Jude H. Kastens

Crop identification using harmonic analysis of time-series AVHRR NDVI data

Harmonic analysis of a time series of National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer normalized difference vegetation index (NDVI) data was used to develop an innovative technique for crop type identification based on temporal changes in NDVI values. Different crops (corn, soybeans, alfalfa) exhibit distinctive seasonal patterns of NDVI variation that have strong periodic characteristics. Harmonic analysis, or Fourier analysis, decomposes a time-dependent periodic phenomenon into a series of constituent sinusoidal functions, or terms, each defined by a unique amplitude and phase value. Amplitude and phase angle images were produced by analysis of the time-series NDVI data and used within a discriminant analysis to develop a methodology for crop type identification. For crops that have a single distinct growing season and period of peak greenness, such as corn, the majority of the variance was captured by the first and additive terms, while winter wheat exhibited a bimodal NDVI periodicity with the majority of the variance accounted for by the second harmonic term.

Using USDA Crop Progress Data for the Evaluation of Greenup Onset Date Calculated from MODIS 250-Meter Data

Identification of the onset of vegetation greenup is a key factor in characterizing and monitoring vegetation dynamics over large areas. However, the relationship between greenup onset dates estimated from satellite imagery and the actual growth stage of vegetation is often unclear. Herein, we present an approach for comparing pixel-level onset dates to regional planting and emergence information for agricultural crops, with the goal of drawing reliable conclusions regarding the physical growth stage of the vegetation of interest at the time of greenup onset. To accomplish this, we calculated onset of greenup using MODIS 250 m, 16-day composite NDVI time series data for Kansas for 2001 and a recently proposed methodology for greenup detection. We then evaluated the estimated greenup dates using the locations of 1,417 large field sites that were planted to corn, soybeans, or sorghum in 2001, in conjunction with United States Department of Agriculture (USDA) weekly crop progress reports containing crop planting and emergence percentage estimates. Average greenup onset dates calculated for the three summer crops showed that the dates were consistent with the relative planting order of corn, sorghum, and soybeans across the state. However, the influence of pre-crop vegetation (weeds and “volunteer” crops) introduced an early bias for the greenup onset dates calculated for many field sites. This pre-crop vegetation signal was most pronounced for the later planted summer crops (soybeans and sorghum) and in areas of Kansas that receive higher annual precipitation. The most reliable results were obtained for corn in semi-arid western Kansas, where pre-crop vegetation had considerably less influence on the greenup onset date calculations. The greenup onset date calculated for corn in western Kansas was found to occur 23 days after 50 percent of the crop had emerged. Corn’s greenup onset was detected, on average, at the agronomic stage where plants are 15 to 45 cm (6 to 18 inches) tall and the crop begins its rapid growth.

Multitemporal, Moderate-Spatial-Resolution Remote Sensing of Modern Agricultural Production and Land Modification in the Brazilian Amazon

We present an extensive review of the literature on remote sensing and land change in Amazonia as part of a call for new methods to study the recent expansion of mechanized annual cropping. Following the review is a presentation of the use of multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) 250-meter Vegetation Index (VI) data to study processes of intensification of mechanized agriculture in Vilhena, Rondônia, Brazil, an Amazonian soy-producing municipality. The case study shows that the high temporal resolution and moderate spatial resolution of the MODIS VI data hold promise for acquiring information necessary to answer important questions about mechanized agriculture and its relationship to deforestation.

Estimativa de área agrícola por meio de séries temporais Modis NDVI no Estado do Mato Grosso

The objective of this work was to evaluate a simple, semi‑automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R2 = 0.89), but poor agreement in municipalities with less than 5% crop cover (R2 = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil.

Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, Brazil

Previous research has established the usefulness of remotely sensed vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to characterize the spatial dynamics of agriculture in the state of Mato Grosso (MT), Brazil. With these data it has become possible to track MT agriculture, which accounts for ~85% of Brazilian Amazon soy production, across periods of several years. Annual land cover (LC) maps support investigation of the spatiotemporal dynamics of agriculture as they relate to forest cover and governance and policy efforts to lower deforestation rates. We use a unique, spatially extensive 9-year (2005–2013) ground reference dataset to classify, with approximately 80% accuracy, MODIS VI data, merging the results with carefully processed annual forest and sugarcane coverages developed by Brazil’s National Institute for Space Research to produce LC maps for MT for the 2001–2014 crop years. We apply the maps to an evaluation of forest and agricultural intensification dynamics before and after the Soy Moratorium (SoyM), a governance effort enacted in July 2006 to halt deforestation for the purpose of soy production in the Brazilian Amazon. We find the pre-SoyM deforestation rate to be more than five times the post-SoyM rate, while simultaneously observing the pre-SoyM forest-to-soy conversion rate to be more than twice the post-SoyM rate. These observations support the hypothesis that SoyM has played a role in reducing both deforestation and subsequent use for soy production. Additional analyses explore the land use tendencies of deforested areas and the conceptual framework of horizontal and vertical agricultural intensification, which distinguishes production increases attributable to cropland expansion into newly deforested areas as opposed to implementation of multi-cropping systems on existing cropland. During the 14-year study period, soy production was found to shift from predominantly single-crop systems to majority double-crop systems.

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.

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.

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.

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 NDVI-based proxy measurements of rangeland productivity in the surveyed ranchers 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.

Investigating collection 4 versus collection 5 MODIS 250 m NDVI time-series data for crop separability in Kansas, USA

ABSTRACT The primary objective of this research was to analyse collection 5 versus collection 4 time-series normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m for the purpose of separating crop types. Using extensive ground reference data from the state of Kansas in the central USA, NDVI value profiles were extracted from different collection versions for 2001 (collections 4 and 5) and 2005 (collection 5 only). Phenological curves for all crops and all data sets were created and visually inspected. Jeffries–Matusita (J-M) distance statistical analysis was performed to assess crop separability. Contrary to expectations, collection 5 time-series MODIS 250 m NDVI data were found to be inferior to collection 4 with respect to crop separability. Specifically, collection 4 data exhibited a greater dynamic range across the growing seasons of the various crop types, and this discriminatory advantage was supported by J-M distance analysis. Though the analysis did not suggest reasons for the outcome, it corroborates the conclusion of the only other similar study in the literature comparing data from collections 4 and 5. Considering the pervasive use of these data for land-cover mapping, it is recommended that MODIS NDVI data from collection 4 should be used where possible for crop type mapping in agricultural regions with climate, geography, and crops similar to Kansas.