Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the increasing availability of satellite data, geospatial analysis tools, and classification methods, it is essential to systematically assess the performance of different combinations of satellite data and classification methods to help select the best approach for LULC classification. Therefore, this study aims to evaluate the LULC classification performance of two commonly used platforms (i.e., ArcGIS Pro and Google Earth Engine) with different satellite datasets (i.e., Landsat, Sentinel, and Planet) through a case study for the city of Charlottetown in Canada. Specifically, three classifiers in ArcGIS Pro, including support vector machine (SVM), maximum likelihood (ML), and random forest/random tree (RF/RT), are utilized to develop LULC maps over the period of 2017–2021. Whereas four classifiers in Google Earth Engine, including SVM, RF/RT, minimum distance (MD), and classification and regression tree (CART), are used to develop LULC maps for the same period. To identify the most efficient and accurate classifier, the overall accuracy and kappa coefficient for each classifier is calculated throughout the study period for all combinations of satellite data, classification platforms, and methods. Change detection is then conducted using the best classifier to quantify the LULC changes over the study period. Results show that the SVM classifier in both ArcGIS Pro and Google Earth Engine presents the best performance compared to other classifiers. In particular, the SVM in ArcGIS Pro shows an overall accuracy of 89% with Landsat, 91% with Sentinel, and 94% with Planet. Similarly, in Google Earth Engine, the SVM shows an accuracy of 87% with Landsat 8 and 92% with Sentinel 2. Furthermore, change detection results show that 13.80% and 14.10% of forest areas have been turned into bare land and urban class, respectively, and 3.90% of the land has been converted into the urban area from 2017 to 2021, suggesting the intensive urbanization. The results of this study will provide the scientific basis for selecting the remote sensing classifier and satellite imagery to develop accurate LULC maps.

Greenhouse gases (GHGs) like nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄) are both emitted and removed by soils. Accurate worldwide allocations of carbon budget are essential for land use planning, global climate change, and climate-related research. Precise measurements, drivers, and mitigation strategies are necessary, given agricultural soil’s significant potential storage and emission capacities. Different agricultural management practices cause greenhouse gas (GHG) emissions into the atmosphere and contribute to anthropogenic emissions. Agricultural soils can generate 70% of the world’s manmade N₂O emissions and also behave as a CO₂ sink and a source of organic carbon and as producers and consumers of CH₄. When it comes to agronomic management, the source and sink of all these GHGs are distinct. Therefore, several approaches to measuring GHG emissions from agricultural soils are available and can be categorized into chamber systems and remote sensing approaches. Sustainable agriculture stands out as a viable and transformative approach to increase agricultural efficiency while addressing the challenge of GHG emissions. Incorporating advanced technologies, precise data analytics, and site-specific management practices can offer a pathway to mitigate GHG emissions, thereby reducing the global warming potential (GWP). Therefore, this review paper focuses solely on the drivers influencing and involving soil emissions and on quantification approaches for GHG emissions. In addition, mitigation practices aimed at optimizing GHG emissions from agricultural soils are highlighted.

Crop yields are adversely affected by climate change; therefore, it is crucial to develop climate adaptation strategies to mitigate the impacts of increasing climate variability on the agriculture system to ensure food security. As one of the largest potato-producing provinces in Canada, Prince Edward Island (PEI) has recently experienced significant instability in potato production. PEI’s local farmers and stakeholders are extremely concerned about the prospects for the future of potato farming industries in the context of climate change. This study aims to use the Decision Support System for Agrotechnology Transfer (DSSAT) potato model to simulate future potato yields under the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios (including SSP1–1.9, SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5). The study evaluates the combined effects of changing climatic conditions at local scales (i.e., warming temperature and changing precipitation patterns) and increasing carbon dioxide (CO₂) concentration in the atmosphere. The results indicate future significant declines in potato yield in PEI under the current farming practices. In particular, under the high-emission scenarios (e.g., SSP3–7.0 and SSP5–8.5), the potato yield in PEI would decline by 48% and 60% in the 2070s and by 63% and 80% by 2090s; even under the low-emission scenarios (i.e., SSP1–1.9 and SSP1–2.6), the potato yield in PEI would still decline by 6–10%. This implies that it is important to develop effective climate adaptation measures (e.g., adjusting farming practices and introducing supplemental irrigation plans) to ensure the long-term sustainability of potato production in PEI.

Oysters are enriched with high-quality protein and are widely known for their exquisite taste. The production of oysters plays an important role in the local economies of coastal communities in many countries, including Atlantic Canada, because of their high economic value. However, because of the changing climatic conditions in recent years, oyster aquaculture faces potentially negative impacts, such as increasing water acidification, warming water temperature, high salinity, invasive species, algal blooms, and other environmental factors. Although a few isolated effects of climate change on oyster aquaculture have been reported in recent years, it is not well understood how climate change will affect oyster aquaculture from a systematic perspective. In the first part of this study, we present a systematic review of the impacts of climate change and some key environmental factors affecting oyster production on a global scale. The study also identifies knowledge gaps and challenges. In addition, we present key research directions that will facilitate future investigations.

Climate change has been attracting significant attention in Canada lately. This study investigates spatiotemporal air temperature and precipitation changes by developing high-resolution (i.e., 1 m × 1 km grid) climate maps from 1971 to 2020. The climate monitoring data are collected and synthesized from various sources, and then used to develop high-resolution climate maps with state-of-the-art spatial interpolation methods. The error metrics results show that the inverse distance weighting method performs the best for air temperature and precipitation and thus is used in this study. Significant temporal trends show that the annual mean temperature increased by 0.03 °C/year in western and eastern Prince Edward Island (PEI), covering 62.75% of PEI area. Similarly, the annual precipitation has decreased by around 4.8 mm/year in Prince County and eastern parts of Queens and Kings Counties, covering 62.81% of PEI area. In growing season, temperature has increased by 0.05 °C/year and precipitation is decreased by 2.1 mm/year in Prince County. This information illustrates the dynamics of temperature and precipitation toward the changing climate.