Computer Vision and Machine Learning Methods for Land Use Land Cover Change Modelling and Forecasting

Dear Colleagues,

Population growth, resource extraction, and natural disturbances are precipitating rapid changes in global land cover dynamics; for example, the spatial expansion of cities is culminating in significant vegetation and habitat loss and/or fragmentation. Forecasting terrestrial change using Earth observation sensor data has been a daunting task; however, with emerging algorithms, new computational tools, and sensor capabilities offered by recent technological advancements, Earth observation and monitoring is taking a more sophisticated dimension with improved accuracy. Accurate and up-to-date knowledge of land use and land cover (LULC) trajectories would enable policy-makers to devise and implement effective and sustainable land management policies.

This Special Issue (SI) aims to attract cutting-edge research with a focus on LULC modelling and forecasting using emerging computer vision, deep learning methods, remote sensing data, and simulation-derived data.

This SI invites original research and review articles that focus on the thematic areas below. Submissions that deploy cellular automata and Bayesian methods to model land-related human and natural disturbances are also highly encouraged in this SI.

(a) Computer vision applications for remote sensing data;

(b) Deep convolutional neural networks for change detection;

(c) Multi-modal data fusion for vegetation change forecasting;

(d) Land use/land cover change characterization using LiDAR data;

(e) Vegetation index-optimized landcover change prediction;

(f) Ecosystems change/productivity forecasting;

(g) Landscape change and/or similarity modelling;

(h) Spatiotemporal analysis of land degradation patterns;

(i) Transformer networks for LULC change prediction;

(j) Hyperspectral remote sensing for vegetation species mapping;

(k) Recurrent neural networks for time analysis of landcover trends;

(l) Long short-term memory (LSTM) networks for forecasting LULC.

Dr. Karim Malik
Dr. Cameron Proctor
Guest Editors

Manuscript Submission Information

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