Patrick Leinenkugel

Understanding the impact of hydropower developments in the context of upstream–downstream relations in the Mekong river basin

Hydropower developments along the main stem of the Mekong River and its tributaries cause transboundary effects within the Mekong Basin Region, which comprises parts of six countries. On the one hand, the provision of hydropower triggers economic development and helps to meet the rising energy demand of the Mekong riparian countries, especially China, Thailand, and Vietnam. On the other hand, the negative impact of dam construction, mainly altered water flow and sediment load, has severe impacts on the environment and the livelihoods of the rural Mekong population. Several discrepancies exist in the needs, demands, and challenges of upstream versus downstream countries. Against the common apprehension that downstream countries are powerlessly exposed to mainly negative impacts whereas upstream countries unilaterally benefit from hydropower, the authors argue that upstream–downstream relations are not really clear-cut. This conclusion is based on a consideration of the complex power play between Mekong riparians, with a focus on recent power trade interactions. The article investigates the consequences of hydropower dams for the Mekong region as well as the role of supranational players, such as the Mekong River Commission and the Greater Mekong Subregion Initiative, on the hydropower debate. It is not nations that are the winners or losers in the hydropower schemes in the Mekong, but rather parts of the riparian population: a few influential and powerful elites versus the large mass of rural poor.

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.

Comparison and enhancement of MODIS cloud mask products for Southeast Asia

Accurate quality information on cloud occurrence is of utmost importance for a wide range of remote-sensing applications and analyses. This study compares the two existing cloud mask products available for the Moderate Resolution Imaging Spectroradiometer (MODIS), stored in the quality layer of the MOD09 daily surface reflectance product. For both masks, statistics on cloud occurrence are calculated for 1 year of daily surface reflectance data covering the area of southeast Asia. Furthermore, a cloud mask enhancement algorithm is presented for increasing cloud flag reliability by effectively combining the existing cloud labels together with the utilization of annual statistics based on the blue reflectance band. Furthermore, since a lot of compositing algorithms rely on cloud mask information for the filtering of unsuitable observations, the influence of the different cloud masks on 8-day MOD09A1 composite outputs is examined with respect to data availability and average view angles. The results of the statistical analysis show that the accuracy of the two cloud mask products differs significantly for southeast Asia. Particularly, the radiative influence of land cover proves to strongly affect the reliability of the cloud flags, although to varying degrees throughout the year. The enhancement algorithm successfully identifies undetected clear observations in the original masks while simultaneously setting upper limits for atmospheric contamination. In this manner, considerably higher proportions of cloud-free observations can be retained compared to a clear-sky conservative combination of the masks as applied in other compositing algorithms.

A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data

ABSTRACT Information on land cover and land use at high spatial resolutions is essential for advancing earth system science as well as for environmental monitoring to support decision-making and reporting processes. In view of this, we present the first version of the DFD Land Use and Land Cover Product for Germany, DFD-LULC_DE, for the year 2014, generated from 702 Landsat-7 and Landat-8 scenes at 30 m resolution. The results were derived based on a fully automated preprocessing chain that integrates data acquisition, radiometric, atmospheric and topographic correction, as well as spectral–temporal feature extraction for all Landsat surface reflectance bands, brightness temperature and various spectral indices. The classification followed a two-step approach: first, an initial classification is performed using a Random Forest classifier trained on ground truth data obtained from the LUCAS survey of EUROSTAT, followed by a semi-automated sampling of additional training data to further improve the initial classification results. Automatic selection of appropriate training samples is based on the vote entropy derived from the initial classification, thereby keeping manual user interaction low. The approach demonstrated is promising, also with respect to a European wide application, and contributes towards the advancement and enhancement of the DLR-DFD’s processing chains, which are directed towards the generation of land cover products at regular intervals being of central importance to related land monitoring and reporting services.

Comparing global land-cover products – implications for geoscience applications: an investigation for the trans-boundary Mekong Basin

In this article we present the results of a comparison of six globally available land-cover products for the Mekong Basin – an area that spans 795,000 km2 and comprises parts of six riparian countries: China, Myanmar, Thailand, Laos, Cambodia, and Vietnam. The basin covers most climatic zones: from high-altitude, snow-covered mountainous regions in the north, to subtropical and tropical rainforest areas and agricultural land further south. The geopolitically important region not only is home to over 72,000,000 inhabitants, but also is a centre of attention of several environmental modelling experts, trying to assess future hydrologic dynamics, climate variability, as well probable land-use developments in the area. We compare land-cover products of the University of Maryland, UMD 1992–1993, the GLC 2000 product, the GlobCover products of 2004–2006 and 2009, as well as the MODIS-derived land-cover products of 2001 and 2009. For harmonization of individual legends, the Land Cover Classification System, LCCS, has been employed. However, even after harmonization, cross-tabulation among the products reveals extreme differences, where the impact of differing classification algorithms weighs higher than the impact of temporal coincidence of products. Especially, differences within mixed-vegetation classes are large, strongly impacting the overall assessment of forested land, other vegetated land, and even cultivated land in the Mekong Basin. The findings presented here are of high relevance for the modelling community as well as Mekong-related environmental studies, which should consider global remote-sensing-derived products with caution and solid background knowledge.

Evaluation of Soil Moisture Retrieval from the ERS and Metop Scatterometers in the Lower Mekong Basin

The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = −0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Nino events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88.

Tropical forest cover dynamics for Latin America using Earth observation data: a review covering the continental, regional, and local scale

The tropical forest cover has varied greatly over the last few decades. The rapid advance of agricultural crops and illegal clearings in natural areas has resulted in the conversion of the majority of the world’s forest into desolated patches. Although rates of deforestation have decreased compared to previous years, forest loss still remains a crucial concern. Latest studies conducted on a global scale identified the Latin American continent as one of the regions exhibiting the highest rates of deforestation in the world. The dynamics of forests over the past 40 years has attracted numerous remote-sensing-based studies to monitor forest loss, analyse patterns, and understand the drivers of land conversion. This review article provides a comprehensive overview of the remote-sensing-based studies of tropical forest dynamics in Latin America. Following an introduction with respect to global forest mapping products, a general outline of tropical forest ecoregions and drivers of deforestation in Latin America is provided. Subsequently, a review and categorization of the existing studies is presented, where focus is laid on selected sensors and data analysis methodologies apply. Furthermore, a case study for the whole of Paraguay is presented; Paraguay is a region which contains highly diverse ecosystems that have been ravaged as a result of deforestation over the past 40 years. The main results, challenges, and future needs are discussed.

Drought impact on vegetation productivity in the Lower Mekong Basin

The Lower Mekong Basin (LMB) has a typical monsoon climate, with high temperatures and an uneven distribution of precipitation throughout the year. This climate, combined with the geographic position of the LMB, has led to an increase in the frequency of extreme weather events over last decade. However, few previous studies have used remote-sensing data to investigate the impact of such weather events, particularly severe droughts, on biological productivity in the LMB. To address this, we assessed the impact of drought on vegetation productivity in the LMB during 2000–2011 using MOD17 products. Several drought events were identified during this period. Of these, the most severe occurred during 2005 and 2010, although the 2005 drought was both more extensive and more intense. Net primary productivity (NPP) exhibited considerable variation during 2000–2011: the droughts in 2005 and 2010 reduced NPP by 14.7% and 8.4%, respectively. The impact of drought on NPP in 2005 was much greater than that in 2010, likely owing to the longer duration and larger deficit of precipitation in 2005 (which lasted from winter 2004 to spring 2005). Our results demonstrate that severe drought had a greater impact on NPP than mild drought, especially for forests, woodlands, and shrublands. Comparatively, little variation in NPP was found for croplands, even under drought conditions, which were attributed to the wide use of irrigation and the exploitation of water sources during drought periods. Moreover, multi-season croplands in Vietnam experienced only a small reduction in gross primary productivity (GPP) in 2005 compared to one-season croplands in Cambodia, which can be related to the shorter growing periods of the former impacted by droughts.

Sensitivity analysis for predicting continuous fields of tree-cover and fractional land-cover distributions in cloud-prone areas

The use of multi-temporal datasets, such as vegetation index time series or phenological metrics, for improved classification and regression performance is well established in the remote-sensing science community. However, the usefulness of such information is less apparent for areas with distinct wet season periods and heavily concentrated cloud cover. In view of this, this study examines the potential of multi-temporal datasets for the estimation of sub-pixel land-cover fractions and percentage tree cover in an area having distinct wet and dry seasons. Prediction is based on a regression tree algorithm in combination with linear least-squares regression planes, which relate multi-spectral and multi-temporal satellite data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor to sub-pixel land-cover proportions and percentage tree cover, derived from high-resolution land-cover maps. Furthermore, several versions of the latter were produced using different classification approaches to evaluate the sensitivity of the response variable on overall prediction accuracy. The results were evaluated according to absolute accuracy levels and according to their long-term inter-annual robustness by applying the regression models to MODIS data over a period of 11 years. The best regression model based on dry season information only estimated continuous fields of percentage tree cover with a prediction error of less than 7% and an inter-annual variability of less than 4% over a time period of 11 years. The inclusion of intra-annual information did not contribute to any improvements in model accuracy compared to information from the dry season alone, and furthermore, deteriorated inter-annual robustness of model predictions. In addition, it has been shown that the quality of the response variable in the training data had significant effects on overall accuracy.

IWRM for the Mekong Basin

The Mekong Basin is facing its most pronounced human induced changes in history. Development on the river’s mainstream, climate change, and economic growth of the riparian countries are taking a toll on the highly sensitive aquatic ecosystems and affect the livelihoods of millions of people in the region. Integrated Water Resources Management (IWRM) can provide a holistic approach to address the emerging challenges in the Mekong Basin. However, transboundary discords between the riparian countries, scattered responsibilities at national levels, power asymmetries, and the absence of legal frameworks and commitment to an IWRM approach at a basin scale make it difficult to implement a strategy that ensures a stable economic growth, sustainable management of natural resources as well as social well-being. On the other hand almost all riparian countries of the Mekong have the potential for a positive economic growth and progressing technical development in the next years. If this development is framed by an appropriate ecological awareness the Mekong Basin could develop in an ecologically and socially sustainable manner.