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Digital Mapping in Dynamic Environments

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 August 2020) | Viewed by 55264

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Special Issue Editors

Prof. Budiman Minasny

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Guest Editor

The University of Sydney
Interests: digital soil mapping; big data analytics; environmental modelling

Dr. Brendan Malone

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Guest Editor

Agriculture and Food Commonwealth Scientific and Industrial Research Organisation, Bruce E Butler Laboratory, Clunies Ross Street, Black Mountain, ACT 2601, Australia
Interests: soil science; digital soil mapping; pedometrics; GIS
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Special Issue Information

Dear Colleagues,

We are glad to host a Special Issue on “Digital mapping in dynamic environments”, seeking for contributions on remote sensing application in digital mapping of the environment.

Anthropogenic activities on the earth system to fulfil increasing demands for food and clean water for the world’s population have accelerated changes in the soil and ecosystem. We need to efficiently map functions of the terrestrial ecosystem so we can manage it strategically. Digital soil and environment mapping has achieved excellent results in the prediction of soil properties at local, regional, continental, and global scales. The convergence of big data, advanced statistical modelling and computing infrastructure have now made large scale digital mapping much more feasible. Field observation data coupled with earth observing remote and proximal sensors provide exciting new opportunities to extract new knowledge. This special issue looks for application of rich remote sensing time series data in combination with statistical models that enable space and time mapping of soil and the environment.

Contributions on mapping application in agriculture, and terrestrial environment, include, but not limited to:

Evaluating climate impacts on the terrestrial ecosystem functioning
Multisource and multitemporal application of remote sensing data for digital mapping
The use of big data analytics for spatiotemporal prediction, including deep learning.
Incorporating process-based models for mapping dynamic soil functions.
Spatial forecasting and/or simulation experiments of environmental resource change.
Data fusion of various proximal and remote sensing products or model ensemble to combine outputs of several models.
Uncertainty analysis of environmental resources.

Prof. Budiman Minasny
Dr. Brendan Malone
Guest Editors

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Keywords

terrestrial ecosystem functioning
digital mapping
environmental modelling
mechanistic modelling, process modelling
spatial data analytics
digital soil mapping
spatiotemporal data analysis
ensemble modelling
deep learning
agricultural soils.

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Published Papers (8 papers)

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Editorial

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5 pages, 176 KiB

Open AccessEditorial

Editorial for Special Issue “Digital Mapping in Dynamic Environments”

by Brendan Malone and Budiman Minasny

Remote Sens. 2020, 12(20), 3384; https://doi.org/10.3390/rs12203384 - 16 Oct 2020

Viewed by 1690

Abstract

It is widely acknowledged that the global stock of soil and environmental resources are diminishing and under threat. This issue stems from current and historical unsustainable management practices, leading to degraded landscapes, which is further compounded by increased pressures upon them from ever-increasing anthropogenic activities. To curb the trajectory toward a collapse of our ecosystems, systematic ways are needed to assess the condition of our natural resources, how much they might have changed, and to what extent this might impact on the life sustaining functions we derive from our environment and the extent of our food producing systems. Some solutions to these issues come in the form of measurement, mapping and monitoring technology, which facilitates powerful ways in which to be informed about and to understand and assess the condition of our landscapes so that they can be managed strategically or simply improved. This Special Issue showcases from several locations across the globe, detailed examples of what is achievable at the convergence of big data brought about by remote and proximal sensing platforms, advanced statistical modelling and computing infrastructure to understand and monitor our ecosystems better. These utilities not only provide high-resolution abilities to map the extent and changes to our food producing systems, they also have yielded new ways to determine land-use and climate effects on the fate of soil carbon across living generations and to identify hydrological risk strategies in otherwise data-poor urban environments. Leveraging the availability of remote sensing data is telling, but the papers in this Special Issue also highlight the sophistication of modelling capabilities to deliver not only highly detailed maps of temporal dynamic soil phenomena but ways to draw new inferences from sparse and disparate model input data. The challenges of restoring our ecosystems are immense and sobering. However, we are well equipped and capable of confronting these pervasive issues in objective and data-informed ways that have previously never been possible. Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

Research

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22 pages, 14181 KiB

Open AccessArticle

National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach

by Pierre Roudier, Olivia R. Burge, Sarah J. Richardson, James K. McCarthy, Gerard J. Grealish and Anne-Gaelle Ausseil

Remote Sens. 2020, 12(18), 2872; https://doi.org/10.3390/rs12182872 - 4 Sep 2020

Cited by 31 | Viewed by 6369

Abstract

Understanding the spatial variation of soil pH is critical for many different stakeholders across different fields of science, because it is a master variable that plays a central role in many soil processes. This study documents the first attempt to map soil pH [...] Read more.

_{2}

O) at high resolution (100 m) in New Zealand. The regression framework used follows the paradigm of digital soil mapping, and a limited number of environmental covariates were selected using variable selection, before calibration of a quantile regression forest model. In order to adapt the outcomes of this work to a wide range of different depth supports, a new approach, which includes depth of sampling as a covariate, is proposed. It relies on data augmentation, a process where virtual observations are drawn from statistical populations constructed using the observed data, based on the top and bottom depth of sampling, and including the uncertainty surrounding the soil pH measurement. A single model can then be calibrated and deployed to estimate pH a various depths. Results showed that the data augmentation routine had a beneficial effect on prediction uncertainties, in particular when reference measurement uncertainties are taken into account. Further testing found that the optimal rate of augmentation for this dataset was 3-fold. Inspection of the final model revealed that the most important variables for predicting soil pH distribution in New Zealand were related to land cover and climate, in particular to soil water balance. The evaluation of this approach on those validation sites set aside before modelling showed very good results (

R^{2} = 0.65

C C C = 0.79

R M S E = 0.54

), that significantly out-performed existing soil pH information for the country. Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

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26 pages, 4768 KiB

Open AccessArticle

Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

by Ruhollah Taghizadeh-Mehrjardi, Karsten Schmidt, Alireza Amirian-Chakan, Tobias Rentschler, Mojtaba Zeraatpisheh, Fereydoon Sarmadian, Roozbeh Valavi, Naser Davatgar, Thorsten Behrens and Thomas Scholten

Remote Sens. 2020, 12(7), 1095; https://doi.org/10.3390/rs12071095 - 29 Mar 2020

Cited by 133 | Viewed by 9920

Abstract

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions. Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

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18 pages, 5944 KiB

Open AccessArticle

Combining Historical Remote Sensing, Digital Soil Mapping and Hydrological Modelling to Produce Solutions for Infrastructure Damage in Cosmo City, South Africa

by George van Zijl, Johan van Tol, Darren Bouwer, Simon Lorentz and Pieter le Roux

Remote Sens. 2020, 12(3), 433; https://doi.org/10.3390/rs12030433 - 29 Jan 2020

Cited by 15 | Viewed by 4499

Abstract

Urbanization and hydrology have an interactive relationship, as urbanization changing the hydrology of a system and the hydrology commonly causing structural damage to the infrastructure. Hydrological modelling has been used to quantify the water causing structural impacts, and to provide solutions to the issues. However, in already-urbanized areas, creating a soil map to use as input in the modelling process is difficult, as observation positions are limited and visuals of the natural vegetation which indicate soil distribution are unnatural. This project used historical satellite images in combination with terrain parameters and digital soil mapping methods to produce an accurate (Kappa statistic = 0.81) hydropedology soil map for the Cosmo City suburb in Johannesburg, South Africa. The map was used as input into the HYDRUS 2D and SWAT hydrological models to quantify the water creating road damage at Kampala Crescent, a road within Cosmo City (using HYDRUS 2D), as well as the impact of urbanization on the hydrology of the area (using SWAT). HYDRUS 2D modelling showed that a subsurface drain installed at Kampala Crescent would need a carrying capacity of 0.3 m³·h⁻¹·m⁻¹ to alleviate the road damage, while SWAT modelling shows that surface runoff in Cosmo City will commence with as little rainfall as 2 mm·month⁻¹. This project showcases the value of multidisciplinary work. The remote sensing was invaluable to the mapping, which informed the hydrological modelling and subsequently provided answers to the engineers, who could then mitigate the hydrology-related issues within Cosmo City. Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

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18 pages, 4483 KiB

Open AccessArticle

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

by Yue Zhou, Jie Xue, Songchao Chen, Yin Zhou, Zongzheng Liang, Nan Wang and Zhou Shi

Remote Sens. 2020, 12(1), 85; https://doi.org/10.3390/rs12010085 - 25 Dec 2019

Cited by 36 | Viewed by 4985

Abstract

Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R² (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land. Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

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27 pages, 14507 KiB

Open AccessEditor’s ChoiceArticle

Automated Near-Real-Time Mapping and Monitoring of Rice Extent, Cropping Patterns, and Growth Stages in Southeast Asia Using Sentinel-1 Time Series on a Google Earth Engine Platform

by Rudiyanto, Budiman Minasny, Ramisah M. Shah, Norhidayah Che Soh, Chusnul Arif and Budi Indra Setiawan

Remote Sens. 2019, 11(14), 1666; https://doi.org/10.3390/rs11141666 - 12 Jul 2019

Cited by 72 | Viewed by 14599

Abstract

More than 50% of the world’s population consumes rice. Accurate and up-to-date information on rice field extent is important to help manage food and water security. Currently, field surveys or MODIS satellite data are used to estimate rice growing areas. This study presents a cost-effective methodology for near-real-time mapping and monitoring of rice growth extent and cropping patterns over a large area. This novel method produces high-resolution monthly maps (10 m resolution) of rice growing areas, as well as rice growth stages. The method integrates temporal Sentinel-1 data and rice phenological parameters with the Google Earth Engine (GEE) cloud-based platform. It uses monthly median time series of Sentinel-1 at VH polarization from September 2016 to October 2018. The two study areas are the northern region of West Java, Indonesia (0.75 million ha), and the Kedah and Perlis states in Malaysia (over 1 million ha). K-means clustering, hierarchical cluster analysis (HCA), and a visual interpretation of VH polarization time series profiles are used to generate rice extent, cropping patterns, and spatiotemporal distribution of growth stages. To automate the process, four supervised classification methods (support vector machine (SVM), artificial neural networks (ANN), random forests, and C5.0 classification models) were independently trialled to identify cluster labels. The results from each classification method were compared. The method can also forecast rice extent for up to two months. The VH polarization data can identify four growth stages of rice—T&P: tillage and planting (30 days); V: vegetative-1 and 2 (60 days); R: reproductive (30 days); M: maturity (30 days). Compared to field survey data, this method measures overall rice extent with an accuracy of 96.5% and a kappa coefficient of 0.92. SVM and ANN show better performance than random forest and C5.0 models. This simple and robust method could be rolled out across Southeast Asia, and could be used as an alternative to time-consuming, expensive field surveys. Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

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20 pages, 4561 KiB

Open AccessArticle

Climate and Land-Use Change Effects on Soil Carbon Stocks over 150 Years in Wisconsin, USA

by Jingyi Huang, Alfred E. Hartemink and Yakun Zhang

Remote Sens. 2019, 11(12), 1504; https://doi.org/10.3390/rs11121504 - 25 Jun 2019

Cited by 33 | Viewed by 7006

Abstract

Soil organic carbon is a sink for mitigating increased atmospheric carbon. The international initiative “4 per 1000” aims at implementing practical actions on increasing soil carbon storage in soils under agriculture. This requires a fundamental understanding of the soil carbon changes across the globe. Several studies have suggested that the global soil organic carbon stocks (SOCS) have decreased due to global warming and land cover change, while others reported SOCS may increase under climate change and improved soil management. To better understand how a changing climate, land cover, and agricultural activities influence SOCS across large extents and long periods, the spatial and temporal variations of SOCS were estimated using a modified space-for-time substitution method over a 150-year period in the state of Wisconsin, USA. We used legacy soil datasets and environmental factors collected and estimated at different times across the state (169,639 km²) coupled with a machine-learning algorithm. The legacy soil datasets were collected from 1980 to 2002 from 550 soil profiles and harmonized to 0.30 m depth. The environmental factors consisted of 100-m soil property maps, 1-km annual temperature and precipitation maps, 250-m remote-sensing (i.e., Landsat)-derived yearly land cover maps and a 30-m digital elevation model. The model performance was moderate but can provide insights on understanding the impacts of different factors on SOCS changes across a large spatial and temporal extent. SOCS at the 0–0.30 m decreased at a rate of 0.1 ton ha⁻¹ year⁻¹ between 1850 and 1938 and increased at 0.2 ton ha⁻¹ year⁻¹ between 1980 and 2002. The spatial variation in SOCS at 0–0.30 m was mainly affected by land cover and soil types with the largest SOCS found in forest and wetland and Spodosols. The loss between 1850 and 1980 was most likely due to land cover change while the increase between 1980 and 2002 was due to best soil management practices (e.g., decreased erosion, reduced tillage, crop rotation and use of legume and cover crops). Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

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Other

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13 pages, 4561 KiB

Open AccessTechnical Note

Macadamia Orchard Planting Year and Area Estimation at a National Scale

by James Brinkhoff and Andrew J. Robson

Remote Sens. 2020, 12(14), 2245; https://doi.org/10.3390/rs12142245 - 13 Jul 2020

Cited by 6 | Viewed by 5078

Abstract

Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold. Full article

(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)

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