Satellite-based assessment of the August 2018 flood in parts of Kerala, India

From 1 June to 29 August 2018, Kerala, a state in southwestern India, recorded 36% excess rainfall than normal levels, leading to widespread floods and landslides events and resulting in 445 deaths. In this study, satellite-based data were used to map the flood inundation in the districts of Thrissur, Ernakulam, Alappuzha, Idukki and Kottayam. Specifically, flood delineation was enabled with Sentinel-1A radar data of 21 August 2018 and was compared with an average pre-flood, water-cover map based on Modified Normalized Difference Water Index (MNDWI) that was developed using a January and February 2018 Sentinel-2A dataset. A 90% increase in water cover was observed during the August 2018 flood event. Low lying areas in the coastal plains of Kuttanad and the Kole lands of Thrissur, had marked a rise of up to 5 and 10 m of water, respectively, during this deluge. These estimates are conservative as that the flood waters had started receding prior to the August 21 Sentinel-1A imagery

Analysis of Sensor Imaging and Field-Validation for Monitoring, Evaluation and Control Future Flood Prone Areas along River Niger and Benue Confluence Ecology, Lokoja, Nigeria

The study area often suffered from flood for the last two year resulting to ecological damages including farmlands, infrastructures, property damage, loss of life and degradation of land-cover. Flood prone areas assessment is conducted using sensor data from space-borne optical sensors with cross-validation by ground-truthing the study area along the two major rivers that converge at Lokoja, otherwise called river-confluence. Maximum likelihood classification (MLC) and ISO-clustering unsupervised classification method of Arcmap-10.1 using NigeriaSat-1 data is applied to the regimes of up-stream and down-stream of River Niger and River Benue respectively. Based on ground truthing of the study areas, classification of inundated areas closely connected with actual flood prone area was developed. The results of the classifications of flood prone areas were displayed on satellite imagery, of which the percentage differences of change detected from variations of 16 class of land-use (LU) and land-cover (LC) using optical sensor shows that wetland flood plain comprising of runoffs-routes and lowland areas recorded the highest of 14.42% using MLC and 16.02% using ISO-DATA. In the final analysis, the classification accuracy conducted shows that the ecology of flood prone areas can be adequately classified using MLC (54.89%) and ISO-clustering unsupervised classification (45.11%). In the same vein, the result of regression function shows high correlation coefficient of 0.6242 (62%) and high strength in their relationship of which the potential flood runoff-route did correlate with the state of the location of the study area. It is anticipated that remote-sensing data integrated from optical sensors could be used to supplement up-stream, down-stream and runoffs-route to monitor, evaluate and detect floods prone areas. It is therefore significant that government and relevant agencies adopts these findings to help in the monitoring, evaluating and control of future ecological disasters. Keywords:Analysis, lokoja,river niger, river benue, confluence, monitor, evaluate, control, ecology, flood, spatial, tempora

Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan

Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan. First; the optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) was processed to delineate the extent of the 2010 flood along Indus River; Pakistan. Moreover; the all-weather all-time capability of higher resolution imagery from the Advanced Synthetic Aperture Radar (ASAR) is used to monitor flooding in the lower Indus river basin. Then a proxy for river discharge from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA’s Aqua satellite and rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) are used to study streamflow time series and precipitation patterns. The AMSR-E detected water surface signal was cross-validated with ground-based river discharge observations at multiple streamflow stations along the main Indus River. A high correlation was found; as indicated by a Pearson correlation coefficient of above 0.8 for the discharge gauge stations located in the southwest of Indus River basin. It is concluded that remote-sensing data integrated from multispectral and microwave sensors could be used to supplement stream gauges in sparsely gauged large basins to monitor and detect floods.JRC.G.2-Global security and crisis managemen

DINAMIKA GENANGAN PESISIR JAKARTA BERDASARKAN DATA MULTI-TEMPORAL SATELIT MENGGUNAKAN INDEKS AIR DAN POLARISASI RADAR

Combining baseline data of remote sensing systems active and passive has many advantages in monitoring coastal inundation dynamically. It has advanced the surface water information gaps in coastal areas, especially areas covered by clouds and shadows. The main objective of this study was to assess the dynamics of coastal inundation in Jakarta based on multi-temporal data optics of Landsat 8 and Synthetic Aperture Radar (SAR) Sentinel 1A. The method of this research used two water index algorithms. They are Modified Normalized Difference Water Index (MNDWI) and Dynamic Surface Water Extent (DSWE) based on spectral reflectance values and empirical formulas. The other method is using the coefficient backscattering of water from a single polarization of Vertical Verticals (VV) and Vertical Horizontal (VH). The study results show that the use of both satellite data baseline of 8, 9, 15, and 16 days is quite effective, applying inundation dynamics for 8-49 days. Based on the threshold value of MNDWI > 0.123 and the backscattering coefficient of -19dB are quite efficient to extract satellite data information. The empirical algorithms result in the feature of inundation, especially along the coastal dikes, reservoirs, mangrove ecosystems, and built-up lands. Satellite monitoring results show that the peak of inundation occurred on 30 May 2016 and was still visible on 15 June 2016. The combination of remote sensing methods is quite effective and efficient for monitoring inundation dynamically.Kombinasi baseline data pengindraan jauh sistem aktif dan pasif memiliki banyak keuntungan dalam pemantauan dinamika genangan pesisir. Kedua jenis sensor satelit mengatasi kesenjangan informasi genangan, terutama pada area yang ditutupi awan/bayangan. Tujuan utama penelitian ini adalah untuk mengkaji dinamika genangan di wilayah pesisir Jakarta berdasarkan data multi-temporal sensor optik dari Landsat 8 dan Synthetic Aperture Radar (SAR) Sentinel 1A. Metode penelitian ini menggunakan dua algoritma indeks air. Algoritma tersebut yaitu Modified Normalized Difference Water Index (MNDWI) dan Dynamic Surface Water Extent (DSWE) berdasarkan nilai spektral reflektansi dan formula empirik. Metode lainnya adalah menggunakan nilai rata-rata koefisien backscatter air dari analisis polarisasi tunggal Vertikal Vertikal (VV) dan Vertikal Horisontal (VH). Hasil studi menunjukkan bahwa penggunaan kedua tipe data satelit dengan baseline data 8, 9, 15 dan 16 hari cukup efektif memantau dinamika genangan selama 8-49 hari, termasuk area yang tertutup awan dan bayangan. Berdasarkan nilai threshold dari MNDWI >0,123 dan koefisien backscattering air -19dB cukup efisien digunakan untuk mengesktrak informasi data satelit. Algoritma empiris tersebut menghasilkan kenampakan genangan, terutama di sepanjang tanggul pantai, waduk, ekosistem mangrove dan lahan terbangun. Hasil pemantauan satelit menunjukkan bahwa puncak genangan terjadi pada 30 Mei 2016 dan masih terlihat pada 15 Juni 2016. Kombinasi metode pengindraan jauh tersebut cukup efektif dan efisien untuk memantau genangan secara dinamis

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data

Mapeamento das áreas inundáveis do Médio São Francisco utilizando técnicas de processamento digital de imagens de sensoriamento remoto e modelo HAND

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2019.As áreas inundáveis desempenham funções ecológicas primordiais para a manutenção do equilíbrio ecológico dos ecossistemas aquáticos e terrestres. Ademais as áreas inundáveis são fundamentais para o sustento de diversas atividades humanas. No entanto, essas áreas vêm sofrendo diversos distúrbios decorrentes das ações antrópicas. O presente trabalho visou realizar a análise da dinâmica fluvial em um trecho do rio São Francisco, localizado entre os municípios de Barra, Pilão Arcado e Xique-Xique, Bahia. Dessa forma, foi calculada a Linha Média das Enchentes Ordinárias (LMEO) e aplicadas técnicas de processamento digital nas imagens Landsat-8/OLI-TIRS e Sentinel-1 (SAR). Os índices espectrais MNDWI, NDWI e AWEI foram aplicados em duas imagens Landsat-8, uma representando a cota do rio próxima à LMEO e a outra um período de seca. A detecção dos alvos de água nas imagens foi feita a partir da técnica de threshould. O índice MNDWI demonstrou maior valor de acurácia, com índice kappa superior a 0,9. Também foi realizada uma análise multitemporal da dinâmica fluvial entre os anos de 2005 e 2019, empregando imagens Landsat 5 e Landsat 8. Em seguida, foram obtidas duas imagens Sentinel-1 representando a cota máxima e mínima do rio, entre os anos 2016 e 2017. Aplicou-se a técnica de threshould para a classificação da água nas imagens. O maior valor de acurácia demonstrado pelo índice kappa nas imagens Sentinel-1 foi 0,47. Além disso, foi gerado o modelo digital HAND da região e delimitada os terrenos marginais, a fim de realizar o levantamento das áreas inundáveis. Por último, foram realizadas simulações de cotas do rio no modelo HAND, as quais demonstraram valor de acurácia superior a 96,67%.Wetlands play a key role in ecological balance process of aquatic and terrestrial ecosystems. In addition, wetlands are crucial because support various human activities. However, anthropogenic actions have impacted these areas. The objective of the present study was to map the wetlands in a section of São Francisco River, using radar (SAR) and optical image processing. The study area is located between the counties of Barra, Pilão Arcado and Xique-Xique, Bahia. Were employed Sentinel-1, Landsat-8/OLI and Landsat-5/TM images. A HAND model was also generated from DEM to map the wetlands. Data from the São Francisco historical series were used to calculate the Limit from Ordinary Flood (LFOF). MNDWI, NDWI and AWEI were applied on two Landsat-8 images, one image representing the flood, with river level like LFOF, and the other image representing the driest period. This process was taken to determine which index demonstrated the best result for water detection. We used threshold technique to water extraction. The MNDWI showed the highest accuracy, Kappa index was greater than 0.9. A multitemporal analysis of river dynamics, between 2005 and 2019, was also performed, using Landsat images. Two Sentinel-1 images, representing the maximum and minimum level of the river, between 2016 and 2017, were obtained. Threshold technique was applied in Sentinel-1 images for open water extraction. The highest accuracy demonstrated by Kappa on Sentinel-1 images was 0.47. River simulations were performed in HAND model, which presented an accuracy higher than 96.67%

Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, italy

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Leveraging geospatial technologies for flood management and disaster assessment in the Lower Mekong River Basin

The sixty million inhabitants of the Lower Mekong River Basin (LMB) in Southeast Asia rely on the natural resources provided by the Mekong River and its annual flooding. Extreme events, however, can cause widespread agricultural, structural, and humanitarian loss across the basin. Population increases, land use change, hydropower development, and climate change threaten to increase flood risk and potential damage over the next century. Geospatial technologies such as satellite remote sensing and Geographic Information Systems (GIS) provide powerful tools for assessing historic risk and monitoring real-time events. In this thesis, I first assess trends in flood magnitude and timing over the last 90 years using stream gauge data to understand changes in the annual flooding of the river and associated risk. Next, I develop methods for using satellite data to map flood extent and relative degree, as well as assess risk to agriculture from both floods and droughts based on historic conditions. Lastly, I demonstrate how the satellite-derived flood maps can be integrated with socio-economic data in automated GIS tools to provide timely and reliable estimates of socio-economic damage during disasters.Geography and the Environmen