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Object oriented image classification (OOIC) and neural network aided Markov Chain (MC) modeling tools were used to map and predict land use and land cover (LULC) changes. A case study in the Kiskatinaw River Watershed (KRW) of Canada was presented. With an overall classification accuracy of 90.45%, the multi-temporal Landsat satellite images of KRW were analyzed for 11 selected LULC types. It was found that KRW experienced a significant wetland depletion along with a change in forest cover types from 1984 to 2010. The vulnerability of LULC change in different parts of KRW was predicted through MC modeling based on the obtained transition probability, and the results indicated slight LULC changes from 2010 with a wetland depletion of 67.89 km 2. In summary, the proposed methods generated valuable results for informed LULC management and hold the potential to be applied to other watersheds.

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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.

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We combined multi-layer perceptron (MLP) neural network and Markov Chain (MC) modeling with object-based image analysis (OBIA) to map and predict land use and land cover (LULC) changes in Stoney Creek Watershed (SCW), British Columbia, Canada. Unsupervised classification was performed using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to produce LULC maps of years 1986, 1999 and 2016. The classification resulted in an overall accuracy of 91.50%. The results show that coniferous forest in SCW experienced a sharp loss while agriculture area increased (4.77% land gain) from 1986 to 2016. LULC scenarios were predicted through MLP neural network and MC modeling based on LULC change analysis data and transition potential. The results indicated that 'Coniferous Forest' LULC type had the highest (3.38% land loss) transition potential and 'Water' and 'Urban Area' LULC types had the lowest transition potential. Application of the proposed method provided valuable information of LULC patterns and dynamics for planners and researchers. The method also has the potential for improved management in other watersheds with similar LULC types.