Brandon Heung

• PhD (Simon Fraser University)
• Professor (Associate) at Dalhousie University

Machine-learning is the automated process of uncovering patterns in large datasets using computer-based statistical models, where a fitted model may then be used for prediction purposes on new data. Despite the growing number of machine-learning algorithms that have been developed, relatively few studies have provided a comparison of an array of di...

The need for improved soil inventory information in the province of British Columbia (BC), Canada, was addressed using a random forest (RF) classifier that was informed using legacy soil data. RF models were prepared for 110 ecodistrict subdivisions of BC, and predictions were subsequently assembled into a final soil parent material map mosaic cove...

Machine-learners used for digital soil mapping are generally trained using either data derived from field-observed soil pits or from soil survey polygons-although no direct comparison of the accuracy resulting from the two methods has yet to be undertaken. This study examined such a comparison over the Okanagan Valley and Kamloops region of British...

Digital soil mapping (DSM) techniques have provided soil information that has revolutionized soil management across multiple spatial extents and scales. DSM practitioners have been increasingly reliant on machine-learning (ML) techniques; yet, methods to generate uncertainty maps from ML predictions are limited. To address this issue, this study in...

(1) Background: Research and development in remote sensing have been used to determine and monitor crop phenology. This approach assesses the internal and external changes of the plant. Therefore, the objective of this study was to determine the potential of using a multispectral sensor to predict phenology in wild blueberry fields. (2) Method: A U...

Clay minerals are essential components of the soil colloids accounting for a significant proportion of the soil matrix and are responsible for altering the physical and chemical properties of soils. Our study aimed to investigate the distribution of clay minerals in soils developed from basement complex (BC), sandstone (SS), mudstone (MS), shale st...

Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity,...

As organic waste is converted to usable amendments via composting, there are large CO2 emissions associated with the decomposition of organic matter via microorganisms. While the active composting phase produces the largest emissions over a short duration, compost can often be stored during and after the maturation phase for much longer periods of...

Terrestrial plant and soil organic carbon stocks are critical for regulating climate change, enhancing soil fertility, and supporting biodiversity. While a global-scale decoupling between plant and soil organic carbon has been documented, the hotspots and interconnections between these two carbon compartments across Africa, the second-largest conti...

The historical conversion of forests to rainfed agricultural lands in the semi-arid forest ecosystems is one of the primary sources of human-induced, greenhouse gas emission and causes of soil organic carbon (SOC) loss. This study aims to predict SOC contents as an extremely crucial factor in soil formation and fertility in the topsoil of tradition...

Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to better understand land utilization on these lands vulnerable to rising sea levels. Current field boundaries del...

Soil serves as a reservoir for organic carbon stock, which indicates soil quality and fertility within the terrestrial ecosystem. Therefore, it is crucial to comprehend the spatial distribution of soil organic carbon stock (SOCS) and the factors influencing it to achieve sustainable practices and ensure soil health. Thus, the present study aimed to...

Introduction The increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is highly variable spatially and is associated with degr...

Soil properties can be influenced by slope location due to erosion, deposition, and mass movement. There are conflicting findings regarding the impact of the slope position on soil properties and elemental concentrations. This study aimed to examine the influence of slope position on the variability of soil properties and elemental concentrations a...

Investigating the correlation between environmental variables and species distribution should be performed using data acquired from appropriate spatial scales to meet adaptive management requirements in a changing environment. This study aimed to model the influence of climate change on the spatial distribution of Br’nt's oak (Quercus brantii Lindl...

Field imagery is an effective way to capture the state of the entire field; yet, current field inspection approaches, when accounting for image resolution and processing speed, using existent imaging systems, do not always enable real-time field inspection. This project involves the innovation of novel technologies by using an FPGA-based image proc...

Wildfire has significant impact on plant phenology. The plants’ phenological variables, derived from time series satellite data, can be monitored and the changes in satellite imagery may be used to identify the beginning, peak, and end of the growing season. This study investigated the use of remote sensing data and land surface phenology (LSP) par...

DSMART has been widely used to disaggregate multi-component soil polygon map units into raster maps. The algorithm randomly selects (simple random sampling approach) an equal number of synthetic sample points from all map units and assigns soil classes proportionate to the map unit composition using the C5.0 algorithm. Conditional Latin Hypercube s...

Large organic deposits in the southwestern plain of Montreal have been converted to agricultural land for vegetable production. In addition to the variable depth of the organic deposits, these soils commonly have an impermeable coprogenous layer between the peat and the underlying mineral substratum. Estimations of the depth and thickness of these...

Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality...

Despite the requirement for data to be normally distributed with variance being independent of the mean, some studies of plastic litter, including COVID-19 face masks, have not tested for these assumptions before embarking on analyses using parametric statistics. Investigation of new data and secondary analyses of published literature data indicate...

Prioritizing new areas for conservation in the Hyrcanian mountain forests is important because future climate change is an immediate threat to endangered species in these areas. Taxus baccata L. (European yew) is one of the most important coniferous species of the Hyrcanian forests that is endangered today for various reasons; therefore, the conser...

Spatial information on land and soil resources are critical towards addressing land degradation for ensuring sustainable soil and crop management. To address these needs, digital soil mapping techniques have emerged as an efficient and low-cost solution. Although digital soil mapping has typically leveraged geospatial environmental variables (e.g.,...

Monitoring the changes in soil organic carbon (SOC) pools is critical for sustainable soil and agricultural management. This case study models total and active organic carbon dynamics (2015/2016 to 2019/2020) using digital soil mapping (DSM) techniques. Model predictors include topographic variables generated from light detection and ranging data;...

Soil quality, defined as the capacity of a soil to function, is one of the most important characteristics of soil. Methods for modelling and monitoring soil quality are needed for sustainable soil management and evaluating soil degradation. In Iran, resource demands have led to the deforestation of the semiarid oak forests. The impacts of these act...

Soil organic carbon (SOC) is an essential property of soil, and understanding its spatial patterns is critical to understanding vegetation management, soil degradation, and environmental issues. This study applies a framework using remote sensing data and digital soil mapping techniques to examine the spatiotemporal dynamics of SOC for the Yazd-Ard...

Digital soil mapping combines soil plot data with environmental datasets to model variation in soil properties across a landscape. The quality of a digital soil map depends on both the quantity and distribution of soil plots within the study extent. Field campaigns to acquire soil data are time intensive and costly to undertake, requiring training...

Information on the spatial distribution of soil pH is essential for assessing soil quality and soil productivity. Digital soil mapping (DSM) is commonly used to predict soil characteristics over various types of landscapes. Over the past decade, researchers have made progress using machine learning techniques to provide reliable predictions of soil...

Although British Columbia (BC), Canada, has a rich history of producing conventional soil maps (CSMs) between 1925 and 2000, the province still lacks a detailed soil map with a comprehensive coverage due to the cost and time required to develop such a product. This study builds on previous digital soil mapping (DSM) research in BC and develops prov...

This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in pred...

Dykelands are agricultural ground protected from coastal inundation by dyke infra-structure and constitute some of the most agriculturally productive lands in Nova Scotia. Between 2015 and 2019, Canada’s Annual Crop Inventory was used to characterize and estimate hectares of agricultural dykelands cultivated in Nova Scotia. The number of hectares o...

Dust pollution is one of the major environmental crises in the arid regions of Iran and there is a need to predict dust pollution and identify its controlling factors to help reduce its adverse effects on the livelihood of residents of these areas. Although deep neural networks (DNN) are powerful tools in the modelling of environmental phenomena, t...

There has been a continued adaption and application of soil health tests across all regions of the globe. However, there are challenges related to the interpretation of the results of soil health tests developed in one region but applied elsewhere. To determine the factors that are the most important for interpreting soil health tests in Nova Scoti...

Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsor...

Digital soil mapping approaches predict soil properties based on the relationships between soil observations and related environmental covariates using techniques such as machine learning (ML) models. In this research, a wide range of ML models (12 base learners) were tested in predicting and mapping soil properties. Furthermore, a super learner ap...

In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeN...

Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models with bio-inspired metaheuristi...

The low potential of agricultural productivity in the majority of central Iran is mainly attributed to high levels of soil salinity. To increase agricultural productivity, while preventing any further salinization, and implement effective soil reclamation programs, precise information about the spatial patterns and magnitude of soil salinity is ess...

Salinization and alkalization are predominant environmental problem world-wide which their accurate assessment is essential for determining appropriate ways to deal with land degradation, for better soil and crop management. In the current research, a combination of random forests and covariate data were used to assess spatial variability of soil s...

Accurate soil information is critically important for forest management planning and operations but is challenging to map. Digital soil mapping (DSM) improves upon the limitations of conventional soil mapping by explicitly linking a variety of environmental data layers to spatial soil point data sets to continuously predict soil variability across...

The study extracts representative features to train a model with supervised machine learning (ML) to detect powdery mildew (Sphaerotheca macularis f. sp. fragariae) on the strawberry leaves. Powdery mildew (PM) is a fungal disease that greatly affects the production of strawberry and usually infects under conditions of warming temperatures and high...

As a landscape changes, so do the flows of matter that run across it. These flows modify the landscape and can thereby alter their own course in a feedback mechanism. This study focuses on one instance of this process: medium-term background soil redistribution induced by sheet erosion. Previous studies that have modelled this phenomenon have eithe...