Digital soil mapping to enhance climate change mitigation and adaptation in the Lower Fraser Valley using remote sensing

Globally, the agriculture sector is constantly being challenged by multiple climate change-induced stresses while agricultural activities are responsible for a large portion of global greenhouse gas emissions. At the same time, agroecosystems have a sizable potential to mitigate climate change through the sequestration of atmospheric carbon-dioxide as soil organic carbon (SOC); a key soil quality parameter that can also enhance climate change adaptation. Although the dual benefits of SOC are well established, intensive agricultural production and associated land use/land cover (LULC) changes continue to drive large declines in SOC. Alternatively, sustainable LULC practices can potentially reverse this trend and improve SOC stocks. Digital soil mapping (DSM) using remote sensing can help elucidate SOC dynamics associated with LULC change and agricultural management practices by producing spatially explicit information on SOC at the field- and landscape-scales. In this research, I developed and applied innovative DSM techniques to study the spatiotemporal changes in SOC and related soil properties in the Lower Fraser Valley (LFV), one of the most intensive agriculture regions of British Columbia, Canada. At the field-scale, I evaluated various sampling strategies for DSM using unmanned aerial vehicle imagery, mid-infrared spectroscopy and geostatistical models to identify the most cost-effective approach. At the landscape-scale, using Landsat satellite imagery and machine learning tools, I produced maps of soil workability thresholds (WT) for the agricultural lands in Delta and then, assessed the SOC dynamics across the entire LFV since 1984. My analysis identified that 40% of Delta’s agricultural lands had a WT of <30%, making them extremely vulnerable to the shifting precipitation patterns expected for the region. In addition, 61% of LFV lost SOC, 12% of the region gained SOC, while 27% remained unchanged between 1984 and 2018. Areas that lost the most SOC were those that had experienced changes in LULC; however, I concluded the majority of SOC loss occurred due to agricultural practices. The dissertation contributes to devising cost-effective approaches to quantify and monitor changes in SOC at the field- and landscape-scales that can help in the development of effective agricultural climate change mitigation and adaptation strategies.Land and Food Systems, Faculty ofGraduat

Assessing land use–land cover change and soil erosion potential using a combined approach through remote sensing, RUSLE and random forest algorithm

Land use–land cover (LULC) change and the associated risk of soil erosion have become a global environmental concern. We herein presented a geospatial analysis to detect LULC changes (1984–2010) in a Canadian watershed by using object-based classification of Landsat satellite images. We found that the watershed experienced a substantial increase in forest clear cutting and built-up areas. The detected LULC changes were implemented into the Revised Universal Soil Loss Equation (RUSLE) to examine the soil erosion potential. We divided the soil erosion risk into five classes ranging from very low (<6 ton ha−1 year−1) to severe (33 ton ha−1 year−1) levels. The random forest algorithm was then implemented and detected that the topography and LULC conditions of 1999 and 2010 had the most influence on the erosion in 2010. The findings of this study will support efficient LULC management to reduce soil erosion and the consequent degradation of water quality

Virtual landscape-scale restoration of altered channels helps us understand the extent of impacts to guide future ecosystem management

Human modification of hydrological connectivity of landscapes has had significant consequences on ecosystem functioning. Artificial drainage practices have fundamentally altered northern landscapes, yet these man made channels are rarely considered in ecosystem management. To better understand the effects of drainage ditches, we conducted a landscape-scale analysis across eleven selected study regions in Sweden. We implemented a unique approach by backfilling ditches in the current digital elevation model to recreate the prehistoric landscape, thus quantifying and characterizing the channel networks of prehistoric (natural) and current (drained) landscapes. Our analysis detected that 58% of the prehistoric natural channels had been converted to ditches. Even more striking was that the average channel density increased from 1.33 km km(-2) in the prehistoric landscape to 4.66 km km(-2) in the current landscape, indicating the extent of ditching activities in the northern regions. These results highlight that man-made ditches should be accurately mapped across northern landscapes to enable more informed decisions in ecosystem management

Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes.Water Management in Baltic Forest