Journal Paper Digests

Journal Paper Digests 2017 #18

• A MATLAB based 3D modeling and inversion code for MT data
• Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods
• Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI3g data
• Spatio-ecological complexity measures in GRASS GIS
• Predicting the abundance of clays and quartz in oil sands using hyperspectral measurements
• Assessing gaps in irrigated agricultural productivity through satellite earth observations-A case study of the Fergana Valley, Central Asia
• Forecast of wheat yield throughout the agricultural season using optical and radar satellite images
• Rapid prediction of soil mineralogy using imaging spectroscopy
• Comparison of Soil Water Potential Sensors: A Drying Experiment
• Essentials in Soil Physics: An introduction to Soil Processes, Functions, Structure and Mechanics
• Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI
• Honey authentication based on physicochemical parameters and phenolic compounds
• Interoperable agro-meteorological observation and analysis platform for precision agriculture: A case study in citrus crop water requirement estimation
• An adaptive approach for UAV-based pesticide spraying in dynamic environments
• A comparison of SMOS and AMSR2 soil moisture using representative sites of the OzNet monitoring network

A MATLAB based 3D modeling and inversion code for MT data

Authors: Singh, A; Dehiya, R; Gupta, PK; Israil, M

Source: COMPUTERS & GEOSCIENCES, 104 1-11; JUL 2017

Abstract: The development of a MATLAB based computer code, AP3DMT, for modeling and inversion of 3D Magnetotelluric (MT) data is presented. The code comprises two independent components: grid generator code and modeling/inversion code. The grid generator code performs model discretization and acts as an interface by generating various I/O files. The inversion code performs core computations in modular form forward modeling, data functionals, sensitivity computations and regularization. These modules can be readily extended to other similar inverse problems like Controlled-Source EM (CSEM). The modular structure of the code provides a framework useful for implementation of new applications and inversion algorithms. The use of MATLAB and its libraries makes it more compact and user friendly. The code has been validated on several published models. To demonstrate its versatility and capabilities the results of inversion for two complex models are presented.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

Authors: Erener, A; Sivas, AA; Selcuk-Kestel, AS; Duzgun, HS

Source: COMPUTERS & GEOSCIENCES, 104 62-74; JUL 2017

Abstract: All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1’s and 0’s to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of l’s and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model’s performance.

Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI3g data

Authors: Gerber, F; Mosinger, K; Furrer, R

Source: COMPUTERS & GEOSCIENCES, 104 109-119; JUL 2017

Abstract: Software packages for spatial data often implement a hybrid approach of interpreted and compiled programming languages. The compiled parts are usually written in C, C++, or Fortran, and are efficient in terms of computational speed and memory usage. Conversely, the interpreted part serves as a convenient user interface and calls the compiled code for computationally demanding operations. The price paid for the user friendliness of the interpreted component is besides performance the limited access to low level and optimized code. An example of such a restriction is the 64-bit vector support of the widely used statistical language R. On the R side, users do not need to change existing code and may not even notice the extension. On the other hand, interfacing 64-bit compiled code efficiently is challenging. Since many R packages for spatial data could benefit from 64-bit vectors, we investigate strategies to efficiently pass 64-bit vectors to compiled languages. More precisely, we show how to simply extend existing R packages using the foreign function interface to seamlessly support 64-bit vectors. This extension is shown with the sparse matrix algebra R package spam. The new capabilities are illustrated with an example of GIMMS NDVI3g data featuring a parametric modeling approach for a non-stationary covariance matrix.

Spatio-ecological complexity measures in GRASS GIS

Authors: Rocchini, D; Petras, V; Petrasova, A; Chemin, Y; Ricotta, C; Frigeri, A; Landa, M; Marcantonio, M; Bastin, L; Metz, M; Delucchi, L; Neteler, M

Source: COMPUTERS & GEOSCIENCES, 104 166-176; JUL 2017

Abstract: Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.

Predicting the abundance of clays and quartz in oil sands using hyperspectral measurements

Authors: Entezari, I; Rivard, B; Geramian, M; Lipsett, MG

Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 59 1-8; JUL 2017

Abstract: Clay minerals play a crucial role in the processability of oil sands ores and in the management of tailings. An increase in fine content generally leads to a decrease in both bitumen recovery performance and tailings settling rate. It is thus important to identify clay types and their abundance in oil sands ores and tailings. This study made use of oil sands samples characterized for quantitative mineralogy by x-ray diffraction, to gain an understanding of changes in the reflectance spectra of oil sands. The sample suite included bitumen-removed oil sands ore samples and their different fine size fractions. Spectral metrics applicable to the prediction of quartz and clay contents in oil sands were then derived with a focus on metrics correlating with sample content in total 2:1 clays (total of illite and illite-smectite) and kaolinite. Metrics in the shortwave infrared (SWIR) and longwave infrared (LWIR) were found to correlate with mineral contents. The best predictions of clays and quartz were achieved using LWIR metrics (R-2 > 0.89). Results also demonstrated the applicability of LWIR metrics in the prediction of kaolinite and total 2:1 clays.

Assessing gaps in irrigated agricultural productivity through satellite earth observations-A case study of the Fergana Valley, Central Asia

Authors: Low, F; Biradar, C; Fliemann, E; Lamers, JPA; Conrad, C

Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 59 118-134; JUL 2017

Abstract: Improving crop area and/or crop yields in agricultural regions is one of the foremost scientific challenges for the next decades. This is especially true in irrigated areas because sustainable intensification of irrigated crop production is virtually the sole means to enhance food supply and contribute to meeting food demands of a growing population. Yet, irrigated crop production worldwide is suffering from soil degradation and salinity, reduced soil fertility, and water scarcity rendering the performance of irrigation schemes often below potential. On the other hand, the scope for improving irrigated agricultural productivity remains obscure also due to the lack of spatial data on agricultural production (e.g. crop acreage and yield). To fill this gap, satellite earth observations and a replicable methodology were used to estimate crop yields at the field level for the period 2010/2014 in the Fergana Valley, Central Asia, to understand the response of agricultural productivity to factors related to the irrigation and drainage infrastructure and environment. The results showed that cropping pattern, i.e. the presence or absence of multi-annual crop rotations, and spatial diversity of crops had the most persistent effects on crop yields across observation years suggesting the need for introducing sustainable cropping systems. On the other hand, areas with a lower crop diversity or abundance of crop rotation tended to have lower crop yields, with differences of partly more than one t/ha yield. It is argued that factors related to the infrastructure, for example, the distance of farms to the next settlement or the density of roads, had a persistent effect on crop yield dynamics over time. The improvement potential of cotton and wheat yields were estimated at 5%, compared to crop yields of farms in the direct vicinity of settlements or roads. In this study it is highlighted how remotely sensed estimates of crop production in combination with geospatial technologies provide a unique perspective that, when combined with field surveys, can support planners to identify management priorities for improving regional production and/or reducing environmental impacts.

Forecast of wheat yield throughout the agricultural season using optical and radar satellite images

Authors: Fieuzal, R; Baup, F

Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 59 147-156; JUL 2017

Abstract: The aim of this study is to estimate the capabilities of forecasting the yield of wheat using an artificial neural network combined with multi-temporal satellite data acquired at high spatial resolution throughout the agricultural season in the optical and/or microwave domains. Reflectance (acquired by Formosat-2, and Spot 4-5 in the green, red, and near infrared wavelength) and multi-configuration backscattering coefficients (acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, at co- (abbreviated HH and VV) and cross-polarization states (abbreviated HV and VH)) constitute the input variable of the artificial neural networks, which are trained and validated on the successively acquired images, providing yield forecast in near real-time conditions. The study is based on data collected over 32 fields of wheat distributed over a study area located in southwestern France, near Toulouse. Among the tested sensor configurations, several satellite data appear useful for the yield forecasting throughout the agricultural season (showing coefficient of determination (R-2) larger than 0.60 and a root mean square error (RMSE) lower than 9.1 quintals by hectare (q ha(-1))): C-vH, C-Hv, or the combined used of X-HH and C-HH, C-HH and C-Hv, or green reflectance and C-HH. Nevertheless, the best accurate forecast (R-2 = 0.76 and RMSE= 7.0 q ha(-1)) is obtained longtime before the harvest (on day 98, during the elongation of stems) using the combination of co- and cross-polarized backscattering coefficients acquired in the C-band (C-vv and C-vH). These results highlight the high interest of using synthetic aperture radar (SAR) data instead of optical ones to early forecast the yield before the harvest of wheat.

Rapid prediction of soil mineralogy using imaging spectroscopy

Authors: Omran, ESE

Source: EURASIAN SOIL SCIENCE, 50 (5):597-612; MAY 2017

Abstract: Hyperspectral images provide rich spectral and spatially continuous information that can be used for soil mineralogy discrimination. This paper proposes a method to evaluate the feasibility of Hyperion image in the rapid prediction of soil mineralogy. Four areas in Egypt were chosen for the current study. Preprocessing of the Hyperion data was done before applying the atmospheric correction. The minimum noise fraction transformation was used to segregate noise in the data. Various techniques were applied to the studied areas in which mixture tune matched filtering gave good results in a prediction of the end-members. Then, it employed to predict soil minerals in each cell using a spectral unmixing method. Illite, chlorite, calcite, dolomite, kaolinite, smectite, quartz, hematite, goethite, vermiculite, palygorskite and some feldspar were identified. In addition, sand and limestone, calcite and dolomite, and sand surface from similarly bright clouds can be distinguished easily based on the proposed method. The soil minerals obtained from X-ray diffraction analysis of the soil samples are in conformity with spectrally dominant mineralogy from Hyperion data. Different minerals can be identified using this method without any knowledge of field spectra or any a priori field data, thus configuring a “true” remote sensing method.

Comparison of Soil Water Potential Sensors: A Drying Experiment

Authors: Degre, A; van der Ploeg, MJ; Caldwell, T; Gooren, HPA

Source: VADOSE ZONE JOURNAL, 16 (4):NIL_4-NIL_11; APR 2017

Abstract: The soil water retention curve (WRC) plays a major role in a soil’s hydrodynamic behavior. Many measurement techniques are currently available for determining the WRC in the laboratory. Direct in situ WRC can be obtained from simultaneous soil moisture and water potential readings covering a wide tension range, from saturation to the wilting point. There are many widely used soil moisture probes. Whereas near-saturation tension can be measured using water-filled tensiometers, wider ranges of water potential require new, more expensive, and less widely used probes. We compared three types of soil water potential sensors that could allow us to measure water potential in the field, with a range relevant to water uptake by plants. Polymer tensiometers (POTs), MPS-2 probes, and pF meters were compared in a controlled drying experiment. The study showed that the POTs and MPS-2 probes had good reliability in their respective ranges. Combined with a soil moisture probe, these two sensors can provide observed WRCs. The pF meters below -30 kPa were inaccurate, and their response was sensitive to measurement interval, with greater estimated suction at shorter measurement intervals. In situ WRC can provide supplementary information, particularly with regard to its spatial and temporal variability. It could also improve the results of other measurement techniques, such as geophysical observations.

Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI

Authors: Whetton, R; Zhao, YF; Shaddad, S; Mouazen, AM

Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 138 127-136; JUN 1 2017

Abstract: This paper explores the use of a novel nonlinear parametric modelling technique based on a Volterra Non-linear Regressive with eXogenous inputs (VNRX) method to quantify the individual, interaction and overall contributions of six soil properties on crop yield and normalised difference vegetation index (NDVI). The proposed technique has been applied on high sampling resolution data of soil total nitrogen (TN) in %, total carbon (TC) in %, potassium (K) in cmol kg(-1), pH, phosphorous (P) in mg kg-I and moisture content (MC) in %, collected with an on-line visible and near infrared (VIS-NIR) spectroscopy sensor from a 18 ha field in Bedfordshire, UK over 2013 (wheat) and 2015 (spring barley) cropping seasons. The online soil data were first subjected to a raster analysis to produce a common 5 m by 5 m grid, before they were used as inputs into the VNRX model, whereas crop yield and NDVI represented system outputs. Results revealed that the largest contributions commonly observed for both yield and NDVI were from K, P and TC. The highest sum of the error reduction ratio (SERR) of 48.59% was calculated with the VNRX model for NDVI, which was in line with the highest correlation coefficient (r) of 0.71 found between measured and predicted NDVI. However, on-line measured soil properties led to larger contributions to early measured NDVI than to a late measurement in the growing season. The performance of the VNRX model was better for NDVI than for yield, which was attributed to the exclusion of the influence of crop diseases, appearing at late growing stages. It was recommended to adopt the VNRX method for quantifying the contribution of on-line collected soil properties to crop NDVI and yield. However, it is important for future work to include additional soil properties and to account for other factors affecting crop growth and yield, to improve the performance of the VNRX model.

Honey authentication based on physicochemical parameters and phenolic compounds

Authors: Oroian, M; Sorina, R

Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 138 148-156; JUN 1 2017

Abstract: The aim of this study is to assess the usefulness of physicochemical parameters (pH, water activity, free acidity, refraction index, Brix, moisture content and ash content), color parameters (L, a, b*, chroma, hue angle and yellow index) and phenolics (quercetin, apigenin, myricetin, isorhamnetin, kaempherol, caffeic acid, chrysin, galangin, luteolin, p-coumaric acid, gallic acid and pinocembrin) in view of classifying honeys according to their botanical origin (acacia, tilia, sunflower, honeydew and polyfloral). Thus, the classification of honeys has been made using the principal component analysis (PCA), linear discriminant analysis (LDA) and artificial neural networks (ANN). The multilayer perceptron network with 2 hidden layers classified correctly 94.8% of the cross validated samples.

Interoperable agro-meteorological observation and analysis platform for precision agriculture: A case study in citrus crop water requirement estimation

Authors: Sawant, S; Durbha, SS; Adinarayana, J

Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 138 175-187; JUN 1 2017

Abstract: Advances in Internet of Things (IoT) based sensing systems have improved capabilities to precisely monitor environmental conditions. Plants are sessile organisms and are affected by biotic and abiotic stresses caused due to surrounding environmental conditions such as soil water content, pest/disease infestation, and soil health. High-resolution sensing (Wireless Sensor Networks (WSN) Systems) of agrometeorological parameters helps to solve critical issues about the crop-weather-soil continuum. Currently, many WSN systems are deployed all over the World for precision agriculture purposes. Although there have been many improvements in the communication aspects of the WSN’s, the data dissemination and near real-time analysis components for taking dynamic decision, particularly in agriculture domain has not matured. The current WSN systems do not have a standardized way of data discovery, access, and sharing, which impedes the integration of data across various distributed sensor networks. This study addresses above issues through the adaptation of a framework based on Open Geospatial Consortium (OGC) standards for Sensor Web Enablement (SWE). For precision agriculture applications a cost-effective, standardized sensing system (hardware and software) has been developed, which includes functionalities such as sensors plug-n-play, remote monitoring, tools for crop water requirement estimation, pest, disease monitoring, and nutrient management. Also, the modeling techniques were integrated with the interoperable web-enabled sensing system for addressing water management problems of horticultural crops in semi-arid areas.

An adaptive approach for UAV-based pesticide spraying in dynamic environments

Authors: Faical, BS; Freitas, H; Gomes, PH; Mano, LY; Pessin, G; de Carvalho, ACPLF; Krishnamachari, B; Ueyama, J

Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 138 210-223; JUN 1 2017

Abstract: Agricultural production has become a key factor for the stability of the world economy. The use of pesticides provides a more favorable environment for the crops in agricultural production. However, the uncontrolled and inappropriate use of pesticides affect the environment by polluting preserved areas and damaging ecosystems. In the precision agriculture literature, several authors have proposed solutions based on Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) for developing spraying processes that are safer and more precise than the use of manned agricultural aircraft. However, the static configuration usually adopted in these proposals makes them inefficient in environments with changing weather conditions (e.g. sudden changes of wind speed and direction). To overcome this deficiency, this paper proposes a computer-based system that is able to autonomously adapt the UAV control rules, while keeping precise pesticide deposition on the target fields. Different versions of the proposal, with autonomously route adaptation metaheuristics based on Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Hill-Climbing for optimizing the intensity of route changes are evaluated in this study. Additionally, this study evaluates the use of a ground control station and an embedded hardware to run the route adaptation metaheuristics. Experimental results show that the proposed computer-based system approach with autonomous route change metaheuristics provides more precise changes in the UAV’s flight route, with more accurate deposition of the pesticide and less environmental damage.

A comparison of SMOS and AMSR2 soil moisture using representative sites of the OzNet monitoring network

Authors: Yee, MS; Walker, JP; Rudiger, C; Parinussa, RM; Koike, T; Kerr, YH

Source: REMOTE SENSING OF ENVIRONMENT, 195 297-312; JUN 15 2017

Abstract: This paper evaluates the performance of different soil moisture products from AMSR2 and SMOS against the most representative stations within the Yanco study area in the Murrumbidgee catchment, in southeast Australia. AMSR2 Level 3 (L3) soil moisture products retrieved from two versions of brightness temperatures using the Japanese Aerospace eXploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithm were included. For the LPRM algorithm, two different parameterization methods were applied. Furthermore, two versions of SMOS L3 soil moisture product were assessed. The results are contrasted against the use of “random” stations. Accounting for all versions, frequencies and overpasses, the latest versions of the JAXA UX2) and LPRM (LP3) products were found to surpass the earlier versions (JX1, LP1 and LP2). Soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used but not X-band. However, X-band retrievals (r: 0.71, MAE: 0.07, RMSD: 0.08 m3/m3) were found to perform better than C-band (r: 0.68-0.70, MAE: 0.070.09 m(3)/m(3), RMSD: 0.09-0.10 m(3)/m(3)). Moreover, an intercomparison between different acquisition times (morning and evening) of AMSR2 X-band products found a better performance from evening overpasses (1:30 pm; r: 0.69-0.77) as opposed to morning overpasses (1:30 am; r: 0.47-0.66). In the case of SMOS, morning (6:00 am; r: 0.77) retrievals were found to be superior over evening (6:00 pm; r: 0.69) retrievals. Overall, both versions of JAXA products, the second and third versions of LPRM X-band products, and two versions of SMOS products were found to meet the mean average error (MAE) goal accuracy of the AMSR2 mission (MAE < 0.08 m(3)/m(3)) but none of the products achieved the SMOS goal of RMSD < 0.04 m(3)/m(3). Furthermore, performance of the products differed depending on the statistic used to evaluate them. Consequently, considering the results in this study, JX2 products are recommended if both absolute and temporal accuracy of the soil moisture product is of importance, whereas LP3(x) products from evening observations and SMOS version 3.00 (SMOS2) products from morning overpasses are recommended if temporal accuracy is of greater importance.