Plan for the uniform mapping of earth resources and environmental complexes from Skylab imagery. Assessment of natural vegetation, environmental, and crop analogs

The author has identified the following significant results. For interpreting a wide range of natural vegetation analogs, S-190A color infrared and the ERTS-1 color composite were consistently more useful than were conventional color or black and white photos. Color infrared was superior for five vegetation analogs while color was superior for only three. The errors in identification appeared to associate more with black and white single band images than with multiband color. For rice crop analogs, spectral and spatial discriminations both contribute to the usefulness of images for data collection. Tests and subjective analyses conducted in this study indicated that the spectral bands exploited in color infrared film were the most useful for agricultural crop analysis. Accuracy of crop identification on any single date of Skylab images was less than that of multidate analysis due to differences in crop calendar, cultural practices used, rice variety, planting date, planting method, water use, fertilization, disease, or mechanical problems

Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand : a case study of maize-grain crop production in the Waikato region : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Palmerston North, New Zealand

Precision agriculture attempts to manage within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. To achieve this, delineation of field-specific management zones (MZs), representing significantly different yield potentials are required. To date, the effectiveness of utilising MZs in New Zealand has potentially been limited due to a lack of emphasis on the interactions between spatiotemporal factors such as soil texture, crop yield, and rainfall. To fill this research gap, this thesis aims to improve the process of delineating MZs by modelling spatiotemporal interactions between spatial crop yield and other complementary factors. Data was collected from five non-irrigated field sites in the Waikato region, based on the availability of several years of maize harvest data. To remove potential yield measurement errors and improve the accuracy of spatial interpolation for yield mapping, a customised filtering algorithm was developed. A supervised machine-learning approach for predicting spatial yield was then developed using several prediction models (stepwise multiple linear regression, feedforward neural network, CART decision tree, random forest, Cubist regression, and XGBoost). To provide insights into managing spatiotemporal yield variability, predictor importance analysis was conducted to identify important yield predictors. The spatial filtering method reduced the root mean squared errors of kriging interpolation for all available years (2014, 2015, 2017 and 2018) in a tested site, suggesting that the method developed in R programme was effective for improving the accuracy of the yield maps. For predicting spatial yield, random forest produced the highest prediction accuracies (R² = 0.08 - 0.50), followed by XGBoost (R² = 0.06 - 0.39). Temporal variables (solar radiation, growing degree days (GDD) and rainfall) were proven to be salient yield predictors. This research demonstrates the viability of these models to predict subfield spatial yield, using input data that is inexpensive and readily available to arable farms in New Zealand. The novel approach employed by this thesis may provide opportunities to improve arable farming input-use efficiency and reduce its environmental impact

Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours

Knowledge of the location and extent of agricultural fields is required for many applications, including agricultural statistics, environmental monitoring, and administrative policies. Furthermore, many mapping applications, such as object-based classification, crop type distinction, or large-scale yield prediction benefit significantly from the accurate delineation of fields. Still, most existing field maps and observation systems rely on historic administrative maps or labor-intensive field campaigns. These are often expensive to maintain and quickly become outdated, especially in regions of frequently changing agricultural patterns. However, exploiting openly available remote sensing imagery (e.g., from the European Union’s Copernicus programme) may allow for frequent and efficient field mapping with minimal human interaction. We present a new approach to extracting agricultural fields at the sub-pixel level. It consists of boundary detection and a field polygon extraction step based on a newly developed, modified version of the growing snakes active contours model we refer to as graph-based growing contours. This technique is capable of extracting complex networks of boundaries present in agricultural landscapes, and is largely automatic with little supervision required. The whole detection and extraction process is designed to work independently of sensor type, resolution, or wavelength. As a test case, we applied the method to two regions of interest in a study area in the northern Germany using multi-temporal Sentinel-2 imagery. Extracted fields were compared visually and quantitatively to ground reference data. The technique proved reliable in producing polygons closely matching reference data, both in terms of boundary location and statistical proxies such as median field size and total acreage

Photomorphic analysis techniques: An interim spatial analysis using satellite remote sensor imagery and historical data

The use of machine scanning and/or computer-based techniques to provide greater objectivity in the photomorphic approach was investigated. Photomorphic analysis and its application in regional planning are discussed. Topics included: delineation of photomorphic regions; inadequacies of existing classification systems; tonal and textural characteristics and signature analysis techniques; pattern recognition and Fourier transform analysis; and optical experiments. A bibliography is included

Investigation of remote sensing techniques as inputs to operational resource management

The author has identified the following significant results. Visual interpretation of 1:125,000 color LANDSAT prints produced timely level 1 maps of accuracies in excess of 80% for agricultural land identification. Accurate classification of agricultural land via digital analysis of LANDSAT CCT's required precise timing of the date of data collection with mid to late June optimum for western South Dakota. The LANDSAT repetitive nine day cycle over the state allowed the surface areas of stockdams and small reservoir systems to be monitored to provide a timely approximation of surface water conditions on the range. Combined use of DIRS, K-class, and LANDSAT CCT's demonstrated the ability to produce aspen maps of greater detail and timeliness than was available using US Forest Service maps. Visual temporal analyses of LANDSAT imagery improved highway map drainage information and were used to prepare a seven county drainage network. An optimum map of flood-prone areas was developed, utilizing high altitude aerial photography and USGS maps

Corn/soybean decision logic: Improvements and new crops

There are no author-identified significant results in this report

Remote sensing of opium poppy cultivation in Afghanistan

This work investigates differences in the survey methodologies of the monitoring programmes of the United Nations Office on Drugs and Crime (UNODC) and the US Government that lead to discrepancies in quantitative information about poppy cultivation. The aim of the research is to improve annual estimates of opium production. Scientific trials conducted for the UK Government (2006–2009) revealed differences between the two surveys that could account for the inconsistency in results. These related to the image interpretation of poppy from very high resolution satellite imagery, the mapping of the total area of agriculture and stratification using full coverage medium resolution imagery. MODIS time-series profiles of Normalised Difference Vegetation Index (NDVI), used to monitor Afghanistan’s agricultural system, revealed significant variation in the agriculture area between years caused by land management practices and expansion into new areas. Image interpretation of crops was investigated as a source of bias within the sample using increasing levels of generalisation in sample interpretations. Automatic segmentation and object-based classification were tested as methods to improve consistency. Generalisation was found to bias final estimates of poppy up to 14%. Segments were consistent with manual field delineations but object-based classification caused a systematic labelling error. The findings show differences in survey estimates based on interpretation keys and the resolution of imagery, which is compounded in areas of marginal agriculture or years with poor crop establishment. Stratified and unstratified poppy cultivation estimates were made using buffered and unbuffered agricultural masks at resolutions of 20, 30 and 60 m, resampled from SPOT-5 10 m data. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Decreasing the resolution of the imagery and buffering increased unstratified estimates. Stratified estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area explained differences in cultivation figures of the opium monitoring programmes in Afghanistan. Supporting methods for yield estimation for opium poppy were investigated at field sites in the UK in 2004, 2005 and 2010. Good empirical relationships were found between NDVI and the yield indicators of mature capsule volume and dry capsule yield. The results suggested a generalised relationship across all sampled fields and years (R2 >0.70) during the 3–4 week period including poppy flowering. The application of this approach in Afghanistan was investigated using VHR satellite imagery and yield data from the UNODC’s annual survey. Initial results indicated the potential of improved yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling. The recommendations for poppy cultivation surveys are: the use of image-based stratification for improved precision and reducing differences in the agricultural mask, and use of automatic segmentation for improved consistency in field delineation of poppy crops. The findings have wider implications for improved confidence in statistical estimates from remote sensing methodologies

A GIS-based method for household recruitment in a prospective pesticide exposure study

<p>Abstract</p> <p>Background</p> <p>Recent advances in GIS technology and remote sensing have provided new opportunities to collect ecologic data on agricultural pesticide exposure. Many pesticide studies have used historical or records-based data on crops and their associated pesticide applications to estimate exposure by measuring residential proximity to agricultural fields. Very few of these studies collected environmental and biological samples from study participants. One of the reasons for this is the cost of identifying participants who reside near study fields and analyzing samples obtained from them. In this paper, we present a cost-effective, GIS-based method for crop field selection and household recruitment in a prospective pesticide exposure study in a remote location. For the most part, our multi-phased approach was carried out in a research facility, but involved two brief episodes of fieldwork for ground truthing purposes. This method was developed for a larger study designed to examine the validity of indirect pesticide exposure estimates by comparing measured exposures in household dust, water and urine with records-based estimates that use crop location, residential proximity and pesticide application data. The study focused on the pesticide atrazine, a broadleaf herbicide used in corn production and one of the most widely-used pesticides in the U.S.</p> <p>Results</p> <p>We successfully used a combination of remotely-sensed data, GIS-based methods and fieldwork to select study fields and recruit participants in Illinois, a state with high corn production and heavy atrazine use. Our several-step process consisted of the identification of potential study fields and residential areas using aerial photography; verification of crop patterns and land use via site visits; development of a GIS-based algorithm to define recruitment areas around crop fields; acquisition of geocoded household-level data within each recruitment area from a commercial vendor; and confirmation of final participant household locations via ground truthing. The use of these procedures resulted in a sufficient sample of participants from 14 recruitment areas in seven Illinois counties.</p> <p>Conclusion</p> <p>One of the challenges in pesticide research is the identification and recruitment of study participants, which is time consuming and costly, especially when the study site is in a remote location. We have demonstrated how GIS-based processes can be used to recruit participants, increase efficiency and enhance accuracy. The method that we used ultimately made it possible to collect biological samples from a specific demographic group within strictly defined exposure areas, with little advance knowledge of the location or population.</p