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

Automated riverine landscape characterization: GIS-based tools for watershed-scale research, assessment, and management

River systems consist of hydrogeomorphic patches (HPs) that emerge at multiple spatiotemporal scales. Functional process zones (FPZs) are HPs that exist at the river valley scale and are important strata for framing whole-watershed research questions and management plans. Hierarchical classification procedures aid in HP identification by grouping sections of river based on their hydrogeomorphic character; however, collecting data required for such procedures with field-based methods is often impractical. We developed a set of GIS-based tools that facilitate rapid, low cost riverine landscape characterization and FPZ classification. Our tools, termed RESonate, consist of a custom toolbox designed for ESRI ArcGIS®. RESonate automatically extracts 13 hydrogeomorphic variables from readily available geospatial datasets and datasets derived from modeling procedures. An advanced 2D flood model, FLDPLN, designed for MATLAB® is used to determine valley morphology by systematically flooding river networks. When used in conjunction with other modeling procedures, RESonate and FLDPLN can assess the character of large river networks quickly and at very low costs. Here we describe tool and model functions in addition to their benefits, limitations, and applications

Mato Grosso, Brazil, ground reference data for crop years 2005-2013 (Dataset)

This dataset was prepared for use in MODIS NDVI-based land cover classification for Mato Grosso, Brazil, which is an Amazonian agricultural frontier. Quantification of the displacement of forests by agriculture (horizontal intensification) and the transition of farm fields from single-cropping to double-cropping (vertical intensification) is necessary for understanding the rapid environmental and social changes that are occurring within this globally important region.The points associated with ‘ground reference set 1’ and ‘ground reference set 2’ identify fields where agricultural cover information was obtained by Embrapa through farmer interviews. The points associated with ‘supplemental pasture/cerrado’ were identified using aerial and satellite imagery to provide additional ground reference samples for the pasture/cerrado data class. See the following publication for more information (please cite this reference when using these data): Kastens, J.H., J.C. Brown, A.C. Coutinho, C.R. Bishop, and J.C.D.M. Esquerdo (2017). Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, Brazil. PLoS ONE, 12(4): e0176168. DOI: 10.1371/journal.pone.0176168 (https://doi.org/10.1371/journal.pone.0176168 ) Annual attributes beginning with ‘plos’ provide a binary indicator for whether or not the sample was used for development of the 14-year Mato Grosso land cover map set described in the PLOS ONE study (1 = used, 0 = not used). For additional information regarding class structure determination and data preparation and filtering, see the following: Brown, J.C., J.H. Kastens, A.C. Coutinho, D.C. Victoria, and C.R. Bishop (2013). Classifying Multiyear Agricultural Land Use Data from Mato Grosso Using Time-Series MODIS Vegetation Index Data. Remote Sensing of Environment, 130(3): 39-50. DOI: 10.1016/j.rse.2012.11.009 (http://dx.doi.org/10.1016/j.rse.2012.11.009)EmbrapaKansas Biological Surve

Data from: The geography of spatial synchrony

Spatial synchrony, defined as correlated temporal fluctuations among populations, is a fundamental feature of population dynamics, but many aspects of synchrony remain poorly understood. Few studies have examined detailed geographical patterns of synchrony; instead most focus on how synchrony declines with increasing linear distance between locations, making the simplifying assumption that distance decay is isotropic. By synthesising and extending prior work, we show how geography of synchrony, a term which we use to refer to detailed spatial variation in patterns of synchrony, can be leveraged to understand ecological processes including identification of drivers of synchrony, a long-standing challenge. We focus on three main objectives: (1) showing conceptually and theoretically four mechanisms that can generate geographies of synchrony; (2) documenting complex and pronounced geographies of synchrony in two important study systems; and (3) demonstrating a variety of methods capable of revealing the geography of synchrony and, through it, underlying organism ecology. For example, we introduce a new type of network, the synchrony network, the structure of which provides ecological insight. By documenting the importance of geographies of synchrony, advancing conceptual frameworks, and demonstrating powerful methods, we aim to help elevate the geography of synchrony into a mainstream area of study and application

Roadside data sample counts.

<p>Roadside data sample counts.</p

RF model OOB accuracy.

<p>The confusion matrix and traditional classification accuracy statistics are given for the OOB results produced using the RF model.</p