The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping

One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data

A new land cover map for the Mekong: Southeast Asia’s largest transboundary river basin

The transboundary Mekong basin, including territorial parts of China, Myanmar, Laos, Thailand, Cambodia, and Vietnam, is endowed with a rich natural resource base. The rapid socio-economic development of the region, however, substantially increases pressure on its natural resources that are increasingly subject of over-exploitation and environmental degradation. Some of the main environmental problems facing the region are common or transboundary issues that only can be addressed by transboundary approaches based on consistent and regional comparable information on the state of the environment at basin scale. In this context, a regional specific land cover map, the MEKONG LC2010 product, was produced for the entire Mekong Basin, utilising information from the MODIS sensor aboard the platforms Aqua and Terra

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

Spatio-temporal patterns of tree cover dynamics in the Mekong Basin as derived from 11 years of MODIS data

Patterns of tree cover loss within the Mekong Basin were analyzed based on 11-years of MODIS Data. The use of the full 11-year history allowed for a deeper analytical examination of changes compared to simple bi-temporal approaches

Land Cover Characteristics and Tree Cover Dynamics in the Mekong Basin. Analysing one decade of satellite data

The Mekong Basin in Southeast Asia is one of the largest international river basins in the world. Its abundant natural resources are shared by six riparian countries, i.e. China, My-anmar, Laos, Thailand, Cambodia, and Vietnam, and provide the basis for more than 72 million people, many who are directly dependant on the resources for their subsistence. Rapid socio-economic growth throughout the last decades, though, has had substantial effects on the rate and intensity of anthropogenic interventions in the region’s ecosystems and on the respective modifications in land cover and land usage, first and foremost on the highly valuable remaining forests in the region. Although socio-economically very diverse, economic reforms towards market liberalisa-tion and intensified transboundary economic flow among the countries have increasingly transformed the Basin into an integrated region. In the course of such continuous coales-cence many transboundary environmental issues have arisen that require interregional approaches as well as regionally consistent environmental information to be adequately assessed and accounted for. In this respect, remote sensing has evolved as a key tool for the spatially continuous and consistent documentation of environmental characteristics, particularly those related to the terrestrial land cover, beyond physical or political bounda-ries, and at various temporal and spatial scales. In view of the absence of such adequate and up-to-date information products for this re-gion, this dissertation contributes towards an improved knowledge base on the current land cover-related characteristics and change processes in the Mekong Basin. More spe-cifically, the aim has been firstly, to present a comprehensive and detailed depiction of the current land cover distribution in the entire Mekong Basin and secondly, to give insight on the most dominant process of land cover change in the Basin and Southeast Asia generally, namely the unprecedented rate of deforestation. This has been achieved by producing regional-specific information products on current and historic land cover characteristics on the basis of remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the platforms Aqua and Terra. However, on account of almost persistent cloud cover in the rainy season, the application of optical remote sensing data poses a challenge that demands special data processing and classification methods. In the early stages of data processing, existing quality prod-ucts with regard to the detection of clouds in MODIS satellite data, so-called cloud masks, are compared and assessed with respect to their validity. It could be shown that due to faulty cloud flags, the availability of suitable observations differs widely depending on the respective cloud mask that is referred to. In this respect, a cloud mask enhancement al-gorithm is presented for increasing cloud flag reliability when working with daily MODIS satellite data. Since cloud masks are essential variables with respect to the conversion of cloudy satellite images into cloud-free image composites or the production of vegetation-index time series, the findings from this comparison are fundamental to the generation of a high-quality data basis that is used for all subsequent applications and analyses. Intra-annual vegetation dynamics (phenology) for the entire Mekong Basin are derived and interpreted for the year 2010. Therefore, an Enhanced Vegetation Index (EVI) time series is produced, with dropouts and noise being effectively reduced by applying an adaptive Savitzky–Golay filter in combination with a harmonic analysis that allows for ad-justments adapted to the respective growing cycles in the time series. Information on land cover is derived by a hierarchical unsupervised classification approach, optimised for re-gions with frequent cloud cover. Moreover, the environmental heterogeneous conditions throughout the region are addressed by a regionally-tuned clustering approach, based on pre-defined physiographic subregions and the use of auxiliary geodata. In this way a re-gional-specific land cover map, the Mekong LC2010 product, is produced that to the au-thor’s best knowledge, provides the scientific community with the first regionally-optimised land cover characterisation for the entire Mekong Basin. This map differentiates 22 land cover classes and includes not only a broad range of standard classes, but also very specific land use types such as classes on double, or triple season croplands, or on dif-ferent aquaculture intensities. The forest cover dynamic in the Lower Mekong Basin is analysed by deriving vegetation structure as per pixel fractional cover of woody vegetation, herbaceous vegetation and barren land in annual time intervals between 2001 and 2011. A multi-scale approach is applied based on a non-linear regression tree algorithm that relates MODIS reflectance data as explanatory variables to subpixel canopy cover as response variable. The latter is derived by the classification and aggregation of high-resolution Landsat data. A particular methodological focus in this application is directed to minimising inter-annual fluctuations in model predictions originating from noisy input data and algorithm limitations, to improve the identification of real land cover changes on the ground. Significant reductions in tree cover undergone within the observation period were identi-fied by utilizing the long-term statistics on inter-annual prediction variability as a function of tree cover. Furthermore, new methodological approaches are tested to utilize the full canopy cover trajectory throughout a period of eleven years to perform a deeper, analyti-cal examination of forest cover dynamics that goes beyond differentiating a simple change-no-change situation. The specific temporal patterns in the tree cover trajectory enable the differentiation between permanent forest cover conversions and temporary forest losses, as well as characterising change processes according to their level of ab-ruptness. In this way, it is shown that the reconstruction of forest disturbance histories can be used to reveal information about the underlying causes of forest cover reduction or may be indicative of the land’s respective anthropogenic usage. Derived results indicate that particularly the dense evergreen forests in Cambodia and the forests in the Central Highlands of Vietnam are at high risk, suffering losses at rates of 0.8% and 1.2% per year. While deforestation in Cambodia is observed as being of mostly permanent nature, in the Vietnamese regions of the Basin a high concentration of temporary forest loss could be identified in addition to the permanent forest losses. Laos is endowed with the highest proportion of woody cover in the LMB and, thus, despite very large absolute forest losses shows a comparatively low deforestation rate of ‘only’ 0.4% per year. However, extensive forest areas in Laos can be considered as degraded forests with lower canopy cover estimates as a result of shifting cultivation practices. This tradi-tional form of agriculture causes widespread patterns of temporary canopy cover reduc-tion, observed particularly in northern Laos and along the Annamite mountain chain. Thai-land shows with 0.1% the lowest annual forest loss rate within the Basin as well as being characterised by an almost absence of temporary forest losses

Land surface parameters derived from remotely sensed data at national to global scales

Since the mid-1970s remote sensing has evolved as a key tool for the documentation of the state of the environment. On local to national scales, high to medium resolution satellite data with pixel sizes of 5-30m provide opportunities for the detailed mapping of even complex mosaic landscapes. The low temporal resolution of these sensors, large data volumes, and costly data acquisition, however, make them less ideal for regular monitoring purposes. On continental to global scales, the derivation of land indicators is generally provided by remote sensing data originating from low to moderate-resolution sensors with spatial resolutions between 250m-1000m. Due to the short revisit periods of these sensors, a spatially continuous, cloud free, and consistent coverage can be ensured within relatively short time spans, features that are particularly demanding for cloud prone areas. Furthermore, the daily or near-daily global coverage of these sensors provide the basis for continuous time series that allow the analysis of regional to global scale intra-annual land surface dynamics

Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007–2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people’s rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR WSM based flood mapping arediscussed. The results contribute to a comprehensive understanding of Mekong Delta flood dynamics in an environment where the flow regime is influenced by the Mekong River, overland water-flow, anthropogenic floodwater control, as well as the tides

Modeling River Discharge Using Automated River Width Measurements Derived from Sentinel-1 Time Series

Against the background of a worldwide decrease in the number of gauging stations, the estimation of river discharge using spaceborne data is crucial for hydrological research, river monitoring, and water resource management. Based on the at-many-stations hydraulic geometry (AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1 time series within an automated workflow. By using a novel decile thresholding method, no a priori knowledge of the AMHG function or proxy is used, as proposed in previous literature. With a relative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8% considering only dry seasons, our method is a significant improvement relative to the optimized AMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded into an automated workflow, it enables a global application for river discharge estimation using solely remote sensing data. Starting with the mapping of river reaches, which have large differences in river width over the year, continuous river width time series are created using high-resolution and weather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River near Vientiane, Laos, for the period from 2015 to 2018

Spatio-temporal patterns of agriculture in the Mekong Delta based on vegetation index time-series from the MODIS sensor

Daily satellite data from the MODIS sensor are used to extract intra-annual harvest-patterns and to derive detailed land use maps including classes on intra-annual harvest patterns, as shown for the area of the Mekong Delta

Remote Sensing in Mapping Mangrove Ecosystems: An Object-Based Approach

Over the past few decades, clearing for shrimp farming has caused severe losses of mangroves in the Mekong Delta (MD) of Vietnam. Although the increasing importance of shrimp aquaculture in Vietnam has brought significant financial benefits to the local communities, the rapid and largely uncontrolled increase in aquacultural area has contributed to a considerable loss of mangrove forests and to environmental degradation. Although different approaches have been used for mangrove classification, no approach to date has addressed the challenges of the special conditions that can be found in the aquaculture-mangrove system in the Ca Mau province of the MD. This paper presents an object-based classification approach for estimating the percentage of mangroves in mixed mangrove-aquaculture farming systems to assist the government to monitor the extent of the shrimp farming area. The method comprises multi-resolution segmentation and classification of SPOT5 data using a decision tree approach as well as local knowledge from the region of interest. The results show accuracies higher than 75% for certain classes at the object level. Furthermore, we successfully detect areas with mixed aquaculture-mangrove land cover with high accuracies. Based on these results, mangrove development, especially within shrimp farming-mangrove systems, can be monitored. However, the mangrove forest cover fraction per object is affected by image segmentation and thus does not always correspond to the real farm boundaries. It remains a serious challenge, then, to accurately map mangrove forest cover within mixed systems