A MODIS-Based Automated Flood Monitoring System for Southeast Asia

Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 16 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017). The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-meter Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-hour latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders

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

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

Spatio-temporal Analysis of Agriculture in the Vietnamese Mekong Delta using MODIS Imagery

New methodologies using MODIS time‒series imagery were developed for revealing spatio‒temporal changes of agricultural environments and land use patterns in the Vietnamese Mekong Delta. The following methodologies were proposed:a Wavelet based Filter for Crop Phenology (WFCP), a Wavelet‒based fi lter for evaluating the spatial distribution of Cropping Systems (WFCS), and a Wavelet‒based fi lter for detecting spatio‒temporal changes in Flood Inundation(WFFI). The WFCP algorithm involves smoothing the temporal profi le of the Enhanced Vegetation Index (EVI) using the wavelet transform approach. As a result of validation using the agricultural statistical data in Japan, it was shown that the WFCP was able to estimate rice growing stages, including transplanting date, heading date and harvesting date from the smoothed EVI data, with 9‒12 days accuracy(RMSE). The WFCS algorithm was developed for detecting rice‒cropping patterns in the Vietnamese Mekong delta based on WFCP. It was revealed that the spatial distribution of rice cropping seasons was characterized by both annual fl ood inundation around the upper region in the rainy season and salinity intrusion around the coastal region in the dry season. The WFFI algorithm was developed for estimating start and end dates of fl ood inundation by using time‒series Land Surface Water Index and EVI. Annual intensity of Mekong fl oods was evaluated from 2000 to 2004, at a regional scale. Applying a series of wavelet‒based methodologies to the MODIS data acquired from 2000 to 2006, it was confi rmed that the cropping season for the winter‒spring rice in the fl ood‒prone area fl uctuated depending on the annual change of fl ood scale. It was also confi rmed that the triple rice‒cropped area in the An Giang province expanded from 2000 to 2005, because the construction of a ring‒dike system and water‒resource infrastructure made it possible to sustain a third rice cropping season during the fl ood season. The proposed methodologies(WFCP, WFCS, WFFI) based on MODIS time‒series imagery made it clear that while the rice cropping in the Vietnamese Mekong Delta was quantitatively(annual fl ooding) and qualitatively(salinity intrusion) affected by water‒resource changes, there were some regions where the cultivation system was changed from double rice cropping to triple rice cropping because of the implementation of measures against fl ooding.日本の食料自給率 (2005年時の供給熱量ベース) は、40% と先進7カ国の中で最も低い。日本は、その食料海外依存度の高さから、世界的な食料価格の変動の影響を最も受け易い国と言える。近年の経済発展に伴う中国の大豆輸入量の増加や世界的なエネルギー政策の転換 (バイオエタノール政策) は、世界の穀物需給バランスを不安定にさせつつあり、世界的な問題となっている。さらに、地球温暖化による農業生産影響、増加し続ける世界人口、鈍化する穀物生産性を考えれば、世界の食料需給バランスが将来にわたって安定し続けると言うことはできないだろう。他方、食料増産・生産性向上を目的とした集約的農業の展開は、発展途上国の農業環境にさらなる負荷を与えるかもしれない。世界の食料生産と密接な関係にある日本は、自国の食料安全保障を議論する前提として、急速に変わり行く世界の農業生産現場やそれを取り巻く農業環境を客観的に理解し、世界の農業環境情報を独自の手法によって収集・整理する必要がある。そこで、筆者は、衛星リモートセンシング技術を活用することによって、地球規模の視点で、時間的・空間的な広がりを持って変わり行く農業生産活動とそれを取り巻く農業環境情報を把握・理解するための時系列衛星データ解析手法の確立を目指すこととした。本研究では、インドシナ半島南端に位置するベトナム・メコンデルタを調査対象領域とした。ベトナムは、タイに次ぐ世界第2位のコメ輸出国であり、その輸出米の9割近くが、ベトナム・メコンデルタで生産されたものである。筆者は、ベトナム・メコンデルタを世界の食料安全保障を考える上で重要な食料生産地帯の一つであると考え、本地域における農業環境及び土地利用パターンの時空間変化を明らかにするためのMODIS データを用いた新たな時系列解析手法の開発を行った。　本研究において提案する時系列解析手法は、次の三つである。1. Wavelet‒based Filter for Crop Phenology (WFCP) ,2. Wavelet‒based Filter for evaluating the spatial distribution of Cropping System (WFCS) , 3. Wavelet‒based Filterfor detecting spatio‒temporal changes in Flood Inundation (WFFI) . WFCP は、時系列植生指数 (EVI) を平滑化するためにウェーブレット変換手法を利用しており、日本の農業統計データを用いた検証結果から、水稲生育ステージ (田植日、出穂日、収獲日) を約9－12日 (RMSE) の精度で推定可能であることが示された。WFCP を基に改良されたWFCS は、水稲作付パターンの年次把握を可能にし、ベトナムメコンデルタにおける水稲作付時期の空間分布が、上流部において毎年雨期に発生する洪水と沿岸部において乾季に発生する塩水遡上によって特徴づけられていることを明らかにした。WFFI は、時系列水指数 (LSWI) と植生指数 (EVI) から、湛水期間、湛水開始日・湛水終息日を広域把握し、メコン川洪水強度の年次変化を地域スケールで評価することを可能にする。そして、ウェーブレット変換を利用した一連の手法を、2000～2006年までのMODIS 時系列画像に適用することによって、メコンデルタ上流部の洪水常襲地帯において、冬春米の作付時期が、年次変化する洪水規模に依存していることを明らかにした。また、An Giang 省において、堤防建設 (輪中) や水利施設の建設によって、洪水期における水稲三期作が可能になった地域が、2000～2005年にかけて拡大していることを明らかにした。本研究で提案したMODIS 時系列画像を利用した時系列解析手法 (WFCP、WFCS、WFFI) によって、ベトナムメコンデルタにおける水稲生産が水資源の量的 (洪水) ・質的 (塩水遡上) 変動影響を受ける一方、洪水対策の実施によって、栽培体系を二期作から三期作に変更している地域があることを明らかにした

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Delimitation of water areas using remote sensing in Brazil’s semiarid region

Remote sensing techniques are of fundamental importance to investigate the changes occurred in the terrestrial mosaic over the years and contribute to the decision-making by increasing efficient environmental and water management. This article aimed to detect, demarcate and quantify the hydric area of Poço da Cruz reservoir, located in Ibimirim, Pernambuco, semiarid region of Brazil, with modeling based on Landsat 8/OLI satellite multispectral images from 2015 to 2020, and to relate it with data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellites average rainfall. For this purpose, the Modified Normalized Difference Water Index (MNDWI) was modeled, being produced georeferenced theme maps and extracted only the pixels represented by positive spectral values, which represent water targets. The open-access software Quantum Geographic Information System (QGIS, version 2.18.16) was used for all stages of digital image processing and connection with complementary databases on the theme maps elaboration. In the results, changes in the spatial distribution of Poço da Cruz were evidenced and analyzed using precipitation data from the CHIRPS product, allowing a better understanding of the rainfall behavior in the region and its influence. The MNDWI was lined with the CHIRPS product, in which the spatial correlation between the rainy event and the water area’s delimitation is documented, especially in October 2017 (minimum values) and October 2020 (maximum values).As técnicas de sensoriamento remoto são de fundamental importância para investigar as alterações ocorridas no mosaico terrestre ao longo dos anos e contribuir para tomadas de decisão cada vez mais eficientes em gestão ambiental e hídrica. Os objetivos deste artigo foram detectar, delimitar e quantificar a área hídrica do reservatório Poço da Cruz, localizado em Ibimirim, Pernambuco, Semiárido do Brasil, com modelagem baseada em imagens multiespectrais do satélite Landsat 8/OLI datadas de 2015 a 2020, bem como relacioná-la com dados de precipitação pluvial média do produto CHIRPS. Para tanto, foi modelado o Índice de Água por Diferença Normalizada Modificado (MNDWI), com o qual se geraram os mapas temáticos georreferenciados e extraíram-se apenas os pixels representados por valores espectrais positivos, que representam alvos hídricos. Utilizou-se o software de livre acesso QGIS 2.18.16 para todas as etapas de processamento digital de imagens e conexão com bancos de dados complementares para a elaboração dos mapas temáticos. Nos resultados foram evidenciadas as mudanças na distribuição espacial do Poço da Cruz, analisadas com a utilização de dados de precipitação com base no produto CHIRPS, permitindo melhor compreensão do comportamento da pluviometria na região e sua influência. O MNDWI foi condizente com o produto de precipitação do CHIRPS, e ficou evidente a variação área hídrica do reservatório com relação à ocorrência de eventos chuvosos, especialmente em outubro/2017 (mínimos valores) e outubro/2020 (máximos valores)

Development and evaluation of a framework for global flood hazard mapping

AbstractNowadays, the development of high-resolution flood hazard models have become feasible at continental and global scale, and their application in developing countries and data-scarce regions can be extremely helpful to increase preparedness of population and reduce catastrophic impacts.The present work describes the development of a novel procedure for global flood hazard mapping, based on the most recent advances in large scale flood modelling. We derive a long-term dataset of daily river discharges from the hydrological simulations of the Global Flood Awareness System (GloFAS). Streamflow data is downscaled on a high resolution river network and processed to provide the input for local flood inundation simulations, performed with a two-dimensional hydrodynamic model. All flood-prone areas identified along the river network are then merged to create continental flood hazard maps for different return periods at 30′′ resolution. We evaluate the performance of our methodology in several river basins across the globe by comparing simulated flood maps with both official hazard maps and a mosaic of flooded areas detected from satellite images. The evaluation procedure also includes comparisons with the results of other large scale flood models. We further investigate the sensitivity of the flood modelling framework to several parameters and modelling approaches and identify strengths, limitations and possible improvements of the methodology

Remote Sensing of River Discharge: A Review and a Framing for the Discipline

Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, and reviewers. While the intellectually diverse subfield of RSQ practitioners can parse this confusion, the broader hydrology community views RSQ as a monolith and such confusion can be damaging. RSQ has not been comprehensively summarized over the past decade, and we believe that a summary of the recent literature has a potential to provide clarity to practitioners and general hydrologists alike. Therefore, we here summarize a broad swath of the literature, and find after our reading that the most appropriate way to summarize this literature is first by application area (into methods appropriate for gauged, semi-gauged, regionally gauged, politically ungauged, and totally ungauged basins) and next by methodology. We do not find categorizing by sensor useful, and everything from un-crewed aerial vehicles (UAVs) to satellites are considered here. Perhaps the most cogent theme to emerge from our reading is the need for context. All RSQ is employed in the service of furthering hydrologic understanding, and we argue that nearly all RSQ is useful in this pursuit provided it is properly contextualized. We argue that if authors place each new work into the correct application context, much confusion can be avoided, and we suggest a framework for such context here. Specifically, we define which RSQ techniques are and are not appropriate for ungauged basins, and further define what it means to be ‘ungauged’ in the context of RSQ. We also include political and economic realities of RSQ, as the objective of the field is sometimes to provide data purposefully cloistered by specific political decisions. This framing can enable RSQ to respond to hydrology at large with confidence and cohesion even in the face of methodological and application diversity evident within the literature. Finally, we embrace the intellectual diversity of RSQ and suggest the field is best served by a continuation of methodological proliferation rather than by a move toward orthodoxy and standardization

Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique)

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract:] Study Region Licungo basin (Mozambique) Study Focus The Licungo basin (23,263 km2) suffers frequent severe flooding due to tropical storms, in a country that is among the world’s most vulnerable in terms of exposure to weather-related hazards and climate change. We propose a methodology for the estimation of the population exposed to flooding at the catchment scale in data-scarce regions, combining satellite imagery with integrated high-resolution hydrological-hydraulic modelling. All the input data needed are retrieved from freely-available global satellite products. The numerical model is also freeware. The methodology is therefore replicable worldwide. An estimate of the flood extent and exposed population during Tropical Storm Ana (January 2022) is presented as a case study. New Hydrological Insights for the Region Current freely-available satellite products in combination with high-resolution hydrological-hydraulic models can be used to estimate the population exposed to flooding in the whole catchment. This estimate is more realistic than the one obtained using satellite imagery alone, since satellite images are very rarely taken at the time of maximum flooding. Using the proposed methodology, we estimate that over 273,000 people (out of 1.5 million) were exposed to flooding in the Licungo basin during Tropical Storm Ana. This represents 18% of the basin population and is 8 times larger than the estimate obtained using only the available satellite images.European Civil Protection and Humanitarian Operations (ECHO); ECHO/-SF/BUD/2018/9100