SOME NEW DEVELOPMENTS ON TWO SEPARATE TOPICS: STATISTICAL CROSS VALIDATION AND FLOODPLAIN MAPPING

This dissertation describes two unrelated threads of research. The first is a study of cross validation (CV), which is a data resampling method. CV is used for model ranking in model selection and for estimating expected prediction error of a model. A review of three resampling methods is provided in Chapter 1. Chapter 2 contains results from simulations that examine various properties of CV, in particular the use of CV for model selection in small sample settings as well as the expected value of the delete-d cross validation statistic. The second research thread is described in Chapter 3, where a new, physically-based computational model (called FLDPLN, or "Floodplain") for mapping potential inundation extents (floodplains) using gridded topographic data is introduced. Due to the parametric economy of FLDPLN, this model has significant advantages over existing methods such as hydrodynamic models. The model is validated using imagery from an actual flood event

THE BIG PICTURE - SATELLITE REMOTE SENSING APPLICATIONS IN RANGELAND ASSESSMENT AND CROP INSURANCE

Livestock Production/Industries, Risk and Uncertainty,

Using Digital Elevation Data for Applications in Floodplain Mapping

Spatial Modeling Applications Jude Kastens – Research Assistant, Kansas Applied Remote Sensing Program (KARS)GIS Day 2007 @ KU is sponsored by: * KU Department of Geography * State of Kansas Data Access and Support Center (DASC) * KU Libraries GIS and Scholar Services * KU Transportation Research Institute * KU Institute for Policy & Social Research * Western Air Maps, Inc. * Coca-Cola * Kansas Biological Survey * KU Center for Remote Sensing of Ice Sheets (CReSIS) * Kansas View Consortiu

Global Probable Maximum Precipitation (PMP) Dataset

The Global PMP Datasets in Geotiff format at the 0.5-hr, 1-hr, 2-hr, 3-hr, 6-hr, 12-hr, and 24-hr durations, are statistically derived based on WMO-NOAA’s endorsed Hershfield PMP estimation technique using IMERG’s 30-min precipitation dataset

Using temporal averaging to decouple annual and nonannual information in AVHRR NDVI time series

As regularly spaced time series imagery becomes more prevalent in the remote sensing community, monitoring these data for temporal consistency will become an increasingly important problem. Long-term trends must be identified, and it must be determined if such trends correspond to true changes in reflectance characteristics of the study area (natural), or if their source is a signal collection and/or processing artifact that can be identified and corrected in the data (artificial). Spectrally invariant targets (SITS) are typically used for sensor calibration and data consistency checks. Unfortunately, such targets are not always available in study regions. The temporal averaging technique described in this research can be used to determine the presence of artificial interannual value drift in any region possessing multiyear regularly sampled time series remotely sensed imagery. Further, this approach is objective and does not require the prior identification of a SIT within the region of study. Using biweekly Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data from 1990 to 2001 covering the conterminous United States, an interannual trend present in the entire scene was identified using the proposed technique and found to correspond extremely well with interannual trends identified using two SITS within the region

Alfalfa water productivity and yield gaps in the U.S. central Great Plains

Context: Yield gap (Yg) analyses using farmer-reported yield and management data have been performed for a number of annual grain crops, but it lacks for perennial forages. The U.S. accounts for 21 % of the global alfalfa production with a large rainfed area located in the central Great Plains, serving as an interesting case-study for Yg in perennial forages. Most existing alfalfa Yg analyses quantified the magnitude of the Yg but failed to identify associated management practices to reduce it. Challenging this analysis, a systematic benchmark for alfalfa water productivity [WP, kg dry matter per mm evapotranspiration (ETc)] that allows for the quantification of Yg in farmer fields does not exist. Objectives: Our objectives were to (i) benchmark alfalfa WP, (ii) quantify Yg in alfalfa farmer fields, and (iii) identify management opportunities to improve alfalfa yield. Methods: We conducted a systematic review of literature and compiled a database on alfalfa yield and ETc (n = 68 papers and 1027 treatment means) from which a WP boundary function was derived. We collected management and yield data from 394 commercial rainfed alfalfa fields during 2016–2019 in central Kansas. We then used satellite imagery to define the growing season (and corresponding water supply) for each field. The boundary function was then used to calculate Yg of each field, and conditional inference trees (CIT) explored the impact of management practices associated with increased yield. Results: Our boundary function suggested an alfalfa WP of 34 kg ha-1 mm-1. Farmer-reported yield ranged from 0.9 to 19.0 Mg ha-1, averaging 7.6 Mg ha-1. Alfalfa water-limited yield potential (Yw) ranged from 11.1 to 23.2 Mg ha-1, resulting in an average yield gap of 54–60 % of Yw. Row spacing, seeding rates, and management of phosphorus fertilizer were major agronomic practices explaining alfalfa yields in farmer fields, followed by surrogate variables as sowing season, stand age, and soil pH. Conclusions: Our study provided the first systematic analysis estimating attainable alfalfa WP as function of ETc, suggesting that large alfalfa Yg exist in the U.S. central Great Plains. We also identified key agronomic practices associated with increased alfalfa yield. Significance: The WP here derived can be used for future studies aiming at quantifying alfalfa Yg across the globe. This was an initial step in quantifying Yg and its associated causes at farmer fields, and we highlight limitations and future directions for perennial forages yield gap analyses

A 21-year record of vertically migrating subepilimnetic populations of Cryptomonas spp.

The vertical distribution and diel migration of Cryptomonas spp. were monitored continuously for 21 years in mesotrophic Cross Reservoir, northeast Kansas, USA. The movements of these motile algae were tracked on multiple dates during July–October of each year using in situ fluorometry and optical microscopy of Lugol’s iodine-preserved samples. Episodes of subepilimnetic diel vertical migration by Cryptomonas were detected and recorded on 221 different days between 1994 and 2014, with just 2 of these years (1998 and 2013) lacking any sampling events with deep peaks sufficiently large enough to track. Whenever a subepilimnetic layer of Cryptomonas was detectable, it was generally observed to ascend toward the bottom of the epilimnion beginning approximately at sunrise; to descend toward the lake bottom during the late afternoon and evening; and to remain as a deep-dwelling population until dawn of the following day. Moreover, there was high day-to-day consistency in the absolute water column depths at which the migrating algal cells would cease their ascending or descending movement. We believe this unique and remarkable dataset comprises the most detailed record of diel migratory behavior for any planktonic freshwater alga reported for a single freshwater lake

Ethanol plant location and intensification vs. extensification of corn cropping in Kansas

This is the author final draft. Copyright 2014 Elsevier.Farmers' cropping decisions are a product of a complex mix of socio-economic, cultural, and natural environments in which factors operating at a number of different spatial scales affect how farmers ultimately decide to use their land in any given year or over a set of years. Some environmentalists are concerned that increased demand for corn driven by ethanol production is leading to conversion of non-cropland into corn production (which we label as “extensification”). Ethanol industry advocates counter that more than enough corn supply comes from crop switching to corn and increased yields (which we label as “intensification”). In this study, we determine whether either response to corn demand – intensification or extensification – is supported. This is determined through an analysis of land-use/land-cover (LULC) data that covers the state of Kansas and a measure of a corn demand shifter related to ethanol production – distance to the closest ethanol plant – between 2007 and 2009

Analysis of Time-Series MODIS 250 m Vegetation Index Data for Crop Classification in the U.S. Central Great Plains

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for largearea crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral–temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region’s major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop’s multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state’s climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season

Remote Sensing and the Rancher: Linking Rancher Perception and Remote Sensing

Abstract In recent years, steps have been taken to implement a new crop insurance program for rangeland and pasture. Unlike traditionally insured row and cereal crops, which have directly measurable yields, there is no such simple, ideal yield standard for rangeland and pasture because of uncertainties regarding how to generally and objectively quantify annual production. With remotely sensed imagery acquired by the Advanced Very High Resolution Radiometer transformed to the Normalized Difference Vegetation Index (NDVI), we derived a proxy relative yield measurement for rangeland and pasture vegetation. This proxy measurement could potentially solve a critical component of the yield quantification problem facing implementation of a rangeland insurance program. In order to evaluate this proxy measurement and how ranchers might accept it, we surveyed a group of Kansas and Oklahoma ranchers to determine how their perception of rangeland productivity compared to NDVIbased proxy measurements of rangeland productivity in the surveyed rancher's county for the growing seasons of 1999-2003. At the scale of the ranch, correlation analysis showed that perception was not highly correlated with the satellite indices. Higher correlations were observed when perception data were aggregated and compared to rangeland indices at the county and study area levels, with performance comparable to using precipitation information. The year with the strongest correlation was the worst drought year of the 5, a desirable outcome in the context of an insurance program. Results from this case study provide some support for using remote sensing data in a national rangeland and pasture insurance program. Such a program would be an important new risk mitigation tool for ranchers. Resumen En añ os recientes se han tomado pasos para implementar un nuevo programa de seguro agrícola para praderas nativas y pastizales. A diferencia de los cultivos en surcos o cereales tradicionalmente asegurados, los cuales tienen rendimientos que se pueden medir directamente, en pastizales y praderas nativas no hay un rendimiento estándar ideal de referencia tan simple, debido a la incertidumbre de como cuantificar, en forma generalizada y objetivamente, la producción anual. Con imágenes de sensores remotos adquiridas por el Radiómetro Avanzado de Muy Alta Resolución y transformadas al Índice Normalizado de Diferencia de Vegetación (NDVI), derivamos una medición substituta del rendimiento relativo de la vegetación de pastizales y praderas naturales. Esta medida substituta pudiera potencialmente resolver un componente crítico del problema de cuantificación del rendimiento que encara la implementación de un programa de seguro en pastizales. Para evaluar esta medida substituta, y como los productores pudieran aceptarla, entrevistamos un grupo de ganaderos de Kansas y Oklahoma para determinar como sus percepciones de la productividad del pastizal se equiparan con las mediciones substitutas de la productividad del pastizal basadas en NDVI del municipio de los ganaderos entrevistados durante las estaciones de crecimiento de 1999 a 2003. A la escala de rancho, el análisis de correlación mostró que la percepción no estuvo altamente correlacionada con los índices del satélite. Las más altas correlaciones se observaron cuando los datos de percepción se agregaron y compararon con los índices del pastizal a nivel de municipio y área de estudio, con un resultado comparable a usar información de precipitación. El añ o con la más alta correlación fue el añ o con la peor sequía de los cinco evaluados, un resultado deseable en el contexto de un programa de seguro. Los resultados proveen algo de soporte para usar datos de sensores remotos en un programa nacional de seguro de pastizales y praderas nativas. Tal programa sería una herramienta nueva importante de mitigación de riesgo para los ganaderos