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

Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data

Rice is the most important food crop in Asia and rice exports can significantly contribute to a country's GDP. Vietnam is the third largest exporter and fifth largest producer of rice, the majority of which is grown in the Mekong Delta. The cultivation of rice plants is important, not only in the context of food security, but also contributes to greenhouse gas emissions, provides man-made wetlands as an ecosystem, sustains smallholders in Asia and influences water resource planning and run-off water management. Rice growth can be monitored with Synthetic Aperture Radar (SAR) time series due to the agronomic flooding followed by rapid biomass increase affecting the backscatter signal. With the advent of Sentinel-1 a wealth of free and open SAR data is available to monitor rice on regional or larger scales and limited data availability should not be an issue from 2015 onwards. We used Sentinel-1 SAR time series to estimate rice production in the Mekong Delta, Vietnam, for three rice seasons centered on the year 2015. Rice production for each growing season was estimated by first classifying paddy rice area using superpixel segmentation and a phenology based decision tree, followed by yield estimation using random forest regression models trained on in situ yield data collected by surveying 357 rice farms. The estimated rice production for the three rice growing seasons 2015 correlates well with data at the district level collected from the province statistics offices with R2s of 0.93 for the Winter–Spring, 0.86 for the Summer–Autumn and 0.87 for the Autumn–Winter season

Wasted Water: Returning to the Fishpond Latrine Amidst Modernity, Pollution, and Stigma

Since 1990, Vietnam has made significant efforts to eradicate open defecation in rural areas. With modernized systems and increasing severity of pollution within its rural provinces, local practices for managing human waste are also condemned as “backwards” and damaging to the environment and sanitary health. Labelled as “unhygienic”, hanging latrines were subsequently banned as an attempt to raise rural standards. Without many alternative solutions for populations living in poverty with insufficient water supplies, this act is viewed as a contentious undertaking to modernize rural Vietnam. Around the same time, flood cycles were also increasingly exploited by high dike fields to meet needs for commercial agriculture. Enabled by Vietnam’s “rice-first policy”, monocrop rice production became dependent on chemical fertilizers, herbicides, and pesticides, severely polluting regional water sources. Former means of nutrient cycling are stigmatized as malpractice—while agriculture is seen as an economic necessity. The result is the displacement of nutrients at two ends: in fields where imported artificial fertilizers replace natural soils, and in households where using human waste is stigmatized and thus cannot be safely integrated into local agricultural systems. Compromised and deemed unsafe for local household usage, open water becomes a threat, continually detaching users from their reliance on the landscape. Set in An Giang province—an anthropogenic delta landscape shaped by its dominant agricultural practices—this thesis uses the premise of a fishpond latrine to challenge the threats of modernity, pollution, and stigma. Based on reflections in Ivan Illich’s H2O and the Waters of Forgetfulness, modern technological advancement combined with sanitation have erased water’s role as a vessel for memory and culture. Sanitation is challenged for reducing water to H2O, a substance concealed within pipes to wash away our waste—resulting in perceptions of water as a polarized substance—strictly being either pure or impure in nature. Mary Douglas’ theory from Purity and Danger: An Analysis of Concepts of Pollution and Taboo complements the views on water’s polarization —where dirt is defined as matter out of place, posing risks and fear resulting in stigma on certain actions and cultures. Based on these theories, the proposal uses the fishpond latrine, a vernacular structure that directly ties household sanitation to the delta’s landscape, as a scalable framework that reinforces nutrient recycling, the connection between water and waste, and the bridging of land uses and household spaces together. Through this framework, a new landscape is proposed where flows are exposed through the restoration of streams and wetlands, and reducing pollution within a newly diversified field through natural cycles and integrated agricultural land use. At a household scale, a spectrum of water cascades through new sanitary spaces, destigmatizing the local practices of economic reuse of resource and conservation. Three settlement and land use typologies for dry, wet, and flood conditions are proposed at multiple scales. With agricultural land use ranging from gardens, orchards, and fields to livestock, aquaculture, and wetlands, these typologies employ adaptive design strategies for household amenities and structures, including the different latrines, for changing land conditions. Countering the efforts to wipe out its existence, the latrine is not removed from the system, but wholly integrated within the larger cycles occurring within the region

Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data

Rice is one of the world’s most dominant staple foods, and hence rice farming plays a vital role in a nation’s economy and food security. To examine the applicability of synthetic aperture radar (SAR) data for large areas, we propose an approach to determine rice age, date of planting (dop), and date of harvest (doh) using a time series of Sentinel-1 C-band in the entire Mekong Delta, Vietnam. The effect of the incidence angle of Sentinel-1 data on the backscatter pattern of paddy fields was reduced using the incidence angle normalization approach with an empirical model developed in this study. The time series was processed further to reduce noise with fast Fourier transform and smoothing filter. To evaluate and improve the accuracy of SAR data processing results, the classification outcomes were verified with field survey data through statistical metrics. The findings indicate that the Sentinel-1 images are particularly appropriate for rice age monitoring with R2  =  0.92 and root-mean-square error (RMSE) = 7.3 days (n  =  241) in comparison to in situ data. The proposed algorithm for estimating dop and doh also shows promising results with R2  =  0.92 and RMSE  =  6.2 days (n  =  153) and R2  =  0.70 and RMSE  =  5.7 days (n  =  88), respectively. The results have indicated the ability of using Sentinel-1 data to extract growth parameters involving rice age, planting and harvest dates. Information about rice age corresponding to the growth stages of rice fields is important for agricultural management and support the procurement and management of agricultural markets, limiting the negative effects on food security. The results showed that multitemporal Sentinel-1 data can be used to monitor the status of rice growth. Such monitoring system can assist many countries, especially in Asia, for managing agricultural land to ensure productivity

Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul

CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH

Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues

ĐỔI MỚI DIY: TACTICAL RURALISM AND TANGIBLE MODELING IN THE MEKONG DELTA

In recent years, the integrity of the Mekong Delta has been put at risk by a combination of environmental and institutional factors. Understanding that the degradation of the Delta would have far-reaching socioeconomic implications for both Vietnam and the Indochinese Peninsula, The World Bank has responded to the situation by implementing initiatives for climate-smart planning tools and improved water management practices throughout the lower Mekong basin. Seeing the potential for tangible modeling as a participatory planning tool, the Bank has hired a team of consultants from Louisiana State University to introduce a methodology called Tangible Landscape to its climate resilience toolkit. This thesis aims to contribute to the consultancy by using literature review, interpretive case studies in a design approach called tactical ruralism, and geospatial analysis to inform the design and fabrication of a conceptual Tangible Landscape model for the Mekong Delta. The author identifies the environmental problems facing the delta, compiles an array of relevant design solutions that can be used to address those problems at the site scale, and creates a series of mappings that identify suitable sites to apply those solutions. He also develops a conceptual transect of rural livelihoods of the Mekong Delta which can be used to inform a forthcoming Tangible Landscape workshop to be held in Viet Nam as part of the World Bank Consultancy. Providing solutions at every scale and level of governance is of particular importance to this project, especially those considered to be “grassroots” or “bottom-up” interventions implemented by individual households, communes, wards, and districts

Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia

  Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants

Korekce lokálního dopadového úhlu SAR dat pro analýzu časových řad: metoda specifická pro krajinný pokryv

To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested...K zajištění co nejvyššího možného časového rozlišení dat SAR je nutné použít všechny dostupné dráhy družic nad daným územím. To může představovat výzvu v hornatém terénu, kde boční snímání družic SAR v kombinaci s různými sklony a aspekty terénu může silně ovlivnit intenzitu zpětného radarového rozptylu. Tyto chyby způsobené terénem je třeba odstranit pro možné porovnání dat v čase. Ačkoli v literatuře jsou popsány metody, které se zabývají tímto problémem, žádná z těchto metod není připravena na operativní a snadno přístupnou analýzu časových řad v horských oblastech. Tato studie se zabývá metodou korekce lokálního dopadového úhlu (LIA) pro analýzu časových řad lesů v horských oblastech. Metodika je založena na použití lineární závislosti mezi radarovým zpětným rozptylem a LIA, který se počítá pro každý satelitní snímek zvlášť. Použitím kombinace databází CORINE a Hansen Global Forest můžeme pro každý jednotlivý snímek získat širokou škálu různých LIA pro konkrétní typ lesa. Algoritmus korekce byl připraven v cloudové platformě Google Earth Engine (GEE) s využitím volně dostupných dat Sentinel-1, digitálního modelu terénu SRTM a databází CORINE a Hansen Global...Katedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyFaculty of SciencePřírodovědecká fakult

Towards risk-based flood management in highly productive paddy rice cultivation – concept development and application to the Mekong Delta

Flooding is an imminent natural hazard threatening most river deltas, e.g. the Mekong Delta. An appropriate flood management is thus required for a sustainable development of the often densely populated regions. Recently, the traditional event-based hazard control shifted towards a risk management approach in many regions, driven by intensive research leading to new legal regulation on flood management. However, a large-scale flood risk assessment does not exist for the Mekong Delta. Particularly, flood risk to paddy rice cultivation, the most important economic activity in the delta, has not been performed yet. Therefore, the present study was developed to provide the very first insight into delta-scale flood damages and risks to rice cultivation. The flood hazard was quantified by probabilistic flood hazard maps of the whole delta using a bivariate extreme value statistics, synthetic flood hydrographs, and a large-scale hydraulic model. The flood risk to paddy rice was then quantified considering cropping calendars, rice phenology, and harvest times based on a time series of enhanced vegetation index (EVI) derived from MODIS satellite data, and a published rice flood damage function. The proposed concept provided flood risk maps to paddy rice for the Mekong Delta in terms of expected annual damage. The presented concept can be used as a blueprint for regions facing similar problems due to its generic approach. Furthermore, the changes in flood risk to paddy rice caused by changes in land use currently under discussion in the Mekong Delta were estimated. Two land-use scenarios either intensifying or reducing rice cropping were considered, and the changes in risk were presented in spatially explicit flood risk maps. The basic risk maps could serve as guidance for the authorities to develop spatially explicit flood management and mitigation plans for the delta. The land-use change risk maps could further be used for adaptive risk management plans and as a basis for a cost–benefit of the discussed land-use change scenarios. Additionally, the damage and risks maps may support the recently initiated agricultural insurance programme in Vietnam.</p