no. : 100001.
Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.
Nishanta Khanal; Mir Matin; Kabir Uddin; Ate Poortinga; Farrukh Chishtie; Karis Tenneson; David Saah. A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL. Remote Sensing 2020, 12, 2888 .
AMA StyleNishanta Khanal, Mir Matin, Kabir Uddin, Ate Poortinga, Farrukh Chishtie, Karis Tenneson, David Saah. A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL. Remote Sensing. 2020; 12 (18):2888.
Chicago/Turabian StyleNishanta Khanal; Mir Matin; Kabir Uddin; Ate Poortinga; Farrukh Chishtie; Karis Tenneson; David Saah. 2020. "A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL." Remote Sensing 12, no. 18: 2888.
Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using primitive map layers. Primitive map layers are a suite of biophysical and end member maps, with land cover primitives representing the raw information needed to make decisions in a dichotomous key for land cover classification. We present best practices to create and assemble primitives from optical satellite using computing technologies, decision tree logic and Monte Carlo simulations to integrate their uncertainties. The concept is presented in the context of a regional land cover map based on a shared regional typology with 18 land cover classes agreed on by stakeholders from Cambodia, Laos PDR, Myanmar, Thailand, and Vietnam. We created annual map and uncertainty layers for the period 2000–2017. We found an overall accuracy of 94% when taking uncertainties into account. RLCMS produces consistent time series products using free long term historical Landsat and MODIS data. The customizable architecture can include a variety of sensors and machine learning algorithms to create primitives and the best suited smoothing can be applied on a primitive level. The system is transferable to all regions around the globe because of its use of publicly available global data (Landsat and MODIS) and easily adaptable architecture that allows for the incorporation of a customizable assembly logic to map different land cover typologies based on the user's landscape monitoring objectives
David Saah; Karis Tenneson; Ate Poortinga; Quyen Nguyen; Farrukh Chishtie; Khun San Aung; Kel N. Markert; Nicholas Clinton; Eric R. Anderson; Peter Cutter; Joshua Goldstein; Ian W. Housman; Biplov Bhandari; Peter V. Potapov; Mir Matin; Kabir Uddin; Hai N. Pham; Nishanta Khanal; Sajana Maharjan; Walter L. Ellenberg; Birendra Bajracharya; Radhika Bhargava; Paul Maus; Matthew Patterson; Africa Ixmucane Flores-Anderson; Jeffrey Silverman; Chansopheaktra Sovann; Phuong M. Do; Giang V. Nguyen; Soukanh Bounthabandit; Raja Ram Aryal; Su Mon Myat; Kei Sato; Erik Lindquist; Marija Kono; Jeremy Broadhead; Peeranan Towashiraporn; David Ganz. Primitives as building blocks for constructing land cover maps. International Journal of Applied Earth Observation and Geoinformation 2019, 85, 101979 .
AMA StyleDavid Saah, Karis Tenneson, Ate Poortinga, Quyen Nguyen, Farrukh Chishtie, Khun San Aung, Kel N. Markert, Nicholas Clinton, Eric R. Anderson, Peter Cutter, Joshua Goldstein, Ian W. Housman, Biplov Bhandari, Peter V. Potapov, Mir Matin, Kabir Uddin, Hai N. Pham, Nishanta Khanal, Sajana Maharjan, Walter L. Ellenberg, Birendra Bajracharya, Radhika Bhargava, Paul Maus, Matthew Patterson, Africa Ixmucane Flores-Anderson, Jeffrey Silverman, Chansopheaktra Sovann, Phuong M. Do, Giang V. Nguyen, Soukanh Bounthabandit, Raja Ram Aryal, Su Mon Myat, Kei Sato, Erik Lindquist, Marija Kono, Jeremy Broadhead, Peeranan Towashiraporn, David Ganz. Primitives as building blocks for constructing land cover maps. International Journal of Applied Earth Observation and Geoinformation. 2019; 85 ():101979.
Chicago/Turabian StyleDavid Saah; Karis Tenneson; Ate Poortinga; Quyen Nguyen; Farrukh Chishtie; Khun San Aung; Kel N. Markert; Nicholas Clinton; Eric R. Anderson; Peter Cutter; Joshua Goldstein; Ian W. Housman; Biplov Bhandari; Peter V. Potapov; Mir Matin; Kabir Uddin; Hai N. Pham; Nishanta Khanal; Sajana Maharjan; Walter L. Ellenberg; Birendra Bajracharya; Radhika Bhargava; Paul Maus; Matthew Patterson; Africa Ixmucane Flores-Anderson; Jeffrey Silverman; Chansopheaktra Sovann; Phuong M. Do; Giang V. Nguyen; Soukanh Bounthabandit; Raja Ram Aryal; Su Mon Myat; Kei Sato; Erik Lindquist; Marija Kono; Jeremy Broadhead; Peeranan Towashiraporn; David Ganz. 2019. "Primitives as building blocks for constructing land cover maps." International Journal of Applied Earth Observation and Geoinformation 85, no. : 101979.
During the last few decades, a large number of people have migrated to Kathmandu city from all parts of Nepal, resulting in rapid expansion of the city. The unplanned and accelerated growth is causing many environmental and population management issues. To manage urban growth efficiently, the city authorities need a means to be able to monitor urban expansion regularly. In this study, we introduced a novel approach to automatically detect urban expansion by leveraging state-of-the-art cloud computing technologies using the Google Earth Engine (GEE) platform. We proposed a new index named Normalized Difference and Distance Built-up Index (NDDBI) for identifying built-up areas by combining the LandSat-derived vegetation index with distances from the nearest roads and buildings analysed from OpenStreetMap (OSM). We also focused on logical consistencies of land-cover change to remove unreasonable transitions supported by the repeat photography. Our analysis of the historical urban growth patterns between 2000 and 2018 shows that the settlement areas were increased from 63.68 sq km in 2000 to 148.53 sq km in 2018. The overall accuracy of mapping the newly-built areas of urban expansion was 94.33%. We have demonstrated that the methodology and data generated in the study can be replicated to easily map built-up areas and support quicker and more efficient land management and land-use planning in rapidly growing cities worldwide.
Nishanta Khanal; Kabir Uddin; Mir A. Matin; Karis Tenneson. Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu. Remote Sensing 2019, 11, 2296 .
AMA StyleNishanta Khanal, Kabir Uddin, Mir A. Matin, Karis Tenneson. Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu. Remote Sensing. 2019; 11 (19):2296.
Chicago/Turabian StyleNishanta Khanal; Kabir Uddin; Mir A. Matin; Karis Tenneson. 2019. "Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu." Remote Sensing 11, no. 19: 2296.