Abstract
Land cover change (LCC) is typically characterized by infrequent changes over space and time. Data-driven methods such as deep learning (DL) approaches have proven effective in many domains for predictive and classification tasks. When applied to geospatial data, sequential DL methods such as long short-term memory (LSTM) have yielded promising results in remote sensing and GIScience studies. However, the characteristics of geospatial datasets selected for use with these methods have demonstrated important implications on method performance. The number of data layers available, the rate of LCC, and inherent errors resulting from classification procedures are expected to influence model performance. Yet, it is unknown how these can affect compatibility with the LSTM method. As such, the main objective of this study is to explore the capacity of LSTM to forecast patterns that have emerged from LCC dynamics given varying temporal resolutions, persistent land cover classes, and auxiliary data layers pertaining to classification confidence. Stacked LSTM modeling approaches are applied to 17-year MODIS land cover datasets focused on the province of British Columbia, Canada. This geospatial data is reclassified to four major land cover (LC) classes during pre-processing procedures. The evaluation considers the dataset at variable temporal resolutions to demonstrate the significance of geospatial data characteristics on LSTM method performance in several scenarios. Results indicate that LSTM can be utilized for forecasting LCC patterns when there are few limitations on temporal intervals of the datasets provided. Likewise, this study demonstrates improved performance measures when there are classes that do not change. Furthermore, providing classification confidence data as ancillary input also demonstrated improved results when the number of timesteps or temporal resolution is limited. This study contributes to future applications of DL and LSTM methods for forecasting LCC.
Highlights
Land cover changes (LCCs) are typically slow changes occurring across Earth’s surface over long periods of time [1]
The goal of this study is to evaluate the effectiveness of long short-term memory (LSTM) networks for LCC forecasting considering actual and hybrid datasets, where hybrid datasets are created by integrating an actual dataset with a hypothetical persistent class
The stacked LSTM modeling approach for forecasting LCC aims to detect patterns occurring across the temporal dimension to forecast forest, anthropogenic areas, non-forested non-anthropogenic areas, and water
Summary
Land cover changes (LCCs) are typically slow changes occurring across Earth’s surface over long periods of time [1]. LCC has been previously assessed at local, regional, and global extents [7]. LCC has been linked to changes in precipitation, air temperature, and ecology at local and regional scales. LCC studies conducted at a global scale have assessed the cumulative implications of land change processes such as urban growth and deforestation [8]. Addressing LCC from a top-down perspective, data-driven modeling tactics enable the extraction and detections of patterns that have resulted from local interactions [9]. Top-down approaches are primarily focused on overall patterns that result from processes, utilizing satellite and aggregated data sources such as Census data to obtain rates of land change over time
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