An approach based on Landsat images for shoreline monitoring to support integrated coastal management - a case study, Ezbet Elborg, Nile Delta, Egypt

Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area

KOMPSAT-3 영상 기반 머신러닝 기법을 활용한 해안선 추출 및 변화 모니터링 시스템 개발에 관한 연구

This paper describes the development of a shoreline extraction and change monitoring system aimed at providing coastal-environment information using high-resolution KOMPSAT series satellite images. For the satellite-image-based shoreline automatic extraction, the machine-learning-based object extraction algorithm was developed, and to utilize the developed algorithm for services, the OpenCV-based monitoring system was developed. In addition, to verify the accuracy of the extracted shoreline information, the reliability of the developed algorithm was verified by comparing the proposed system with the existing diverse image object extraction methods and manually digitized results. First, to develop the high-resolution-image-based shoreline automatic extraction algorithm, the artificial-neural-network-(ANN)-based machine learning technique was used. For the application of this technique, training sample data extracted in advance from KOMPSAT images were created, and the clustering technology was applied to the data. The water and land were divided into binary categories to extract vector-format shorelines. Thus, data with more precise accuracy compared to the existing NDVI-based shoreline data extraction technique can be extracted, and the final vector-format data were calculated, making it possible to maximize their use as quantitative data. That is, the final output was calculated in terms of the type of standardized data in the geographical information category, thus securing the diverse uses of the analysis results. In addition, to develop a monitoring system for its effective utilization, instead of using the existing commercial software, an OpenCV-based system was implemented for extracting, comparing, and analyzing shoreline data. As a result, the system can be used in diverse platform environments, and in particular, the multiple-time image-based data comparison and analysis function makes it possible to conduct quantitative analysis and to monitor shoreline change trends. Thus, the system is believed to be usable as an effective tool for analyzing coastal- environment changes. Coastal-environment changes occur more slowly and are wider in scope compared to land environment and weather changes, making it difficult to define their occurrence time as well as to quantify the coastal damage, if any. The main purpose of analyzing the satellite-image-based global observation information is to monitor the change trends from the macro perspective. Given this purpose, the proposed shoreline data extraction algorithm and the monitoring system using such algorithm are deemed to be suitable as tools for analyzing the coastal-environment change data.1. 서 론 1 1.1 연구배경 및 목적 1 1.2 연구내용 및 방법 3 2. 해안선 변화 모니터링을 위한 위성정보 활용 및 객체추출 방법 5 2.1 해안선 변화 모니터링 관측을 위한 국내외 위성정보 활용 현황 5 2.2 고해상도 영상 기반 해안선 매핑 기법 개발에 관한 국내외 연구 동향 7 3. 다중시기 아리랑 위성영상을 활용한 해안선 변화 모니터링 시스템 구축 15 3.1 위성영상 기반 해안선 추출 알고리즘 구축을 위한 적용 이론 검토 15 3.1.1 정규수분지수(NDVI: Normalized Difference Water Index) 16 3.1.2 정규식생지수(NDVI: Normalized Difference Vegetation Index) 18 3.1.3 에지 검출 기법(Edge Detection Technique) 20 3.1.4 이진화 기법(Thresholding Technique) 23 3.1.5 머신러닝(기계학습) 기법(Machine Learning Technique) 27 3.1.6 모폴로지 필터링(Morphological Fitlering) 31 3.2 위성영상을 활용한 해안선 추출 및 변화 모니터링 시스템 구축 34 3.2.1 위성영상을 활용한 해안선 추출 알고리즘 설계 34 3.2.2 해안선 자동 추출 알고리즘 개발 37 3.2.3 해안선 매핑 프로토타입 개발 41 3.2.4 해안선 매핑 및 변화 모니터링 시스템 개발 46 4. 해안선 추출 및 변화 모니터링 시스템을 활용한 해안선 변화 분석 및 정확도 검증 56 4.1 정확도 검증을 위한 적용 지역 선정 56 4.2 추출결과의 정확도 검증 57 5. 결론 60 감사의 글 62 References 64Maste

YEŞİLIRMAK DELTASI’NDA KIYI EROZYONUNUN DOĞRUSAL REGRESYON ORANI YÖNTEMİYLE ANALİZİ

Bu çalışmada ulusal öneme haiz sulak alanlar kapsamında tescillenen Yeşilırmak Deltası’nın yaklaşık 18,5 km’lik kıyı bölümünde gerçekleşen erozyon uzaktan algılama ve Coğrafi Bilgi Sistemleri (CBS) yardımıyla araştırılmıştır. 1985–2022 periyodunda gerçekleşen kıyı çizgisi değişimlerinin belirlenmesi ve erozyonun derecesinin anlaşılabilmesi için 1985, 1990, 1996, 2001, 2006, 2011, 2017 ve 2022 yıllarına ait Landsat-5 TM/Landsat-8 OLI uydu görüntüleri kullanılmıştır. Uydu görüntülerinden kıyı çizgilerinin belirlenmesinde normalize fark su indeksi (NDWI) ve modifiye normalize fark su indeksi (MNDWI) entegre edilmiştir. Yıllık kıyı çizgisi değişim oranları 1985–2022 periyodunda sekiz farklı yıla ait kıyı çizgilerinden doğrusal regresyon oranı (LRR) yöntemiyle %95 güven düzeyinde hesaplanmış, Yeşilırmak Nehri’nin batı kesimindeki Bölge-1’de maksimum -25,8 m/yıl, doğu kesimindeki Bölge-2’de maksimum - 7,7 m/yıl’a ulaşan erozyon oranı belirlenmiştir. Kıyı çizgisi değişimleri sınıflandırıldığında deltanın %34’ü yüksek, %9’u orta, %18’i düşük derecede olmak üzere %61’inde erozyon gerçekleştiği anlaşılmıştır. 1985– 2022 periyodunda erozyonla kaybedilen alanlar çakıştırma analizi ile belirlenmiş, Bölge-1’de 179,23 ha ve Bölge-2’de 82,22 ha olmak üzere toplam 261,45 ha alanın erozyon ile kaybedildiği görülmüştür. Analiz sonuçları, Yeşilırmak Deltası kıyılarındaki erozyon, birikim ve stabil alanların belirlenerek kıyı dinamiklerinin ve erozyon tehlikesinin daha iyi anlaşılmasına katkı sağlamış ve kıyı çizgisi değişimlerinin belirlenmesinde Landsat görüntüleri ve LRR yönteminin etkinliğini ortaya çıkarmıştı

Estuarine Shoreline Mapping using Object-based Ensemble Analysis, Aerial Imagery, and LiDAR: A Case Study in the Neuse River Estuary, NC

Estuarine shorelines are highly dynamic due to their unique geological history, wave and weather conditions, and human modifications to the shoreline. These interactions are heightened as sea level rise intensifies and extreme storms become more frequent due to climate change. Estuarine shoreline classification maps are critical to understanding the context and magnitude of storm-induced erosion as well as ad hoc efforts to shoreline stabilization. Here, an object-based ensemble analysis is used to map natural and engineered shoreline types observed within the Neuse River Estuary (NRE), NC. Object-based ensemble analysis has emerged as a successful framework to improve image classification but has yet to be tested in classifying an estuarine shoreline environment. This approach used in-situ reference data, high-resolution aerial imagery, and LiDAR point data to train an ensemble of five machine learning algorithms (Random Forest, Support Vector Machine, LibLINEAR, Artificial Neural Network, and k-Nearest Neighbors). The object-based ensemble produced the highest overall classification accuracy at 76.4% (Kappa value = 0.66), 6.3% higher than the top performing pixel-based model, justifying its use to produce the final shoreline classification map. NRE shoreline change and erosion vulnerability were classified using the object-based image analysis and produced comparable erosion rates to those observed in past studies. The object-based ensemble approach was an effective way to map shoreline classifications in the NRE and should continue to be explored within other shoreline management applications