Journal Paper Digests 2018 #11
- Epikarst mapping by remote sensing
- Predictive mapping of soil-landscape relationships in the arid Southwest United States
- Global linkages between teleconnection patterns and the terrestrial biosphere
- A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images
- Towards machine ecoregionalization of Earth’s landmass using pattern segmentation method
- Laser-based spectroscopic methods to evaluate the humification degree of soil organic matter in whole soils: a review
- Better models are more effectively connected models
- Multi-scale relief model (MSRM): a new algorithm for the visualization of subtle topographic change of variable size in digital elevation models
- Deep learning in agriculture: A survey
- Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series
- How far are we from the use of satellite rainfall products in landslide forecasting?
- Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa
- Accounting for non-stationary variance in geostatistical mapping of soil properties
- Global environmental costs of China’s thirst for milk
- Exploring the case for a national-scale soil conservation and soil condition framework for evaluating and reporting on environmental and land use policies
Epikarst mapping by remote sensing
Authors: Pardo-Iguzquiza, E; Dowd, PA; Ruiz-Constan, A; Martos-Rosillo, S; Luque-Espinar, JA; Rodriguez-Galiano, V; Pedrera, A
Source: CATENA, 165 1-11; JUN 2018
Abstract: Epikarst the shallow, surficial part of a karstic massif - has a significant influence on the spatio-temporal variability of recharge and the hydrodynamic functioning of many karst aquifers. In the Mediterranean morphoclimatic zone, the average thickness of a well-developed epikarst is around ten metres, but the spatial patterns of its degree of development are very heterogeneous due to the complex interaction of a number of different factors such as lithology, fracturing, weathering, soil and vegetation. In addition, direct field observation is difficult because good outcropping conditions are restricted to particular locations, some areas are not accessible and the size of the study area is often too large for exhaustive field surveys. Satellite-based remote sensing, however, provides a complete coverage of an entire area with spectral resolutions that detect variability in features that can define image textures related to the development of the epikarst. This paper describes a quantitative methodology for epikarst mapping using satellite images and field data. The proposed method comprises an unsupervised classification to define the spectral signature of each of three epikarst development categories in a high-resolution satellite image followed by a supervised classification of the terrain into one of the three categories on a low spatial resolution scale. The training areas in the field are assigned to the three categories by a panel of experts using the Delphi method. Geophysical data are used for validation to overcome any bias that may be introduced by the panel. The proposed methodology has been applied to the Sierra de las Nieves karstic aquifer (Malaga, southern Spain). The outcome is a map of estimated epikarst development that is an approximation to reality and which can be improved as more experimental data become available.
Predictive mapping of soil-landscape relationships in the arid Southwest United States
Authors: Regmi, NR; Rasmussen, C
Source: CATENA, 165 473-486; JUN 2018
Abstract: Multi-scale geospatial and absolute variation of surface and near-surface soil physical and chemical properties can be mapped and quantified by coupling digital soil mapping techniques with high resolution remote sensing products. The goal of this research was to advance data-driven digital soil mapping techniques by developing an approach that can integrate multi-scale digital surface topography and reflectance-derived remote sensing products, and characterize multi-scale soil-landscape relations of Quaternary alluvial and eolian deposits. The study area spanned the arid landscape encompassed by the Barry M. Goldwater Range West (BMGRW), which is administered by the Marine Corps Air Station Yuma, in southwestern Arizona, USA. An iterative principal component analysis (iPCA) was implemented for LiDAR elevation- and Landsat ETM + -derived soil predictors, termed environmental covariates. Principal components that characterize > 95% of covariate space variability were then integrated and classified using an ISODATA (Iterative Self-Organizing Data) unsupervised technique. The classified map was further segmented into polygons based on a region growing algorithm, yielding multi scale maps of soil-landscape relations that were compared with maps of soil landforms identified from aerial photographs, satellite images and field observation. The approach identified and mapped the spatial variability of soil-landscape relationships in alluvial and eolian deposits and illustrated the applicability of coupling covariate selection and integration by iPCA, ISODATA classification of integrated data layers, and image segmentation for effective spatial prediction of soil landscape characteristics. The approach developed here is data driven, applicable for multi-scale mapping, allows incorporation of a wide variety of covariates, and maps spatially homogenous soil-landscape units that are necessary for hydrologic models, land and ecosystem management decisions, and hazard assessment.
Global linkages between teleconnection patterns and the terrestrial biosphere
Authors: Dahlin, KM; Ault, TR
Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 69 56-63; JUL 2018
Abstract: Interannual variability In the global carbon cycle is largely due to variations in carbon uptake by terrestrial ecosystems, yet linkages between climate variability and variability in the terrestrial carbon cycle are not well understood at the global scale. Using a 30-year satellite record of semi-monthly leaf area index (LAI), we show that four modes of climate variability El Nino/Southern Oscillation, the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Indian Ocean Dipole Mode strongly impact interannual vegetation growth patterns, with 68% of the land surface impacted by at least one of these teleconnection patterns, yet the spatial distribution of these impacts is heterogeneous. Considering the patterns’ impacts by biome, none has an exclusively positive or negative relationship with LAI. Our findings imply that future changes in the frequency and/or magnitude of teleconnection patterns will lead to diverse changes to the terrestrial biosphere and the global carbon cycle.
A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images
Authors: Wang, ZH; Yang, XM; Lu, C; Yang, FS
Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 69 88-98; JUL 2018
Abstract: Automatic updating of land use/cover change (LUCC) databases using high spatial resolution images (HSRI) is important for environmental monitoring and policy making, especially for coastal areas that connect the land and coast and that tend to change frequently. Many object-based change detection methods are proposed, especially those combining historical LUCC with HSRI. However, the scale parameter(s) segmenting the serial temporal images, which directly determines the average object size, is hard to choose without experts’ intervention. And the samples transferred from historical LUCC also need experts’ intervention to avoid insufficient or wrong samples. With respect to the scale parameter(s) choosing, a Scale Self-Adapting Segmentation (SSAS) approach based on the exponential sampling of a scale parameter and location of the local maximum of a weighted local variance was proposed to determine the scale selection problem when segmenting images constrained by LUCC for detecting changes. With respect to the samples transferring, Knowledge Transfer (KT), a classifier trained on historical images with LUCC and applied in the classification of updated images, was also proposed. Comparison experiments were conducted in a coastal area of Zhujiang, China, using SPOT 5 images acquired in 2005 and 2010. The results reveal that (1) SSAS can segment images more effectively without intervention of experts. (2) KT can also reach the maximum accuracy of samples transfer without experts’ intervention. Strategy SSAS + KT would be a good choice if the temporal historical image and LUCC match, and the historical image and updated image are obtained from the same resource.
Towards machine ecoregionalization of Earth’s landmass using pattern segmentation method
Authors: Nowosad, J; Stepinski, TF
Source: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 69 110-118; JUL 2018
Abstract: We present and evaluate a quantitative method for delineation of ecophysiographic regions throughout the entire terrestrial landmass. The method uses the new pattern-based segmentation technique which attempts to emulate the qualitative, weight-of-evidence approach to a delineation of ecoregions in a computer code. An ecophysiographic region is characterized by homogeneous physiography defined by the cohesiveness of patterns of four variables: land cover, soils, landforms, and climatic patterns. Homogeneous physiography is a necessary but not sufficient condition for a region to be an ecoregion, thus machine delineation of ecophysiographic regions is the first, important step toward global ecoregionalization. In this paper, we focus on the first-order approximation of the proposed method - delineation on the basis of the patterns of the land cover alone. We justify this approximation by the existence of significant spatial associations between various physiographic variables. Resulting ecophysiographic regionalization (ECOR) is shown to be more physiographically homogeneous than existing global ecoregionalizations (Terrestrial Ecoregions of the World (TEW) and Bailey’s Ecoregions of the Continents (BEC)). The presented quantitative method has an advantage of being transparent and objective. It can be verified, easily updated, modified and customized for specific applications. Each region in ECOR contains detailed, SQL-searchable information about physiographic patterns within it. It also has a computer-generated label. To give a sense of how ECOR compares to TEW and, in the U.S., to EPA Level III ecoregions, we contrast these different delineations using two specific sites as examples. We conclude that ECOR yields regionalization somewhat similar to EPA level M ecoregions, but for the entire world, and by automatic means.
Laser-based spectroscopic methods to evaluate the humification degree of soil organic matter in whole soils: a review
Authors: Senesi, GS; Martin-Neto, L; Villas-Boas, PR; Nicolodelli, G; Milori, DMBP
Source: JOURNAL OF SOILS AND SEDIMENTS, 18 (4):1292-1302; APR 2018
Abstract: The objective of this review is to survey critically the results obtained by the application of laser-induced fluorescence spectroscopy (LIFS) and laser-induced breakdown spectroscopy (LIBS) to the evaluation of the humification degree (HD) of soil organic matter (SOM) directly in untreated, intact whole soils.A large number of soils of various origin and nature, either native or under various cultivations, land use, and management, at various depths, have been studied to evaluate the HD of their SOM directly in intact whole samples. The LIFS spectra were obtained by either a bench or a portable argon laser apparatus that emits UV-VIS light of high power, whereas the LIBS spectra were obtained using a Q-switched Nd:YAG laser at 1064 nm.The close correlations found by comparing H-LIF values of whole soil samples with values of earlier proposed humification indexes confirmed the applicability of LIFS to assess the HD of SOM in whole soils. The high correlation found between HDLIBS values and H-LIF values showed the promising potential of LIBS for the evaluation HD of SOM.The LIFS technique shows to be a valuable alternative to evaluate the HD of SOM by probing directly the whole solid soil sample, thus avoiding the use of any previous chemical and/or physical treatments or separation procedures of SOM from the mineral soil matrix. The emerging application of LIBS to evaluate the HD of SOM in whole soils appears promising and appealing due to its sensitivity, selectivity, accuracy, and precision.
Better models are more effectively connected models
Authors: Nunes, JP; Wainwright, J; Bielders, CL; Darboux, F; Fiener, P; Finger, D; Turnbull, L
Source: EARTH SURFACE PROCESSES AND LANDFORMS, 43 (6):1355-1360; MAY 2018
Abstract: Water- and sediment-transfer models are commonly used to explain or predict patterns in the landscape at scales different from those at which observations are available. These patterns are often the result of emergent properties that occur because processes of water and sediment transfer are connected in different ways. Recent advances in geomorphology suggest that it is important to consider, at a specific spatio-temporal scale, the structural connectivity of system properties that control processes, and the functional connectivity resulting from the way those processes operate and evolve through time. We argue that a more careful consideration of how structural and functional connectivity are represented in models should lead to more robust models that are appropriate for the scale of application and provide results that can be upscaled. This approach is necessary because, notwithstanding the significant advances in computer power in recent years, many geomorphic models are still unable to represent the landscape in sufficient detail to allow all connectivity to emerge. It is important to go beyond the simple representation of structural connectivity elements and allow the dynamics of processes to be represented, for example by using a connectivity function. This commentary aims to show how a better representation of connectivity in models can be achieved, by considering the sorts of landscape features present, and whether these features can be represented explicitly in the model spatial structure, or must be represented implicitly at the subgrid scale.
Multi-scale relief model (MSRM): a new algorithm for the visualization of subtle topographic change of variable size in digital elevation models
Authors: Orengo, HA; Petrie, CA
Source: EARTH SURFACE PROCESSES AND LANDFORMS, 43 (6):1361-1369; MAY 2018
Deep learning in agriculture: A survey
Authors: Kamilaris, A; Prenafeta-Boldu, FX
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 147 70-90; APR 2018
Abstract: Deep learning constitutes a recent, modem technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series
Authors: Yin, H; Prishchepov, AV; Kuemmerle, T; Bleyhl, B; Buchner, J; Radeloff, VC
Source: REMOTE SENSING OF ENVIRONMENT, 210 12-24; JUN 1 2018
Abstract: Agricultural land abandonment is a common land-use change, making the accurate mapping of both location and timing when agricultural land abandonment occurred important to understand its environmental and social outcomes. However, it is challenging to distinguish agricultural abandonment from transitional classes such as fallow land at high spatial resolutions due to the complexity of change process. To date, no robust approach exists to detect when agricultural land abandonment occurred based on 30-m Landsat images. Our goal here was to develop a new approach to detect the extent and the exact timing of agricultural land abandonment using spatial and temporal segments derived from Landsat time series. We tested our approach for one Landsat footprint in the Caucasus, covering parts of Russia and Georgia, where agricultural land abandonment is widespread. First, we generated agricultural land image objects from multi-date Landsat imagery using a multi resolution segmentation approach. Second, we estimated the probability for each object that agricultural land was used each year based on Landsat temporal-spectral metrics and a random forest model. Third, we applied temporal segmentation of the resulting agricultural land probability time series to identify change classes and detect when abandonment occurred. We found that our approach was able to accurately separate agricultural abandonment from active agricultural lands, fallow land, and re-cultivation. Our spatial and temporal segmentation approach captured the changes at the object level well (overall mapping accuracy = 97 +/- 1%), and performed substantially better than pixel-level change detection (overall accuracy = 82 +/- 3%). We found strong spatial and temporal variations in agricultural land abandonment rates in our study area, likely a consequence of regional wars after the collapse of the Soviet Union. In summary, the combination of spatial and temporal segmentation approaches of time-series is a robust method to track agricultural land abandonment and may be relevant for other land-use changes as well.
How far are we from the use of satellite rainfall products in landslide forecasting?
Authors: Brunetti, MT; Melillo, M; Peruccacci, S; Ciabatta, L; Brocca, L
Source: REMOTE SENSING OF ENVIRONMENT, 210 65-75; JUN 1 2018
Abstract: Satellite rainfall products have been available for many years (since ‘90) with an increasing spatial/temporal resolution and accuracy. Their global scale coverage and near real-time products perfectly fit the need of an early warning landslide system. Notwithstanding these characteristics, the number of studies employing satellite rainfall estimates for predicting landslide events is quite limited.In this study, we propose a procedure that allows us to evaluate the capability of different rainfall products to forecast the spatial -temporal occurrence of rainfall -induced landslides using rainfall thresholds. Specifically, the assessment is carried out in terms of skill scores, and receiver operating characteristic (ROC) analysis. The procedure is applied to ground observations and four different satellite rainfall estimates: 1) the Tropical Rainfall Measurement Mission Multi -satellite Precipitation Analysis, TMPA, real time product (3642-RT), 2) the SM2RASC product obtained from the application of SM2RAIN algorithm to the Advanced SCATterometer (ASCAT) derived satellite soil moisture (SM) data, 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), and 4) the Climate Prediction Center (CPC) Morphing Technique (CMORPH). As case study, we consider the Italian territory for which a catalogue listing 1414 rainfall induced landslides in the period 2008-2014 is available.Results show that satellite products underestimate rainfall with respect to ground observations. However, by adjusting the rainfall thresholds, satellite products are able to identify landslide occurrence, even though with less accuracy than ground-based rainfall observations. Among the four satellite rainfall products, CMORPH and SM2RASC are performing the best, even though differences are small. This result is to be attributed to the high spatial/temporal resolution of CMORPH, and the good accuracy of SM2RSC. Overall, we believe that satellite rainfall estimates might be an important additional data source for developing continental or global landslide warning systems.
Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa
Authors: Leenaars, JGB; Claessens, L; Heuvelink, GBM; Hengla, T; Gonzalez, MR; van Bussel, LGJ; Guilpart, N; Yang, HS; Cassman, KG
Source: GEODERMA, 324 18-36; AUG 15 2018
Abstract: In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled georeferenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km(2). The mean RZ-PAWHC for SSA is 74 mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km(3)) due to soil conditions restricting root zone depth. Of these, 4800 km(3) are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km2), and 2500 km(3) due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km(3), aluminium toxicity by 600 km(3), porosity by 120 km(3) and alkalinity by 20 km(3). The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available.
Accounting for non-stationary variance in geostatistical mapping of soil properties
Authors: Wadoux, AMJC; Brus, DJ; Heuvelink, GBM
Source: GEODERMA, 324 138-147; AUG 15 2018
Abstract: Simple and ordinary kriging assume a constant mean and variance of the soil variable of interest. This assumption is often implausible because the mean and/or variance are linked to terrain attributes, parent material or other soil forming factors. In kriging with external drift (KED) non-stationarity in the mean is accounted for by modelling it as a linear combination of covariates. In this study, we applied an extension of KED that also accounts for non-stationary variance. Similar to the mean, the variance is modelled as a linear combination of covariates. The set of covariates for the mean may differ from the set for the variance. The best combinations of covariates for the mean and variance are selected using Akaike’s information criterion. Model parameters of the selected model are then estimated by differential evolution using the Restricted Maximum Likelihood (REML) in the objective function. The methodology was tested in a small area of the Hunter Valley, NSW Australia, where samples from a fine grid with gamma K measurements were treated as measurements of the variable of interest. Terrain attributes were used as covariates. Both a non-stationary variance and a stationary variance model were calibrated. The mean squared prediction errors of the two models were somewhat comparable. However, the uncertainty about the predictions was much better quantified by the non-stationary variance model, as indicated by the mean and median of the standardized squared prediction error and by accuracy plots. We conclude that the non -stationary variance model is more flexible and better suited for uncertainty quantification of a mapped soil property. However, parameter estimation of the non -stationary variance model requires more attention due to possible singularity of the covariance matrix.
Global environmental costs of China’s thirst for milk
Authors: Bai, ZH; Lee, MRF; Ma, L; Ledgard, S; Oenema, O; Velthof, GL; Ma, WQ; Guo, MC; Zhao, ZQ; Wei, S; Li, SL; Liu, X; Havlik, P; Luo, JF; Hu, CS; Zhang, FS
Source: GLOBAL CHANGE BIOLOGY, 24 (5):2198-2211; MAY 2018
Abstract: China has an ever-increasing thirst for milk, with a predicted 3.2-fold increase in demand by 2050 compared to the production level in 2010. What are the environmental implications of meeting this demand, and what is the preferred pathway? We addressed these questions by using a nexus approach, to examine the interdependencies of increasing milk consumption in China by 2050 and its global impacts, under different scenarios of domestic milk production and importation. Meeting China’s milk demand in a business as usual scenario will increase global dairy-related (China and the leading milk exporting regions) greenhouse gas (GHG) emissions by 35% (from 565 to 764Tg CO2eq) and land use for dairy feed production by 32% (from 84 to 111 million ha) compared to 2010, while reactive nitrogen losses from the dairy sector will increase by 48% (from 3.6 to 5.4Tg nitrogen). Producing all additional milk in China with current technology will greatly increase animal feed import; from 1.9 to 8.5Tg for concentrates and from 1.0 to 6.2Tg for forage (alfalfa). In addition, it will increase domestic dairy related GHG emissions by 2.2 times compared to 2010 levels. Importing the extra milk will transfer the environmental burden from China to milk exporting countries; current dairy exporting countries may be unable to produce all additional milk due to physical limitations or environmental preferences/legislation. For example, the farmland area for cattle-feed production in New Zealand would have to increase by more than 57% (1.3 million ha) and that in Europe by more than 39% (15 million ha), while GHG emissions and nitrogen losses would increase roughly proportionally with the increase of farmland in both regions. We propose that a more sustainable dairy future will rely on high milk demanding regions (such as China) improving their domestic milk and feed production efficiencies up to the level of leading milk producing countries. This will decrease the global dairy related GHG emissions and land use by 12% (90Tg CO2eq reduction) and 30% (34 million ha land reduction) compared to the business as usual scenario, respectively. However, this still represents an increase in total GHG emissions of 19% whereas land use will decrease by 8% when compared with 2010 levels, respectively.
Exploring the case for a national-scale soil conservation and soil condition framework for evaluating and reporting on environmental and land use policies
Authors: Humphries, RN; Brazier, RE
Source: SOIL USE AND MANAGEMENT, 34 (1):134-146; MAR 2018
Abstract: It has long been realized that the conservation of soil capital and ecosystem services are of paramount importance, resulting in a growing case for a change in attitude and policymaking in respect of soils. Current UK and EU approaches are risk-based and focused on measures to manage and remediate the adverse impact of current policies and practices directed at maximizing productivity and profit, rather than one of resource conservation. Increasing soil loss and degradation is evidence that current policy is not working and a new approach is needed. In the UK there is governmental ambition to progress towards natural capital-led land use policies but, in the absence of a framework to determine the relative condition of the soil resource, the delivery of sustainable soil conservation policies will continue to be inhibited. Common Standards Monitoring (CSM) is an established monitoring and management framework (based on ecosystem structure, function and process) and has been effectively deployed for almost two decades by the UK Government for the monitoring and reporting of key biological and earth science natural capital and ecosystem services from field’ to local, regional and national levels to the European Commission. It is argued that a CSM for soils could be developed for the UK’s soil resources as well as for those elsewhere, and would be able to deliver a conservation rather than the current risk-based approach. It is capable of accommodating the complexities and variation in soil types and functions and potentially being practical and cost-effective in its implementation.