7 research outputs found
TECHNIQUE FOR IDENTIFYING BURNED VEGETATION AREA USING LANDSAT 8 DATA
During the last two decades, forest and land fire is a catastrophic event that happens almost every year in Indonesia. Therefore, it is necessary to develop a technic to monitor forest fires using satellite data to obtain the latest information of burned area in a large scale area. The objective of this research is to develop a method for burned area mapping that happened between two Landsat 8 data recording on August 13rd and September 14th 2015. Burned area was defined as a burned area of vegetation. The hotspot distribution during the period August - September 2015 was used to help visual identification of burned area on the Landsat image and to verify the burned area resulted from this research. Samples were taken at several land covers to determine the spectral pattern differences among burned area, bare area and other land covers, and then the analysis was performed to determine the suitable spectral bands or indices and threshold values that will be used in the model. Landsat recorded on August 13rd before the fire was extracted for soil, while Landsat recorded on September 14th after the fire was extracted for burned area. Multi-temporal analysis was done to get the burned area occurring during the certain period. The results showed that the clouds could be separated using combination of ocean blue and cirrus bands, the burned area was extracted using a combination of NIR and SWIR band, while soil was extracted using ratio SWIR / NIR. Burned area obtained in this study had high correlation with the hotspot density of MODIS with the accuracy was around 82,4 %
Standardized time-series and interannual phenological deviation : new techniques for burned-area detection using long-term MODIS-NBR datase
Typically, digital image processing for burned-areas detection combines the use of a spectral index and the seasonal differencing method. However, the seasonal differencing has many errors when applied to a long-term time series. This article aims to develop and test two methods as an alternative to the traditional seasonal difference. The study area is the Chapada dos Veadeiros National Park (Central Brazil) that comprises different vegetation of the Cerrado biome. We used the MODIS/Terra Surface Reflectance 8-Day composite data, considering a 12-year period. The normalized burn ratio was calculated from the band 2 (250-meter resolution) and the band 7 (500-meter resolution reasampled to 250-meter). In this context, the normalization methods aim to eliminate all possible sources of spectral variation and highlight the burned-area features. The proposed normalization methods were the standardized time-series and the interannual phenological deviation. The standardized time-series calculate for each pixel the z-scores of its temporal curve, obtaining a mean of 0 and a standard deviation of 1. The second method establishes a reference curve for each pixel from the average interannual phenology that is subtracted for every year of its respective time series. Optimal threshold value between burned and unburned area for each method was determined from accuracy assessment curves, which compare different threshold values and its accuracy indices with a reference classification using Landsat TM. The different methods have similar accuracy for the burning event, where the standardized method has slightly better results. However, the seasonal difference method has a very false positive error, especially in the period between the rainy and dry seasons. The interannual phenological deviation method minimizes false positive errors, but some remain. In contrast, the standardized time series shows excellent results not containing this type of error. This precision is due to the design method that does not perform a subtraction with a baseline (prior year or average phenological curve). Thus, this method allows a high stability and can be implemented for the automatic detection of burned areas using long-term time series
Increasing Spatial Detail of Burned Scar Maps Using IRS‑AWiFS Data for Mediterranean Europe
A two stage burned scar detection approach is applied to produce a burned scar map for Mediterranean Europe using IRS-AWiFS imagery acquired at the end of the 2009 fire season. The first stage identified burned scar seeds based on a learning algorithm (Artificial Neural Network) coupled with a bootstrap aggregation process. The second stage implemented a region growing process to extend the area of the burned scars. Several ancillary datasets were used for the accuracy assessment and a final visual check was performed to refine the burned scar product. Training data for the learning algorithm were obtained from MODIS-based polygons, which were generated by the Rapid Damage Assessment module of the European Forest Fire Information System. The map produced from this research is the first attempt to increase the spatial detail of current burned scar maps for the Mediterranean region. The map has been analyzed and compared to existing burned area polygons from the European Forest Fire Information System. The comparison showed that the IRS-AWiFS-based burned scar map improved the delineation of burn scars; in addition the process identified a number of small burned scars that were not detected on lower resolution sensor data. Nonetheless, the results do not clearly support the improved capability for the detection of smaller burned scars. A number of reasons can be provided for the under-detection of burned scars, these include: the lack of a full coverage and cloud free imagery, the time lag between forest fires and image acquisition date and the occurrence of fires after the image acquisition dates. On the other hand, the limited spectral information combined with the presence of undetected cloud shadows and shaded slopes are reasons for the over-estimation of small burned scars
Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms
Forests are one of the major carbon sinks that significantly contribute towards achieving
targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG)
emissions. In order to contribute to regular National Inventory Reporting, and as part of
the on-going development of the Irish national GHG reporting system (CARBWARE),
improvements in characterisation of changes in forest carbon stocks have been
recommended to provide a comprehensive information flow into CARBWARE. The Irish
National Forest Inventory (NFI) is updated once every six years, thus there is a need for
an enhanced forest monitoring system to obtain annual forest updates to support
government agencies and forest management companies in their strategic decision making
and to comply with international GHG reporting standards. Sustainable forest
management is imperative to promote net carbon absorption from forests. Based on the
NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric
CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become
a net emitter of carbon. Disturbances from human induced activities such as clear felling,
thinning and deforestation results in carbon emissions back into the atmosphere. Funded
by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study
focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR)
satellite based sensors for monitoring changes in the small stand forests of Ireland.
Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2
PALSAR-2 sensors have been used to map forest areas and characterise the different
disturbances observed within three different regions of Ireland. Forest mapping and
disturbance characterisation was achieved by combining the machine learning supervised
Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis
(ISODATA) classification techniques. The lack of availability of ground truth data
supported use of this unsupervised approach which forms natural clusters based on their
multi-temporal signatures, with divergence statistics used to select the optimal number of
clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial
where there is a dearth of ground-based information. When applied to the forests, mapped
with an accuracy of up to 97% by RF, the ISODATA technique successfully identified
the unique multi-temporal pattern associated with clear-fells which exhibited a decrease
of 4 to 5 decibels (dB) between the images acquired before and after the event. The
clustering algorithm effectively highlighted the occurrence of other disturbance events
within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas
of tree growth and afforestation.
A highlight of the work is the successful transferability of the algorithm, developed using
ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential
continuity of annual forest monitoring. The higher spatial and radiometric resolutions of
ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to
ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS
PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images.
Moreover, even with some different backscatter characteristics of images acquired in
different seasons, similar signature patterns between the sensors were retrieved that helped
to define the cluster groups, thus demonstrating the robustness of the algorithm and its
successful transferability.
Having proven the potential to monitor forest disturbances, the results from both the
sensors were used to detect deforestation over the time period 2007-2016. Permanent
land-use changes pertaining to conversion of forests to agricultural lands and windfarms
were identified which are important with respect to forest monitoring and carbon reporting
in Ireland.
Overall, this work has presented a viable approach to support forest monitoring operations
in Ireland. By providing disturbance information from SAR, it can supplement projects
working with optical images which are generally limited by cloud cover, particularly in
parts of northern, western and upland Ireland. This approach adds value to ground based
forest monitoring by mapping distinct forests over large areas on an annual basis. This
study has demonstrated the ability to apply the algorithm to three different study areas,
with a vision to operationalise the algorithm on a national scale. The main limitations
experienced in this study were the lack of L-band SAR data availability and reference
datasets. With typically only one image acquired per year, and discrepancies and
omissions existing within reference datasets, understanding the behaviour of certain
cluster groups representing disturbances was challenging. However, this approach has
addressed some issues within the reference datasets, for example locating areas for which
a felling licence was granted but where trees were never cut, by providing detailed
systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B,
P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited
SAR image acquisitions provided more images per year are available, especially during
the summer months
6. Uluslararası Öğrenciler Fen Bilimleri Kongresi: Tam Metin Bildiriler Kitabı, 20-21 Mayıs 2022
Çevrimiçi (X, 434 Sayfa; 26 cm.)Değerli Katılımcılar, Meslektaşlarım ve Uluslararası Öğrenciler, 6. Uluslararası Öğrenciler Fen Bilimleri Kongresi Tam metin Kitabını etkinliğin yazarlarına ve katılımcılarına sunmak bizler için büyük bir onur ve ayrıcalıktır. Bunu yararlı, heyecan ve ilham verici bulacağınızı umuyoruz. Son beş yıldır çeşitli bilim dallarında çalışan genç uluslararası araştırmacıları bir araya getirmek amacıyla kongrelerimizi düzenledik ve bu hepimizi gerçekten motive etti. Küresel Covid-19 pandemisinin ardından altıncı kongreyi, yüz yüze canlı ve çevrimiçi sanal oturumları birleştirerek karma bir etkinlik olarak düzenledik. Kongrenin ilk günü olan 20 Mayıs’ta, 100'den fazla katılımcıyı bir araya getiren ve tamamen yüz yüze sekiz oturum gerçekleştirildi. Bu ilk günün sabahında davetli konuşmacılarımız tarafından iki ilgi çekici sunum yapıldı: Ege Üniversitesi'nden Prof. Dr. Bahattin Tanyolaç “Covid-19 Aşıları” ve Gebze Teknik Üniversitesi'nden Dr. Yakup Genç “Metaverse” hakkında konuştular. Etkinliğin ikinci gününde dokuz çevrimiçi oturum Zoom üzerinden gerçekleştirildi ve YouTube üzerinden canlı olarak yayınlandı; bu oturumların videolarına Youtube kanalımızdan ulaşabilirsiniz. Altıncı kongremizi de yine büyük bir istek ve heyecanla gerçekleştirdik. İki gün süren kongrede, yirmi sekiz farklı ülkeden yüz elliyi aşkın genç araştırmacı ve akademisyen bir araya geldi ve on yedi oturumda toplam doksan yedi bildiri sunuldu. Bildirilerin kırk yedi tanesi canlı yüz yüze, elli tanesi ise çevrimiçi olarak sunuldu. Öte yandan, elli iki bildiri uluslararası (Türk olmayan) katılımcılar tarafından, kırk beş bildiri ise Türk katılımcılar tarafından sunuldu. Kongre, özellikle fen bilimleri alanında eğitimlerine devam eden uluslararası öğrencilerin ve genç akademisyenlerin önlerindeki akademik camia ile etkileşimlerini gayet samimi bir ortam sunarak teşvik ederken, yeni ve güncel çalışmalarını sunmaları ve tartışmaları için de güzel bir fırsat sağlamış oldu. Onların katkıları sayesinde Kongre olabildiğince seçkin ve nitelikli bir düzeye ulaşmış oldu. Kongre, Ziraat Mühendisliği, Mimarlık, Biyoloji ve Biyomühendislik, Kimya ve Kimya Mühendisliği, İnşaat Mühendisliği, Bilgisayar Bilimi ve Mühendisliği, Elektrik, Elektronik ve Haberleşme Mühendisliği, Enerji, Gıda Mühendisliği, Jeoloji Mühendisliği, Makine Mühendisliği, Matematik, Malzeme Bilimi, Metalürji ve Malzeme Mühendisliği, Mekatronik Mühendisliği, Nanoteknoloji, Fizik, Tekstil Mühendisliği, Kentsel ve Bölgesel Planlama, vb. çok çeşitli konulardaki son gelişmeleri tartışmak için keyifli bir ortam sağladı. Tüm katılımcılara kongre programımıza ve dolayısıyla tam metin kitabımıza yaptıkları katkılardan dolayı teşekkür ederiz. Ayrıca verdikleri destek ile bu kongrenin gerçekleşmesine katkı sağlayan İzmir Kâtip Çelebi Üniversitesi’ne, Uluslararası Öğrenci Dernekleri Federasyonu’na (UDEF), Türkiye Bilimsel ve Teknolojik Araştırma Kurumu’na (TÜBİTAK) ve ana organizatörümüz İzmir Uluslararası Misafir Öğrenci Derneği'ne teşekkürlerimizi arz ederiz. Organizasyon komitemize ve etkinlik süresince gönüllü olarak çalışan tüm öğrencilere içten şükran ve takdirlerimi sunuyorum. Bu kongre dizisinin devam eden başarısı, 2023'te düzenlenmeyi hedeflediğimiz 7. Uluslararası Öğrenciler Fen Bilimleri Kongresi için planlamanın artık güvenle ilerleyebileceği anlamına geliyor; bu kongremiz de muhtemelen hem çevrimiçi hem de yüz yüze olacak. Katkılarından dolayı tüm yazarlara, katılımcılara ve gönüllülere teşekkür ederiz. Prof. Dr. Mehmet Çevik Kongre Başkan