Land use and land cover (LULC) classification maps help understand the state and trends of agricultural production and provide insights for applications in environmental monitoring. One of the major downfalls of the LULC technique is inherently linked to its need for ground truth data to cross-validate maps. This paper aimed at evaluating the efficiency of machine learning (ML) in limiting the use of ground truth data for LULC maps. This was accomplished by (1) extracting reliable LULC information from Sentinel-2 and Landsat-8 s images, (2) generating remote sensing indices used to train ML algorithms, and (3) comparing the results with ground truth data. The remote sensing indices that were tested include the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the normalized built-up index (NDBI), the urban index (UI), and the normalized bare land index (NBLI). Extracted vegetation indices were evaluated on three ML algorithms, namely, random forest (RF), k-nearest neighbour (K-NN), and k dimensional-tree (KD-Tree). The accuracy of these algorithms was assessed with standard statistical measures and ground truth data randomly collected in Prince Edward Island, Canada. Results showed that high kappa coefficient values were achieved by K-NN (82% and 74%), KD-Tree (80% and 78%), and RF (83% and 73%) for Sentinel-2A and Landsat-8 imagery, respectively. RF was a better classifier than K-NN and KD-Tree and had the highest overall accuracy with Sentinel-2A satellite images (92%). This approach provides the basis for limiting the collection of ground truth data and thus reduces the labour cost, time, and resources needed to collect ground truth data for LULC maps.
A 3-yr study on harvesting management applied to alfalfa (Medicago sativa L.) shows that a three-cut system, the last cut taken in October, yielded significantly more dry matter than a two-cut system. The latter system, however, gave a more uniform production throughout the years and maintained the stand at the highest level. Fall cuttings, taken at weekly intervals from the end of August to the end of September, reduced both the yield and the stand, the earliest cuttings being most harmful. October cuttings showed little effect on the productivity of alfalfa. The percentages of non structural carbohydrates stored in the roots on I November increased only slightly with the delay in taking the third cut in September. The accumulation of the food reserves was favored most by the two-cut system and the three-cut system with the last cut taken at mid-October. The influence of the harvesting regimes on the chemical composition of the forage is also discussed.
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 of wheat, barley, corn, forages and soybeans were compiled for each year and compared to the previous year. This was accomplished using GIS software, satellite images, and geodata from the Nova Scotia’s Land Property Database. Results revealed that from 2015 to 2019, an average of 56% of the dykelands’ total surface was dedicated to the production of field crops (wheat, barley, corn, soybeans) and forage. Results also highlighted the importance of forage production on the dykelands. Forage was the largest commodity grown, representing around 80% of the total crop land area of the agricultural dykelands. Corn and soybeans were the second and third crops of abundance, constituting 12 and 4% of the total crop land area, respectively. This study represents the first attempt to document the number of hectares of the principal crops grown on Nova Scotia’s dykelands using crop inventory and property boundaries. Given the predictions of rising sea levels and the overtopping risks that the dykelands face, this study will facilitate more suitable land-use policies by providing stakeholders with an accurate quantitative assessment of the utilization of agricultural dykelands.
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