Aplicação de Um Sistema Webgis na Agricultura de Precisão

http://dx.doi.org/10.5902/2179460X18160Precision farming commonly uses the Geographic Information Systems (GIS) because they present themselves like excellent space management tools. Recent developments in internet technologies have contributed to access, publication, distribution and exploitation of geographic information. The use of GIS distributed on the Internet (Web GIS) is a viable solution in the management of farming systems and in studies on various scales laying GIS functionality available to users through a simple browser, without the need for large investments in relation to software or even specialized technical training. In the present article, we present the features of a Web GIS prototype, developed using free software, which provides geographic information layers that are under the responsibility of Embrapa South Livestock, allowing users to view and conducting research and operations typical spatial analysis of a conventional SIG. As a result presents briefly the features of WebGIS and shows up two applications at different geographical scales in order to explain its multiscale capabilities and their potential use in areas that interact directly or indirectly with the precision agriculture and the management of agricultural space.A agricultura de precisão utiliza comumente os Sistemas de Informação Geográfica (SIG) pois estes apresentam-se como excelentes ferramentas de gestão espacial. Os recentes desenvolvimentos em tecnologias da Internet têm contribuído para o acesso, publicação, exploração e distribuição da Informação Geográfica. A utilização de SIG distribuídos na Internet (WebGIS) é uma solução viável na gestão de sistemas agropecuários e em estudo em diversas escalas visto que coloca funcionalidades de SIG ao alcance de usuários, através de um simples browser, sem necessidade de grandes investimentos em relação a softwares ou mesmo em formação técnica especializada. Assim, no presente artigo, apresenta-se as funcionalidades de um protótipo de WebGIS, desenvolvido utilizando softwares livres, que disponibiliza informações geográficas de camadas que estão sob a responsabilidade da Embrapa Pecuária Sul, permitindo aos usuários a visualização e a realização de pesquisas e operações de análise espacial típicas de um SIG convencional. Como resultado apresenta-se sucintamente as funcionalidades do WebSIG e demonstra-se duas aplicações em diferentes escalas geográficas de modo a explicitar sua capacidade multiescalar e seu uso potencial em áreas que interagem direta ou indiretamente com a agricultura de precisão e a gestão do espaço agropecuário

Big Geospatial Data Analysis with Array Technologies for Agricultural Applications

165 σ.Οι δορυφορικές αποστολές US Landsat και EU Sentinel παρέχουν μαζικά διαχρονικά τηλεπισκοπικά δεδομένα. Για το λόγο αυτό, η ανάπτυξη αποδοτικών τεχνολογιών για την απευθείας διαχείριση και επεξεργασία αυτών των τηλεπισκοπικών δεδομένων είναι θεμελιώδους σημασίας. Προς την κατεύθυνση αυτή, σχεδιάστηκε, αναπτύχθηκε και αξιολογήθηκε ένα WebGIS σύστημα για την online ανάλυση ανοιχτών τηλεπισκοπικών δεδομένων και για εφαρμογές γεωργίας ακριβείας. Ειδικότερα, ο πυρήνας του συστήματος βασίζεται στο rasdaman Array Database Management System για την αποθήκευση των δεδομένων και το πρότυπο Web Coverage Processing Service του Open Geospatial Consortium για την εκτέλεση ερωτημάτων πάνω σε αυτά. Διάφορα ερωτήματα σχεδιάστηκαν και υλοποιήθηκαν για την πρόσβαση και την επεξεργασία πολυφασματικών δορυφορικών εικόνων. Το πρόγραμμα πελάτη του WebGIS συστήματος, το οποίο βασίζεται στις βιβλιοθήκες OpenLayers και GeoExt οι οποίες είναι γραμμένες στην γλώσσα προγραμματισμού javascript, χρησιμοποιεί τα υλοποιημένα ερωτήματα για την ad-hoc, online χωρική και φασματική ανάλυση των τηλεπισκοπικών δεδομένων. Το ανεπτυγμένο σύστημα στην τρέχουσα μορφή του καλύπτει πλήρως τον Ελλαδικό χώρο με πολυφασματικά δεδομένα τα οποία προέρχονται από το δορυφόρο Landsat 8,τα οποία με αυτόματο τρόπο συλλέγονται, προ-επεξεργάζονται, καταλογοποιούνται και είναι έτοιμα προς διάθεση και για τις περαιτέρω βασικές επεξεργασίες ανάλυσης. Τα ανεπτυγμένα ερωτήματα επεξεργασίας των δεδομένων τα οποία και εστιάζουν σε αγροτικές εφαρμογές είναι σε θέση να υπολογίσουν αποτελεσματικά την κάλυψη της βλάστησης, την κόμη φυλλώματος (canopy) και το υδατικό στρες της βλάστησης σε αγροτικές και δασώδεις εκτάσεις.Τα online παρεχόμενα τηλεπισκοπικά προϊόντα του συστήματος, συγκρίθηκαν και αξιολογήθηκαν σε σχέση με παρόμοιες διεργασίες οι οποίες πραγματοποιήθηκαν σε τυπικό λογισμικότηλεπισκόπησης και GIS συστημάτωνUS Landsat and EU Sentinel missions provide massive multitemporal remote sensing data. Therefore, the development of efficient technologies for their direct manipulation and processing is of fundamental importance. Towards this direction, we have designed, developed and evaluated a WebGIS system for the online analysis of open remote sensing data and for precision agriculture applications. In particular, the core functionality consists of the rasdaman Array Database Management System for storage, and the Open Geospatial Consortium Web Coverage Processing Service for data querying. Various queries have been designed and implemented in order to access and process multispectral satellite imagery. The web-client, which is based on the OpenLayers and GeoExt javascript libraries, exploits these queries enabling the online ad-hoc spatial and spectral remote sensing data analysis. The developed framework is fully covering Greece with Landsat 8 multispectral data which are stored and pre-processed automatically in our hardware for demonstration purposes. The developed queries, which are focusing on agricultural applications, can efficiently estimate vegetation coverage, canopy and water stress over agricultural and forest areas. The online delivered remote sensing products have been evaluated and compared with similar processes performed from standard desktop remote sensing and GIS software.Αθανάσιος K. Κάρμα

Connecting NASA Science and Engineering with Earth Science Applications

ABSTRACT The National Research Council (NRC) recently highlighted the dual role of NASA to support both science and applications in planning Earth observations. This article reports the efforts of the NASA Applied Sciences Program and NASA Soil Moisture Active Passive (SMAP) mission to integrate applications with science and engineering in prelaunch planning. The SMAP Early Adopter program supported the prelaunch applied research that comprises the SMAP Special Collection of the Journal of Hydrometeorology. This research, in turn, has resulted in unprecedented prelaunch preparation for SMAP applications and critical feedback to the mission to improve product specifications and distribution for postlaunch applications. These efforts have been a learning experience that should provide direction for upcoming missions and set some context for the next NRC decadal survey

Predictors of Puma Occupancy Indicate Prey Vulnerability is More Important Than Prey Availability in a Highly Fragmented Landscape

Habitat fragmentation represents the single greatest conservation challenge of the 21st century. This problem is particularly acute for large, obligate carnivores like pumas Puma concolor which have persisted in North and South America in the face of habitat fragmentation and other anthropogenic disturbances. Shrinking habitat and reduced connectivity mean that mapping habitat is increasingly important for species conservation in multiple-use landscapes. Previous work suggests that pumas occupy habitats where sufficient stalking cover and preferred prey are present, yet the intersection of these factors has rarely been assessed. Here we used data from 68 299 camera trap nights collected from 181 sites throughout the San Francisco Bay Area over a four-year period to identify key predictors of habitat occupancy for pumas and their primary prey (mule deer Odocoileus hemionus). Our goal was to determine whether pumas occupy habitats based on relative measures of prey availability (detection frequency), or ease of predation (density of stalking cover) and whether these predictors changed between seasons. Our results indicated that pumas primarily occupied forested habitats and did not choose habitats with abundant deer. Instead, pumas preferentially occupy habitats that facilitate their stalk and ambush hunting strategy, rather than higher prey densities, per se. The best occupancy models for mule deer indicated the importance of roads and shrub cover. However, even the best deer models performed poorly compared to the puma models, likely due to the ubiquity of mule deer in the region. Although prey density is a widely accepted correlate of habitat quality for many carnivores, our results suggest that structural elements of habitat may be a more important variable in predicting habitat use by large stalk and ambush predators like pumas, which has important implications for conservation success

Investigation into the bio-physical constraints on farmer turn-out-date decisions using remote sensing and meteorological data.

ThesisDoctoral thesisAccepted versionGrass is the most common landcover in Ireland and covers a bigger percentage (52%) of the country than any other in Europe. Grass as fodder is Ireland’s most important crop and is the foundation of its most important indigenous industry, agriculture. Yet knowledge of its distribution, performance and yield is scant. How grass is nationally, on a farm by farm, year by year basis managed is not known. In this thesis the gaps in knowledge about grassland performance across Ireland are presented along with arguments on why these knowledge gaps should be closed. As an example the need for high spatial resolution animal stocking rate data in European temperate grassland systems is shown. The effect of high stocking density on grass management is most apparent early in the growing season, and a 250m scale characterization of early spring vegetation growth from 2003-2012, based on MODIS NDVI time series products, is constructed. The average rate of growth is determined as a simple linear model for each pixel, using only the highest quality data for the period. These decadal spring growth model coefficients, start of season cover and growth rate, are regressed against log of stocking rate (r2 19 = 0.75, p<0.001). This model stocking rate is used to create a map of grassland use intensity in Ireland, which, when tested against an independent set of stocking data, is shown to be successful with an RMSE of 0.13 Livestock Unit/ha for a range of stocking densities from 0.1 to 3.3 Livestock Unit/ha. This model provides the first validated high resolution approach to mapping stocking rates in intensively managed European grassland systems. There is a demonstrated a need for a system to estimate current growing conditions. Using the spring growth model constructed for estimating stocking density a new style of grass growth progress anomaly map in the time-domain was developed. Using the developed satellite dataset 1 and 12 years of ground climate station data in Ireland, NDVI was modelled against time as a proxy for grass growth This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of “days behind” and “days ahead” of the norm. SPAT estimates for 2012 and 2013 are compared to ground based estimates from 30 climate stations and have a correlation coefficient of 0.897 and RMSE of 15days. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. This is understood by the author to be the first validated growth anomaly service, and the first for intensive European grasslands. The decisions on when to turn out cattle (the turn out date (TOD)) from winter housing to spring grazing is an important one on Irish dairy farms which has significant impacts on operating costs on the farm. To examine the relationship of TOD to conditions, the National Farm Survey (NFS) of Ireland database was geocoded and the data on turn out dates from 199 farms across Ireland over five years was used. A fixed effects linear panel data model was employed to explore the association between TOD and conditions, as it allows for unobserved variation between farmers to be ignored in favour of modelling the variance year on year. The environmental variables used in the analysis account for 38% of the variance in the turn out dates on farms nationwide. National seasonal conditions dominate over local variation, and for every week earlier grass grows in spring, farmers gain 3.7 days in grazing season but ignore 3.3 days of growth that could have been used. Every 100mm extra rain in spring means TOD is a day later and every dry day leads to turn out being half a day earlier. A well-drained soil makes TOD 2.5 days earlier compared to a poorly drained soil and TOD gets a day later for every 16km north form the south coast. This work demonstrates that precision agriculture 1 driven by optical and radar satellite data is closer to being a reality in Europe driven by enormous amounts of free imagery from NASA and the ESA Sentinel programs coupled with open source meteorological data and models and new developments in data analytics

APPLICATION OF HIERARCHICAL SPECIES DISTRIBUTION MODELS TO AVIAN SPECIES OF SOUTH DAKOTA AND THE UPPER MISSOURI RIVER BASIN

Recognizing the distributional patterns of species can inform management actions and increase scientific knowledge about species. Habitat Suitability Models (HSMs) are valuable tools in modeling species’ niches and effects of climate change and anthropogenic and natural disturbances on species’ distributions and abundances. In this dissertation, I expanded the application of hierarchical HSMs for a rare bird (Virginia’s warbler) and an economically valuable bird (ring-necked pheasant) in South Dakota. Also, we developed multiscale HSMs for grassland birds in the Upper Missouri River Basin (UMRB) to quantify current habitat associations and predict the influences of climate and landcover change associated with the implementation of bioenergy with carbon capture and storage (BECCS) and other carbon mitigation scenarios. We found that applying an Ensemble of Small Models (ESMs) approach within a hierarchical framework can lead to detailed information about niches of rare species, limiting factors at each habitat selection order, and potential distribution, which could help inform multiscale management. At the broadest habitat selection order, Virginia’s warbler had a narrow climatic niche. The importance of environmental variables changed across finer orders, such that at broader orders many covariates were important whereas at finer orders certain covariates became more important than others. For the model of pheasant abundance, my results showed that our hierarchical Bayesian approach allows for simultaneous selection of variables and scales of effect. I found that pheasant abundance was positively affected by intermediate levels of grassland cover. Scales of effect and spatiotemporal variation influenced predictor variable impacts on pheasant abundance. For the modeling of grassland birds across the UMRB, my results showed that the influence of climate change on abundance, distribution and species richness of grassland species is more pronounced than the influence of landcover changes due to implementing BECCS scenarios. This finding implies that regardless of landcover and land-use changes, climate change may limit or expand abundance and distribution of grassland bird species in the UMRB. Further, we found that grassland birds will be more affected by regional increases in temperature than decreases in precipitation

Statistical techniques for improving prediction in crop progress stages with meteorological and satellite data

Οι εκθέσεις προόδου της καλλιέργειας (CPR) του USDA παρουσιάζουν την εβδομαδιαία πρόοδο που σημειώθηκε στα διάφορα φαινολογικά στάδια των επιλεγμένων καλλιεργειών και ιδιαίτερα του καλαμποκιού. Σε αυτή την διπλωματική, ο στόχος μας ήταν να προβλέψουμε τα CPR ενός ολόκληρου έτους λαμβάνοντας υπόψη διαθέσιμα δεδομένα από συναφή χαρακτηριστικά με τρόπο που να μπορούμε να νικήσουμε τις προβλέψεις βάσει εμπειρικών μέσων από ιστορικά δεδομένα. Για το λόγο αυτό, χρησιμοποιήσαμε δύο χαρακτηριστικά, τον δείκτη κανονικοποιημένης βλάστησης (NDVI) και τις συγκεντρωτικές ημέρες καλλιέργειας (AGDDs). Προκειμένου να επιτευχθεί ο στόχος μας, εφαρμόσαμε αρκετές προσεγγίσεις μοντελοποίησης, συμπεριλαμβανομένων μοντέλων ανεξάρτητων μήξεων και κρυμμένα μοντέλα HMMs και συγκρίναμε διαφορετικούς τύπους εκτιμητών και προγνωστικών λαμβάνοντας υπόψη και τα δύο χαρακτηριστικά ή τη χωριστή επεξεργασία τους ή πραγματοποιώντας μετασχηματισμούς δεδομένων, όπως διαφορές. Τα αποτελέσματα έδειξαν ότι τα προαναφερθέντα μοντέλα δεν μπορούν να προβλέψουν καλύτερα από τα ιστορικά δεδομένα. Τέλος, κατορθώσαμε να λάβουμε καλύτερες προβλέψεις χρησιμοποιώντας απλή γραμμική παλινδρόμηση. Αυτή η μελέτη μπορεί να επεκταθεί σε διάφορες κατευθύνσεις για μελλοντικές εργασίες.Crop Progress Reports (CPRs) of the USDA are listing the weekly progress made in the different phenological stages of selected crops and in particular of corn. In this thesis, our goal was to predict the CPRs of a full year by taking into account available data from related features in a way that we can beat the predictions based on empirical means from historical data. For this reason, we used two features, the mean Normalized Difference Vegetation Index (NDVI) and the Accumulated Growing Degree Days (AGDDs). In order to achieve our target we implemented several modeling approaches, including Independent Mixture Models and Hidden Markov Models HMMs and we compared different type of estimators and predictors by taking into account both features or treating them separately, or making data transformations, such as differences. The results showed that the aforementioned models cannot predict better than the historical data. Finally, we managed to obtain better predictions by using Simple Linear Regression. This study can be extended in several directions for future work