Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain)

[EN] The abandonment of agricultural plots entails a low economic productivity of the land and a higher vulnerability to wildfires and degradation of affected areas. In this sense, the local government of Galicia is promoting new methodologies based on high-resolution images in order to classify the territory in basic and generic land uses. This procedure will be used to control the sustainable management of plots belonging to the Land Bank. This paper presents an application study for maintaining and updating land use/land cover geospatial databases using parcel-oriented classification. The test is performed over two geographic areas of Galicia, in the northwest of Spain. In this region, forest and shrublands in mountain environments are very heterogeneous with many private unproductive plots, some of which are in a high state of abandonment. The dataset is made of high spatial resolution multispectral imagery, cadastral cartography employed to define the image objects (plots), and field samples used to define evaluation and training samples. A set of descriptive features is computed quantifying different properties of the objects, i.e. spectral, texture, structural, and geometrical. Additionally, the effect on the classification and updating processes of the historical land use as a descriptive feature is tested. Three different classification methodologies are analyzed: linear discriminant analysis, decision trees, and support vector machine. The overall accuracies of the classifications obtained are always above 90 % and support vector machine method is proved to provide the best performance. Forest and shrublands areas are especially undefined, so the discrimination between these two classes is low. The results enable to conclude that the use of automatic parcel-oriented classification techniques for updating tasks of land use/land cover geospatial databases, is effective in the areas tested, particularly when broad and well defined classes are required.The authors appreciate the collaboration and support provided by Xunta de Galicia, Sociedade para o Desenvolvemento Comarcal de Galícia, and Banco de Terras de Galicia. The financial support provided by the Spanish Ministerio de Ciencia e Innovación in the framework of the projects CGL2010-19591/BTE and CGL2009-14220 is also acknowledged.Hermosilla, T.; Díaz Manso, J.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Fernández-Sarría, A.; Ferradáns Nogueira, P. (2012). Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain). Applied Geomatics. 4(4):245-255. https://doi.org/10.1007/s12518-012-0087-zS24525544Arikan M (2004) Parcel-based crop mapping through multi-temporal masking classification of landsat 7 images in Karacabey, Turkey. Int Arch Photogramm Remote Sens Spat Inf Sci 35:1085–1090Balaguer A, Ruiz LA, Hermosilla T, Recio JA (2010) Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Comput Geosci 36(2):231–240Balaguer-Besser A, Hermosilla T, Recio JA, Ruiz LA (2011) Semivariogram calculation optimization for object-oriented image classification. Model Sci Educ Learn 4(7):91–104Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm 65(1):2–16Cohen Y, Shoshany M (2000) Integration of remote sensing, GIS and expert knowledge in national knowledge-based crop recognition in Mediterranean environment. Int Arch Photogramm Remote Sens 33(Part B7):280–286Congalton R (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46Dadhwal VK, Singh RP, Dutta S, Parihar JS (2002) Remote sensing based crop inventory: a review of Indian experience. Trop Ecol 43(1):107–122De Wit AJW, Clevers JGPW (2004) Efficiency and accuracy of per-field classification for operational crop mapping. Int J Remote Sens 25:4091–4112Del Frate F, Pacifici F, Solimini D (2008) Monitoring urban land cover in Rome, Italy, and its changes by single-polarization multitemporal SAR images. IEEE J Sel Top Appl Earth Obs Remote Sens 1:87–97Díaz-Manso JM, Ferradáns-Nogueira P (2011) Modelo de uso actual da terra. In: Cobelle-Rico EJ, Diaz-Manso JM, Crecente-Maseda R, Martínez-Rivas EM (eds) Mercado e Mobilidade de Terras en Galícia, 1st edn. Servizo de Publicacións e Intercambio Científico, Santiago de Compostela, Spain, pp 31–44Dupas CA (2000) SAR and LANDSAT TM image fusion for land cover classification in the Brazilian Atlantic Forest Domain. Int Arch Photogramm Remote Sens XXXIII(Part B1):96–103El Kady M, Mack CB (1992) Remote sensing for crop inventory of Egypt’s old agricultural lands. Int Arch Photogramm Remote Sens 29:176–185Everitt BS, Dunn G (2001) Applied multivariate data analysis, 2nd edn. Edward Arnold, LondonHaralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Transact Syst Man Cybern 3(6):610–622Hermosilla T, Almonacid J, Fernández-Sarría A, Ruiz LA, Recio JA (2010) Combining features extracted from imagery and lidar data for object-oriented classification of forest areas. Int Arch Photogramm Remote Sens Spat Inf Sci 38(4/C7)Hernández Orallo J, Ramírez Quintana MJ, Ferri Ramírez C (2004) Introducción a la minería de datos. Pearson Educación S.A, MadridHomer C, Huang C, Yang L, Wylie B, Coan M (2004) Development of a 2001 National Land-Cover Database for the United States. Photogramm Eng Remote Sens 70:829–840Huberty CJ (1994) Applied discriminant analysis. Wiley, New YorkLaws KI (1985) Goal-directed texture image segmentation. Appl Artif Intel II, SPIE 548:19–26Ormeci C, Alganci U, Sertel E (2010) Identification of crop areas using SPOT-5 data, FIG Congress 2010 Facing the Challenges—building the capacity. Sydney, Australia, pp 11–16Peled A, Gilichinsky M (2004) GIS-driven analyses of remotely sensed data for quality assessment of existing land cover classification. Int Arch Photogramm Remote Sens Spat Inf Sci 35Peled A, Gilichinsky M (2010) Knowledge-based classification of land cover for the quality assessment of GIS database. Int Arch Photogramm Remote Sens Spat Inf Sci 38:217–222Perveen F, Nagasawa R, Ali S, Husnain (2008) Evaluation of ASTER spectral bands for agricultural land cover mapping using pixel-based and object-based classification approaches. Int Arch Photogramm Remote Sens Spat Inf Sci 37(4-C1)Petit CC, Lambin EF (2002) Impact of data integration technique on historical land-use/land-cover change: comparing historical maps with remote sensing data in the Belgian Ardennes. Landsc Ecol 17:117–132Quinlan JR (1993) C4.5: Programs for machine learning. Kaufmann, San FranciscoRabe A, van der Linden S, Hostert P (2010) imageSVM, Version 2.1. www.hu-geomatics.deRecio JA, Hermosilla T, Ruiz LA, Fernández-Sarría A (2011) Historical land use as a feature for image classification. Photogramm Eng Remote Sens 77(4):377–387Ruiz LA, Fernández-Sarría A, Recio JA (2004) Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study. Int Arch Photogramm Remote Sens Spat Inf Sci 35(B4):1109–1115Ruiz LA, Recio JA, Hermosilla T, Fdez. Sarriá A (2009) Identification of agricultural and land cover database changes using object-oriented classification techniques. 33rd International Symposium on Remote Sensing of Environment, May 4–8, Stresa (Italy)Ruiz LA, Recio JA, Fernández-Sarría A, Hermosilla T (2011) A feature extraction software tool for agricultural object-based image analysis. Comput Electron Agric 76(4):284–296Tansey K, Chambers I, Anstee A, Denniss A, Lamb A (2009) Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas. Appl Geogr 29(2):145–157van der Linden S, Rabe A, Wirth F, Suess S, Okujeni A, Hostert P (2010) imageSVM regression, application manual: imageSVM version 2.1. Humboldt-Universität zu Berlin, GermanyVapnik VN (1998) Statistical learning theory. Wiley, New YorkWalsh SJ, McCleary AL, Mena CF, Shao Y, Tuttle JP, Gonzalez A, Atkinson R (2008) QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: implications for control and land use management. Remote Sens Environ 112(5):1927–1941Walter V (2004) Object-based classification of remote sensing data for change detection. ISPRS J Photogramm Remote Sens 58:225–238Walter V (2005) Object-based evaluation of lidar and multiespectral data for automatic change detection in GIS databases. Geo-Inf Syst 18:10–15Zaragozí, B, Rabasa, A, Rodríguez-Sala, JJ, Navarro, JT, Belda, A, Ramón, A (2012) Modelling farmland abandonment: A study combining GIS and data mining techniques. Agric Ecosys Environ 155:124–132Zhang S, Liu X (2005) Realization of data mining model for expert classification using multi-scale spatial data. Int Arch Photogramm Remote Sens Spat Inf Sci 26(4/W6):107–11

Toward a Comprehensive Approach to the Collection and Analysis of Pica Substances, with Emphasis on Geophagic Materials

Pica, the craving and subsequent consumption of non-food substances such as earth, charcoal, and raw starch, has been an enigma for more than 2000 years. Currently, there are little available data for testing major hypotheses about pica because of methodological limitations and lack of attention to the problem.In this paper we critically review procedures and guidelines for interviews and sample collection that are appropriate for a wide variety of pica substances. In addition, we outline methodologies for the physical, mineralogical, and chemical characterization of these substances, with particular focus on geophagic soils and clays. Many of these methods are standard procedures in anthropological, soil, or nutritional sciences, but have rarely or never been applied to the study of pica.Physical properties of geophagic materials including color, particle size distribution, consistency and dispersion/flocculation (coagulation) should be assessed by appropriate methods. Quantitative mineralogical analyses by X-ray diffraction should be made on bulk material as well as on separated clay fractions, and the various clay minerals should be characterized by a variety of supplementary tests. Concentrations of minerals should be determined using X-ray fluorescence for non-food substances and inductively coupled plasma-atomic emission spectroscopy for food-like substances. pH, salt content, cation exchange capacity, organic carbon content and labile forms of iron oxide should also be determined. Finally, analyses relating to biological interactions are recommended, including determination of the bioavailability of nutrients and other bioactive components from pica substances, as well as their detoxification capacities and parasitological profiles.This is the first review of appropriate methodologies for the study of human pica. The comprehensive and multi-disciplinary approach to the collection and analysis of pica substances detailed here is a necessary preliminary step to understanding the nutritional enigma of non-food consumption

Combined measurement of differential and total cross sections in the H → γγ and the H → ZZ* → 4ℓ decay channels at s=13 TeV with the ATLAS detector

A combined measurement of differential and inclusive total cross sections of Higgs boson production is performed using 36.1 fb−1 of 13 TeV proton–proton collision data produced by the LHC and recorded by the ATLAS detector in 2015 and 2016. Cross sections are obtained from measured H→γγ and H→ZZ*(→4ℓ event yields, which are combined taking into account detector efficiencies, resolution, acceptances and branching fractions. The total Higgs boson production cross section is measured to be 57.0−5.9 +6.0 (stat.) −3.3 +4.0 (syst.) pb, in agreement with the Standard Model prediction. Differential cross-section measurements are presented for the Higgs boson transverse momentum distribution, Higgs boson rapidity, number of jets produced together with the Higgs boson, and the transverse momentum of the leading jet. The results from the two decay channels are found to be compatible, and their combination agrees with the Standard Model predictions

Search for High-Mass Resonances Decaying to τν in pp Collisions at √s=13 TeV with the ATLAS Detector

A search for high-mass resonances decaying to τν using proton-proton collisions at √s=13 TeV produced by the Large Hadron Collider is presented. Only τ-lepton decays with hadrons in the final state are considered. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 36.1 fb−1. No statistically significant excess above the standard model expectation is observed; model-independent upper limits are set on the visible τν production cross section. Heavy W′ bosons with masses less than 3.7 TeV in the sequential standard model and masses less than 2.2–3.8 TeV depending on the coupling in the nonuniversal G(221) model are excluded at the 95% credibility level

Search for the direct production of charginos and neutralinos in final states with tau leptons in √s=13 TeV collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with at least two hadronically decaying tau leptons is presented. The analysis uses a dataset of pp collisions corresponding to an integrated luminosity of 36.1 fb−1, recorded with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of 13TeV.Nosignificant deviation from the expected Standard Model background is observed. Limits are derived in scenarios of ˜χ+1 ˜χ−1 pair production and of ˜χ±1 ˜χ02 and ˜χ+1 ˜χ−1 production in simplified models where the neutralinos and charginos decay solely via intermediate left-handed staus and tau sneutrinos, and the mass of the ˜ τL state is set to be halfway between the masses of the ˜χ±1 and the ˜χ01. Chargino masses up to 630 GeV are excluded at 95% confidence level in the scenario of direct production of ˜χ+1 ˜χ−1 for a massless ˜χ01. Common ˜χ±1 and ˜χ02 masses up to 760 GeV are excluded in the case of production of ˜χ±1 ˜χ02 and ˜χ+1 ˜χ−1 assuming a massless ˜χ01. Exclusion limits for additional benchmark scenarios with large and small mass-splitting between the ˜χ±1 and the ˜χ01 are also studied by varying the ˜ τL mass between the masses of the ˜χ±1 and the ˜χ01

Measurement of differential cross sections and W + /W − cross-section ratios for W boson production in association with jets at √s =8 TeV with the ATLAS detector

This paper presents a measurement of the W boson production cross section and the W + /W − cross-section ratio, both in association with jets, in proton--proton collisions at s √ =8 TeV with the ATLAS experiment at the Large Hadron Collider. The measurement is performed in final states containing one electron and missing transverse momentum using data corresponding to an integrated luminosity of 20.2 fb −1 . Differential cross sections for events with one or two jets are presented for a range of observables, including jet transverse momenta and rapidities, the scalar sum of transverse momenta of the visible particles and the missing transverse momentum in the event, and the transverse momentum of the W boson. For a subset of the observables, the differential cross sections of positively and negatively charged W bosons are measured separately. In the cross-section ratio of W + /W − the dominant systematic uncertainties cancel out, improving the measurement precision by up to a factor of nine. The observables and ratios selected for this paper provide valuable input for the up quark, down quark, and gluon parton distribution functions of the proto

Operation and performance of the ATLAS Tile Calorimeter in Run 1

The Tile Calorimeter is the hadron calorimeter covering the central region of the ATLAS experiment at the Large Hadron Collider. Approximately 10,000 photomultipliers collect light from scintillating tiles acting as the active material sandwiched between slabs of steel absorber. This paper gives an overview of the calorimeter’s performance during the years 2008–2012 using cosmic-ray muon events and proton–proton collision data at centre-of-mass energies of 7 and 8TeV with a total integrated luminosity of nearly 30 fb−1. The signal reconstruction methods, calibration systems as well as the detector operation status are presented. The energy and time calibration methods performed excellently, resulting in good stability of the calorimeter response under varying conditions during the LHC Run 1. Finally, the Tile Calorimeter response to isolated muons and hadrons as well as to jets from proton–proton collisions is presented. The results demonstrate excellent performance in accord with specifications mentioned in the Technical Design Report

Search for supersymmetry in events with four or more leptons in √s =13 TeV pp collisions with ATLAS

Results from a search for supersymmetry in events with four or more charged leptons (electrons, muons and taus) are presented. The analysis uses a data sample corresponding to 36.1 fb −1 of proton-proton collisions delivered by the Large Hadron Collider at s √ =13 TeV and recorded by the ATLAS detector. Four-lepton signal regions with up to two hadronically decaying taus are designed to target a range of supersymmetric scenarios that can be either enriched in or depleted of events involving the production and decay of a Z boson. Data yields are consistent with Standard Model expectations and results are used to set upper limits on the event yields from processes beyond the Standard Model. Exclusion limits are set at the 95% confidence level in simplified models of General Gauge Mediated supersymmetry, where higgsino masses are excluded up to 295 GeV. In R -parity-violating simplified models with decays of the lightest supersymmetric particle to charged leptons, lower limits of 1.46 TeV, 1.06 TeV, and 2.25 TeV are placed on wino, slepton and gluino masses, respectively

Performance of missing transverse momentum reconstruction with the ATLAS detector using proton–proton collisions at √s = 13 TeV

The performance of the missing transverse momentum (EmissT) reconstruction with the ATLAS detector is evaluated using data collected in proton–proton collisions at the LHC at a centre-of-mass energy of 13 TeV in 2015. To reconstruct EmissT, fully calibrated electrons, muons, photons, hadronically decaying τ -leptons, and jets reconstructed from calorimeter energy deposits and charged-particle tracks are used. These are combined with the soft hadronic activity measured by reconstructed charged-particle tracks not associated with the hard objects. Possible double counting of contributions from reconstructed charged-particle tracks from the inner detector, energy deposits in the calorimeter, and reconstructed muons from the muon spectrometer is avoided by applying a signal ambiguity resolution procedure which rejects already used signals when combining the various EmissT contributions. The individual terms as well as the overall reconstructed EmissT are evaluated with various performance metrics for scale (linearity), resolution, and sensitivity to the data-taking conditions. The method developed to determine the systematic uncertainties of the EmissT scale and resolution is discussed. Results are shown based on the full 2015 data sample corresponding to an integrated luminosity of 3.2 fb−1

Measurement of the t¯tZ and t¯tW cross sections in proton-proton collisions at √s=13 TeV with the ATLAS detector

A measurement of the associated production of a top-quark pair (t¯t) with a vector boson (W, Z) in proton-proton collisions at a center-of-mass energy of 13 TeV is presented, using 36.1  fb−1 of integrated luminosity collected by the ATLAS detector at the Large Hadron Collider. Events are selected in channels with two same- or opposite-sign leptons (electrons or muons), three leptons or four leptons, and each channel is further divided into multiple regions to maximize the sensitivity of the measurement. The t¯tZ and t¯tW production cross sections are simultaneously measured using a combined fit to all regions. The best-fit values of the production cross sections are σt¯tZ=0.95±0.08stat±0.10syst pb and σt¯tW=0.87±0.13stat±0.14syst pb in agreement with the Standard Model predictions. The measurement of the t¯tZ cross section is used to set constraints on effective field theory operators which modify the t¯tZ vertex