Journal Paper Digests 2018 #16
- Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates
- Ecosystem services provided by heavy metal-contaminated soils in China
- A family of (dis)similarity measures based on evenness and its relationship with beta diversity
- Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain
- A refined method for rapidly determining the relationship between canopy NDVI and the pasture evapotranspiration coefficient
- Discrimination of tea varieties using FTIR spectroscopy and allied Gustafson-Kessel clustering
- FORAGE - An online system for generating and delivering property-scale decision support information for grazing land and environmental management
- DeepDendro - A tree rings detector based on a deep convolutional neural network
- Evaluating pedotransfer functions of the Splintex model
- Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China
- Influence of species richness, evenness, and composition on optical diversity: A simulation study
- Australian wheat production expected to decrease by the late 21st century
- Exploring the case for a national-scale soil conservation and soil condition framework for evaluating and reporting on environmental and land use policies
Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates
Authors: Froslev, TG; Kjoller, R; Bruun, HH; Ejrnaes, R; Brunbjerg, AK; Pietroni, C; Hansen, AJ
Source: NATURE COMMUNICATIONS, 8 1188-1188; OCT 30 2017
Abstract: DNA metabarcoding is promising for cost-effective biodiversity monitoring, but reliable diversity estimates are difficult to achieve and validate. Here we present and validate a method, called LULU, for removing erroneous molecular operational taxonomic units (OTUs) from community data derived by high-throughput sequencing of amplified marker genes. LULU identifies errors by combining sequence similarity and co-occurrence patterns. To validate the LULU method, we use a unique data set of high quality survey data of vascular plants paired with plant ITS2 metabarcoding data of DNA extracted from soil from 130 sites in Denmark spanning major environmental gradients. OTU tables are produced with several different OTU definition algorithms and subsequently curated with LULU, and validated against field survey data. LULU curation consistently improves alpha-diversity estimates and other biodiversity metrics, and does not require a sequence reference database; thus, it represents a promising method for reliable biodiversity estimation.
Ecosystem services provided by heavy metal-contaminated soils in China
Authors: Ding, KB; Wu, Q; Wei, H; Yang, WJ; Sere, G; Wang, SZ; Echevarria, G; Tang, YT; Tao, J; Morel, JL; Qiu, RL
Source: JOURNAL OF SOILS AND SEDIMENTS, 18 (2):380-390; FEB 2018
Abstract: Soils provide a variety of ecosystem services (ESs) that are crucial to food security, water security, energy security, climate change abatement, and biodiversity, especially in densely populated countries such as China. At present, China is facing great challenges from serious soil heavy metal (HM) contamination which has damaged soil ESs and soil security. In this paper, we evaluate the ESs that contaminated soils can potentially provide before and after remediation, and we explore the connections between these ESs and the achievement of soil security in China.After an introduction to the concepts of ESs and soil security and a review of the current status of soil HM contamination in China, the ESs that can potentially be provided by HM-contaminated soils are discussed. Finally, we discuss the current remediation status of HM-contaminated soils from the standpoint of optimizing the ability of these soils to provide ESs.The status of the provision of ESs by HM-contaminated soils of croplands, brownfields, and mining wastelands is described in detail. Contaminated cropland soils fail to provide provisioning (e.g., food production), cultural, and regulating services, while the regulating and supporting services of brownfield soils are greatly reduced. The ESs of mining wasteland soils have been severely damaged, resulting in a high potential for contamination of surrounding ecosystems. Recent soil remediation projects have demonstrated that the damaged ESs of HM-contaminated soils can be restored, which would enhance Chinese soil security. However, it has often been the case that only visible ESs (e.g., food production and vegetation cover) are addressed, while other less noticeable but important services (e.g., water quality and biodiversity) are neglected. Therefore, we propose a framework for the evaluation of ESs provided by HM-contaminated soils.The ESs that could potentially be provided by HM-contaminated soils would help to achieve soil security in China, not only by improving food security, water security, and energy security but also by helping to protect soil biodiversity and abate global climate change. The ESs provided by HM-contaminated soils should be taken into account in soil policy and management systems as well as by the remediation industry.
A family of (dis)similarity measures based on evenness and its relationship with beta diversity
Authors: Ricotta, C
Source: ECOLOGICAL COMPLEXITY, 34 69-73; MAY 2018
Abstract: In this paper, I propose a new evenness-based method for calculating plot-to-plot (dis)similarity coefficients. The method is very flexible, as (dis)similarity can be calculated for any kind of species abundance data (also including functional or phylogenetic differences between species), and can be easily generalized to multiple sites. To show how the proposed method works in practice, the behavior of two similarity coefficients based on Pielou’s and Williams’ evenness is examined with simulated data representing an ideal ecological gradient. Being derived from classical evenness indices, which have been used in ecology for decades, this new family of measures has a great potential for future research in community ecology and multivariate analysis.
Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain
Authors: Gruber, A; Crow, WT; Dorigo, WA
Source: WATER RESOURCES RESEARCH, 54 (2):1353-1367; FEB 2018
Abstract: Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.
A refined method for rapidly determining the relationship between canopy NDVI and the pasture evapotranspiration coefficient
Authors: Alam, MS; Lamb, DW; Rahman, MM
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 147 12-17; APR 2018
Abstract: The estimation of actual crop evapotranspiration (ETc) from any given land cover or crop type is important for irrigation water management and agricultural water consumption analysis. The main parameter used for such estimations is the crop coefficient (K-c). Spectral reflectance indices, such as the normalized difference vegetation index (NDVI) and the crop coefficient of a specific crop or pasture canopy are important indicators of ‘vigour’, namely the photosynthetic activity and rate of biomass accumulation. Measuring both parameters simultaneously, with a view to understanding how they interact, or for creating optical, surrogate indicators of K-c is very difficult because K-c itself is difficult to measure. In this study a portable enclosed chamber was used to measure ET, of a pasture and subsequently calculated K-c from reference evapotranspiration (ETc) data derived from a nearby automatic weather station (AWS). Calibration of the chamber confirms the suitability of the device to measure the amount of water vapour produced by local plant evapotranspiration, producing a calibration factor (C) close to 1 (C = 1.02, R-2 = 0.87). The coincident NDVI values were measured using a portable active optical sensor. In a test involving a pasture (Festuca arundinacea var. Dovey) at two different stages of growth in two consecutive growing seasons, the NDVI and crop coefficients were observed to be strongly correlated (R-2 = 0.80 and 0.77, respectively). A polynomial regression (R-2 = 0.84) was found to be the best fit for the combined, multi-temporal KC-NDVI relationship. The main advantages of this method include the suitability of operating at a smaller scale (< 1 m(2)), in real time and repeatability.
Discrimination of tea varieties using FTIR spectroscopy and allied Gustafson-Kessel clustering
Authors: Wu, XH; Zhu, J; Wu, B; Sun, J; Dai, CX
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 147 64-69; APR 2018
Abstract: For the purpose of classifying tea varieties, allied Gustafson-Kessel (AGK) clustering was proposed to cluster the Fourier transform infrared reflectance (FTIR) spectra of tea samples. As a fuzzy clustering algorithm, AGK can not only produce fuzzy membership and typicality values but also cluster various shapes of data with the help of Gustafson-Kessel (GK) clustering. After FTIR spectra were collected by FTIR-7600 infrared spectrometer, they were preprocessed with multiple scatter correction (MSC). To reduce the dimensionality of FTIR spectra and make the classification of data easily, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to process the FTIR spectra. After that, fuzzy c-means (FCM) clustering, possibilistic c-means (PCM) clustering, AGK clustering and allied fuzzy c-means (AFCM) clustering were performed to cluster data, respectively. The clustering accuracy of AGK achieved 93.9% which was the highest one than other fuzzy clustering algorithms. The results obtained in experiments showed that AGK coupled with FTIR spectroscopy could provide an effective discrimination model for classification of tea varieties successfully.
FORAGE - An online system for generating and delivering property-scale decision support information for grazing land and environmental management
Authors: Zhang, BS; Carter, J
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 150 302-311; JUL 2018
Abstract: Queensland grazing industries operate in one of the world’s most variable climates and face enormous challenges to achieve profitability and sustainability. To assist grazing enterprises, the Queensland Government has developed an operational online information system - FORAGE, to facilitate best management practice for grazing land. The FORAGE system provides land managers with property-scale information relating to rainfall, ground cover, soil erodibility, land types, tree density, seasonal climate outlooks and pasture growth simulated using the GRASP grazing system model. This information is site-specific and tailored to facilitate on-ground management decisions common to Queensland grazing enterprises. The FORAGE system makes complex remotely-sensed data, information from a range of databases and pasture growth models more accessible and relevant to land managers. This paper describes the structure, resources (e.g. software, databases and models), information generating processes, delivery mechanisms and information products of the FORAGE system.
DeepDendro - A tree rings detector based on a deep convolutional neural network
Authors: Fabijanska, A; Danek, M
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 150 353-363; JUL 2018
Abstract: Tree-ring analysis is widely used in many different fields of science. Among others, information carried by annual tree rings allows determining the rates of environmental changes and the timing of events. The analysis of the tree rings requires prior detection of tree-ring boundaries which is traditionally performed manually with the use of stereoscope, a moving table, and a data recorder. This is, however, time-consuming and very cumbersome, especially in the case of long tree-ring series. Several approaches to an automatic detection of tree-ring boundaries exist; however, they use basic image processing techniques. As a result, their accuracy is limited, and their application is restricted mainly to conifer wood where the tree-ring boundaries are clearly defined. There also exists some commercial software, however, none of them is perfect as they fail when applied to the ring-porous wood type. Therefore this paper proposes a DeepDendro approach i.e., an automatic tree-ring boundary detector built upon the U-Net convolutional network. To the authors’ best knowledge this is the first study which applies ConvNets for an automatic detection of tree rings. The performance of the existing approach was tested on the dataset of images of wood cores of three species that represent the ring-porous type of the anatomical structure (Quercus sp., Fraxinus excelsior L., and Ulmus sp.). The testing dataset contained over 2500 of tree-ring boundaries, 96% of which were determined correctly by the proposed method. The corresponding precision is at the level of 0.97 which confirms that only a few false boundaries were introduced by the DeepDendro approach. The results were obtained automatically without any user interaction.
Evaluating pedotransfer functions of the Splintex model
Authors: Dos Reis, AMH; Armindo, RA; Duraes, MF; Van Lier, QD
Source: EUROPEAN JOURNAL OF SOIL SCIENCE, 69 (4):685-697; JUL 2018
Abstract: The soil water retention curve (SWRC) is an essential hydraulic function for the understanding and modelling of soil hydraulic processes. Its direct determination is time consuming and sometimes expensive because it requires extensive sampling, especially when spatial and temporal variation of soil hydraulic properties are an issue. The use of pedotransfer functions (PTFs) is a viable alternative to predict the parameters of SWRC from more easily determined soil attributes. Splintex is a physically based engineering model containing two PTFs to estimate SWRC parameters and saturated hydraulic conductivity, but its performance and functionality have not yet been fully evaluated and disseminated for soil science. We examined the functionality of the PTFs available in Splintex to estimate parameters of the Van Genuchten-Mualem (VGM) SWRC equation with a laboratory-measured dataset containing information on particle size, bulk and particle density, saturated water content and parameters of the SWRCs. In addition, we tested the performance of both PTFs for deriving some soil physical-structural variables calculated from estimated SWRC parameters. Compared with VGM parameters fitted to the laboratory dataset, the model performed well. Its predictive performance for soil air capacity, relative field capacity and soil physical-structural quality indices was also evaluated. The performance of Splintex for estimating the VGM parameters depended on the combined effect of all input variables rather than on isolated correlations between an input variable and a model parameter.
Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China
Authors: Zhao, YJ; Zeng, Y; Zheng, ZJ; Dong, WX; Zhao, D; Wu, BF; Zhao, QJ
Source: REMOTE SENSING OF ENVIRONMENT, 213 104-114; AUG 2018
Abstract: Monitoring biodiversity is essential for the conservation and management of forest resources. A method called “spectranomics” that maps the diversity of forest species based on species-driven leaf optical traits using imaging spectroscopy has been developed for tropical forests in earlier studies. In this study we applied the “spectranomics” method in combination with airborne hyperspectral (PHI-3 sensor with 1 m spatial resolution) and LiDAR ( > 4 points/m(2)) data to first identify interspecies variations in biochemical and structural properties of trees and then estimate the tree species diversity within the Shennongjia Forest Nature Reserve in China. Firstly, we used the watershed algorithm based on morphological crown control to isolate individual tree crowns (ITCs) from the LiDAR data. For each ITC, we then calculated seven vegetation indices (VIs) representing key biochemical properties from the hyperspectral data and additionally derived the LiDAR-based tree height which was identified to support the discrimination of the tree species in a preceding analysis. Finally we utilized the combination of the seven selected VIs and tree height as input to a self-adaptive Fuzzy C-Means (FCM) clustering algorithm. The FCM algorithm was applied to fixed subsets of 30 m x 30 m and it was assumed that the number of clusters identified within a subset represents the number of occurring species. The species richness and Shannon-Wiener diversity index calculated from the clustering outputs correlated well with the field reference data (R-2 = 0.83, RMSE = 0.25). The results show that forest species diversity can be directly predicted using the suggested clustering method based on crown-by-crown variations in biochemical and structural properties in the examined subtropical forest without the need to distinguish the individual tree species.
Influence of species richness, evenness, and composition on optical diversity: A simulation study
Authors: Wang, R; Gamon, JA; Schweiger, AK; Cavender-Bares, J; Townsend, PA; Zygielbaum, AI; Kothari, S
Source: REMOTE SENSING OF ENVIRONMENT, 211 218-228; JUN 15 2018
Abstract: While remote sensing has increasingly been applied to estimate a biodiversity directly through optical diversity, there is a need to better understand the mechanisms behind the optical diversity-biodiversity relationship. Here, we examined the relative contributions of species richness, evenness, and composition to the spectral reflectance, and consider factors confounding the remote estimation of species diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota. We collected hyperspectral reflectance of 16 prairie species using a tram-mounted imaging spectrometer, and a full-range field spectrometer with a leaf clip, and simulated plot-level images from both instruments with different species richness, evenness and composition. Two optical diversity metrics were explored: the coefficient of variation (CV) of spectral reflectance in space and classified species derived from Partial Least Squares Discriminant Analysis (PLS-DA), a spectral classification method. Both optical diversity metrics (CV and PLS-DA classified species) were affected by species richness and evenness. Diversity metrics that combined species richness and evenness together (e.g. Shannon’s index) were more strongly correlated with optical diversity than either metric alone. Image-derived data were influenced by both leaf traits and canopy structure and showed larger spectral variability than leaf clip data, indicating that sampling methods influence optical diversity. Leaf and canopy traits both contributed to optical diversity, sometimes in complex or contradictory ways. Large within-species variation sometimes confounded biodiversity estimation from optical diversity, and a single species markedly altered the optical-biodiversity relationship. Biodiversity estimation from CV was strongly influenced by soil background, while estimation from PLS-DA classified species was not sensitive to soil background. These findings are consistent with recent empirical studies and demonstrate that modeling approaches can be used to explore effects of spatial scale and guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.
Australian wheat production expected to decrease by the late 21st century
Authors: Wang, B; Liu, D; O’Leary, GJ; Asseng, S; Macadam, I; Lines-Kelly, R; Yang, XH; Clark, A; Crean, J; Sides, T; Xing, HT; Mi, CR; Yu, Q
Source: GLOBAL CHANGE BIOLOGY, 24 (6):2403-2415; JUN 2018
Abstract: Climate change threatens global wheat production and food security, including the wheat industry in Australia. Many studies have examined the impacts of changes in local climate on wheat yield per hectare, but there has been no assessment of changes in land area available for production due to changing climate. It is also unclear how total wheat production would change under future climate when autonomous adaptation options are adopted. We applied species distribution models to investigate future changes in areas climatically suitable for growing wheat in Australia. A crop model was used to assess wheat yield per hectare in these areas. Our results show that there is an overall tendency for a decrease in the areas suitable for growing wheat and a decline in the yield of the northeast Australian wheat belt. This results in reduced national wheat production although future climate change may benefit South Australia and Victoria. These projected outcomes infer that similar wheat-growing regions of the globe might also experience decreases in wheat production. Some cropping adaptation measures increase wheat yield per hectare and provide significant mitigation of the negative effects of climate change on national wheat production by 2041-2060. However, any positive effects will be insufficient to prevent a likely decline in production under a high CO2 emission scenario by 2081-2100 due to increasing losses in suitable wheat-growing areas. Therefore, additional adaptation strategies along with investment in wheat production are needed to maintain Australian agricultural production and enhance global food security. This scenario analysis provides a foundation towards understanding changes in Australia’s wheat cropping systems, which will assist in developing adaptation strategies to mitigate climate change impacts on global wheat production.
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.