Daniel E. Irwin

Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins

Floods are among the most catastrophic natural disasters around the globe impacting human lives and infrastructure. Implementation of a flood prediction system can potentially help mitigate flood-induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in situ observations (i.e., rain and stream gauges). Recently, satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a distributed hydrologic model to characterize the spatial extent of flooding and associated hazards over sparsely gauged or ungauged basins. We present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based distributed hydrologic model, Coupled Routing and Excess STorage (CREST), was implemented for the Nzoia basin, a subbasin of Lake Victoria in Africa. Moderate Resolution Imaging Spectroradiometer Terra-based and Advanced Spaceborne Thermal Emission and Reflection Radiometer-based flood inundation maps were produced over the region and used to benchmark the distributed hydrologic model simulations of inundation areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography, and other products along with space-based flood inundation extents as inputs to the distributed hydrologic model. We conclude that the quantification of flooding spatial extent through optical sensors can help to calibrate and evaluate hydrologic models and, hence, potentially improve hydrologic prediction and flood management strategies in ungauged catchments.

LANDSCAPE ARCHAEOLOGY: Remote-sensing investigation of the ancient Maya in the Peten rainforest of northern Guatemala

Conducting field research in the dense forests of the Peten, northern Guatemala, is as difficult today as it was for A. V. Kidder 70 years ago. However, through the use of airborne and satellite imagery we are improving our ability to investigate ancient Maya settlement, subsistence, and landscape modification in this dense forest region. Today the area is threatened by encroaching settlement and deforestation. However, it was in this region that the Maya civilization began, flourished, and abruptly disappeared for unknown reasons in the ninth century a.d. At the time of its collapse it had attained one of the highest population densities in human history. How the Maya were able to manage water successfully and feed this dense population is not well understood at this time. A project funded by the National Aeronautics and Space Administration (NASA) used remote-sensing technology to investigate large seasonal swamps (bajos) that make up 40% of the landscape. Through the use of remote sensing, ancient Maya features such as sites, roadways, canals, and water reservoirs have been detected and verified through ground reconnaissance. The results of this preliminary research cast new light on the adaptation of the ancient Maya to their environment. Microenvironmental variation within the wetlands was elucidated and the different vegetation associations identified in the satellite imagery. More than 70 new archaeological sites within and at the edges of the bajo were mapped and tested. The combination of satellite imagery and ground verification demonstrated that the Maya had modified their landscape in the form of dams, reservoirs, and possible drainage canals along the Holmul River and its tributaries. The use of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM), 1-m IKONOS satellite imagery, as well as high-resolution airborne STAR-3i radar imagery—2.5 m backscatter/10 m Digital Elevation Model (DEM)—are opening new possibilities for understanding how a civilization was able to survive for centuries on a karst topographic landscape. This understanding is critical for the current population that is experiencing rapid population growth and destroying the landscape through non-traditional farming and grazing techniques, resulting in socioeconomic problems.

Putting Us on the Map: Remote Sensing Investigation of the Ancient Maya Landscape

A common problem for archaeologists studying ancient settlement in the Maya Lowlands is overcoming the dense vegetation in order to obtain an accurate regional perspective of the presence of archaeological sites, their exact locations and their overall extents. Most often this is done by extensive ground surveys in which many individuals chop parallel paths through the vegetation in search of sites. Once a site is found an effort is made to mark its location on a regional map and to explore its perimeter. Obtaining locational information has been made dramatically easier in recent years with the advent of improved Global Positioning Systems (GPS), however the process of initial identification of sites and the determination of their borders is exceedingly labor intensive and has remained relatively unchanged since the beginning of settlement surveys in the region in the 1950 s. Currently, we are revolutionizing settlement survey in the Maya Lowlands by using remotely sensed data from IKONOS, Quickbird, and Eo 1, satellites as well as airborne AIRSAR radar data. The Ancient Maya built their cities, towns and even their smallest hamlets using excavated limestone and lime plasters. We propose that the decay of these structures provides a unique microenvironment for the growth of vegetation as the levels of moisture and nutrition within the ruins vary substantially from those in the surrounding forest. These microenvironmental differences on the ground are likewise represented by compositional differences in the forest canopy both in the species present and in leaf color (representing moisture/nutritional stress) visible through the analysis of high-resolution satellite data. In this way the detailed analysis of forest composition can reveal a detailed picture of the ancient settlements that lie beneath it. Preliminary examinations using this technique have been very successful and we are refining these techniques in order to efficiently comprehend the details of Ancient Maya settlement in the Lowlands.

Biogeography of tropical montane cloud forests. Part II: Mapping of orographic cloud immersion

Abstract This study details two unique methods to quantify cloud-immersion statistics for tropical montane cloud forests (TMCFs). The first technique uses a new algorithm for determining cloud-base height using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, and the second method uses numerical atmospheric simulation along with geostationary satellite data. Cloud-immersion statistics are determined using MODIS data for March 2003 over the study region consisting of Costa Rica, southern Nicaragua, and northern Panama. Comparison with known locations of cloud forests in northern Costa Rica shows that the MODIS-derived cloud-immersion maps successfully identify known cloud-forest locations in the United Nations Environment Programme (UNEP) World Conservation Monitoring Centre (WCMC) database. Large connected regions of cloud immersion are observed in regions in which the trade wind flow is directly impinging upon the mountain slopes; in areas in which the flow is parallel to the slopes,...

Using remote sensing to monitor the influence of river discharge on watershed outlets and adjacent coral Reefs: Magdalena River and Rosario Islands, Colombia

a b s t r a c t Worldwide, coral reef ecosystems are being increasingly threatened by sediments loads from river dis- charges, which in turn are influenced by changing rainfall patterns due to climate change and by growing human activity in their watersheds. In this case study, we explored the applicability of using remote sensing (RS) technology to detect and monitor the relationship between water quality at the coral reefs around the Rosario Islands, in the Caribbean Sea and the rainfall patterns in the Magdalena River water- shed. From the Moderate Resolution Imaging Spectroradiometer (MODIS), this study used the water surface reflectance product (MOD09GQ) to estimate water surface reflectance as a proxy for sediment concentration and the land cover product (MCD12Q1 V51) to characterize land cover of the watershed. Rainfall was estimated by using the 3B43 V7 product from the Tropical Rainforest Measuring Mission (TRMM). For the first trimester of each year, we investigated the inter-annual temporal variation in water surface reflectance at the Rosario Islands and at the three main mouths of the Magdalena River water- shed. No increasing or decreasing trends of water surface reflectance were detected for any of the sites for the study period 2001-2014 (p > 0.05) but significant correlations were detected among the trends of each site at the watershed mouths (r = 0.57-0.90, p < 0.05) and between them and the inter-annual variation in rainfall on the watershed (r = 0.63-0.67, p < 0.05). Those trimesters with above-normal water surface reflectance at the mouths and above-normal rainfall at the watershed coincided with La Ni ˜ na conditions while the opposite was the case during El Niconditions. Although, a preliminary analysis of inter-annual land cover trends found only cropland cover in the watershed to be significantly correlated with water surface reflectance at two of the watershed mouths (r = 0.58 and 0.63, p < 0.05), the validation analysis draw only a 40.7% of accuracy in this land cover classification. This requires further analysis to confirm the impact of the cropland on the water quality at the watershed outlets. Spatial analysis with MOD09GQ imagery detected the overpass of river plumes from Barbacoas Bay over the Rosario Islands waters.

Yearly Extraction of Central America's Land Cover for Carbon Flux Monitoring

Ideal remote sensor land cover extraction for national to regional carbon flux monitoring constitutes highly accurate change detection on an annual basis, a challenge magnified in the cloud-occluded tropics. Focusing on seven Central American countries from Belize to Panama, this study tested the feasibility of yearly land cover extraction from MODIS surface reflectance composites, ancillary land cover maps, and country-produced reference polygons. GIS models were created to automate the country-specific process of generating annual input tables for a greenhouse gas inventory tool. MODIS-favorable results in a six-category schema suggest that improvements may depend on international reference data warehousing and interoperability.

Application of satellite observations to manage natural disasters in the Lake Victoria Basin

Lake Victoria, the second largest fresh water lake in the Eastern part of Africa is a vital natural resource for the economic well being and prosperity of over 30 million people located in riparian regions of Uganda, Kenya and Tanzania. It covers a large area of about 68, 870 km2 and produces a GDP of about US

SERVIR: Connecting Earth Observation Satellite Data to Local Science Applications

30 billion per year. The region is also very much prone to natural disasters such as severe floods during heavy precipitation periods in the Eastern part of Africa. In addition to floods, the precipitation also produces large infestations of mosquito larvae due to the standing water in many areas. This further causes multiple vector borne diseases such as Malaria, Rift Valley Fever and more. These problems are of serious concern and require active and aggressive surveillance and management to minimize the loss of human and animal lives and property damage. Satellite imagery and observations along with the in situ measurements provide a great tool to analyze and study this area and inform the policy makers to make calculated policy decisions which are beneficial to the environment. Recently, NASA and USAID have joined forces with the Regional Center for Mapping of Resources for Development (RCMRD) located in Nairobi, Kenya to utilize multiple NASA sensors such as TRMM, SRTM and MODIS to develop flood potential maps for the Lake Victoria Basin. The idea is to generate a flood forecasts and remote sensing data has proven extremely valuable for identifying the location, extent, and severity of these events. However, despite extraordinary efforts on the part of remote sensing data providers to rapidly deliver such maps, there is typically a delay of several days or even weeks from the onset of flooding until such maps are available to the disaster management community. This paper summarizes efforts at NASA to address this problem through development of an integrated and automated process of a) flood detection b) flood forecasting, c) satellite data acquisition, d) rapid flood mapping and distribution, and e) validation of flood forecasting and detection products.

Mapping threats to agriculture in East Africa: Performance of MODIS derived LST for frost identification in Kenya’s tea plantations

With four international centers or “hubs,” a joint NASA/USAID initiative known as SERVIR is well positioned to fast-track the application of Earth Observation (EO) satellite data into decision-making contexts across the globe. This chapter describes the SERVIR program and how it helps developing countries use EO data to address environmental issues. The focus is on SERVIR applications in Land-Cover and Land-Use Change (LCLUC) and emissions in Asia. These projects and applications, developed in collaboration with SERVIR hubs in Nepal and Thailand, are helping decision-makers in these regions monitor and manage forest resources, understand land cover dynamics, and inform greenhouse gas inventories.

Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data

Abstract Frost is a major threat to crop productivity in the Kenyan highlands. With agriculture being central to the Kenyan economy, every effort needs to be taken to alleviate losses especially on high value crops like tea, the leading foreign exchange earner. Current frost mapping efforts by SERVIR, a joint initiative between National Aeronautics and Space Administration (NASA) and the U.S. Agency for International Development (USAID), and its hub institution in Eastern and Southern Africa, the Regional Center for Mapping of Resources for Development (RCMRD), utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) derived Land Surface Temperature (LST) to probabilistically map areas that have been affected by frost. In this paper, we assessed the accuracy of these frost maps by testing the performance of MYD11A1 MODIS product in indicating areas affected by frost. MODIS derived LST values corresponding to frost and no frost observation locations and dates were reclassified according to 6 predetermined categories representing frost severity levels. The overall accuracy of each threshold category as LST cutoff separating frost and no frost affected areas was determined. An overall performance measure was then estimated using a Receiver Operating Characteristics curve (ROC). Overall accuracies of 67.3%–71.9% among the thresholds were obtained. An area under the ROC curve of 0.69 was obtained, indicating a poor performance of MODIS LST to distinguish frost from no frost areas. This shows that although MODIS derived LST can be used to identify frost-affected areas, it is not on its own sufficient in discriminating these areas with high levels of accuracy. Revision of temperature thresholds is recommended, in addition to improved characterization of frost occurrence in the region to include other factors that may be affecting frost occurrence. These results stand to better prepare the agricultural sector for damaging weather-related events.