Exploring the Sensitivity of Recurrent Neural Network Models for Forecasting Land Cover Change

Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) architectures, have obtained successful outcomes in timeseries analysis tasks. While RNNs demonstrated favourable performance for Land Cover (LC) change analyses, few studies have explored or quantified the geospatial data characteristics required to utilize this method. Likewise, many studies utilize overall measures of accuracy rather than metrics accounting for the slow or sparse changes of LC that are typically observed. Therefore, the main objective of this study is to evaluate the performance of LSTM models for forecasting LC changes by conducting a sensitivity analysis involving hypothetical and real-world datasets. The intent of this assessment is to explore the implications of varying temporal resolutions and LC classes. Additionally, changing these input data characteristics impacts the number of timesteps and LC change rates provided to the respective models. Kappa variants are selected to explore the capacity of LSTM models for forecasting transitions or persistence of LC. Results demonstrate the adverse effects of coarser temporal resolutions and high LC class cardinality on method performance, despite method optimization techniques applied. This study suggests various characteristics of geospatial datasets that should be present before considering LSTM methods for LC change forecasting

Evaluating long short-term memory networks for modeling land cover change

Land cover change (LCC) can be viewed as dynamic complex systems which require relevant relationships to be encoded when represented within various modeling approaches. Recurrent Neural Networks (RNNs), specifically the Long Short-Term Memory (LSTM) variant, belong to a category of Deep Learning (DL) approaches best suited for sequential and timeseries data analysis, thus suitable for representing LCC. The primary objective of this study is to examine the capacity and effectiveness of LSTM networks for forecasting LCC given varying geospatial input datasets with feature impurities. Using synthetic and MODIS land cover datasets for British Columbia, Canada, results demonstrate the sensitivity of LSTM models to varying geospatial input dataset characteristics. Geospatial datasets with finer temporal resolutions and increased timesteps yielded favourable results while coarser temporal resolutions and fewer timesteps were affiliated with less successful outcomes. This thesis research contributes to the advancement of automated, data-driven DL methodologies for forecasting LCC

Assessing the Impact of Neighborhood Size on Temporal Convolutional Networks for Modeling Land Cover Change

Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past studies have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models. However, these studies do not adequately assess the association between neighborhood size and DL model capability to forecast LCCs, where neighborhood size refers to the spatial extent captured by each data sample. The objectives of this research study were to: (1) evaluate the effect of neighborhood size on the capacity of DL models to forecast LCCs, specifically Temporal Convolutional Networks (TCN) and Convolutional Neural Networks (CNN-TCN), and (2) assess the effect of auxiliary spatial variables on model capacity to forecast LCCs. First, each model type and neighborhood setting configuration was assessed using data derived from multitemporal MODIS LC for the Regional District of Bulkley-Nechako, Canada, comparing subareas exhibiting different amounts of LCCs with trends obtained for the full region. Next, outcomes were compared with three other study regions. The modeling results were evaluated with three-map comparison measures, where the real-world LC for the next timestep, the real-world LC for the previous timestep, and the forecasted LC for the next year were used to calculate correctly transitioned areas. Across all regions explored, it was observed that increasing neighborhood sizes improved the DL model’s capabilities to forecast short-term LCCs. CNN–TCN models forecasted the most correct LCCs for several regions while reducing error due to quantity when provided additional spatial variables. This study contributes to the systematic exploration of neighborhood sizes on selected spatiotemporal DL techniques for geographic applications

A landscape metrics-based sample weighting approach for forecasting land cover change with deep learning models

Unaddressed imbalance of multitemporal land cover (LC) data reduces deep learning (DL) model usefulness to forecast changes. To manage geospatial data imbalance, there is a lack of specialized cost-sensitive learning strategies available. Sample weights are typically derived from training instance frequencies which disregard spatial pattern complexities. Therefore, this study proposes a geospatial sample weighting approach underpinned by class-level landscape metrics (LSMs) to assign importance to categories based on relative indicators of spatial form. A case study demonstrates the application and effects of the LSM-based sample weighting approach for projecting LC changes of a region in British Columbia, Canada. Four spatiotemporal DL models are provided weighted training samples including multitemporal LC data and explanatory factors. Sample weights calculated from indicators of patch density, shape irregularity and shape heterogeneity improved figure of merit and related measures over baseline configurations. This study contributes to LC change data imbalance management strategies for DL models

Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks

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

Mitigating Imbalance of Land Cover Change Data for Deep Learning Models with Temporal and Spatiotemporal Sample Weighting Schemes

An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments to model internal parameters. The main goal of this research study was to implement and evaluate temporal and spatiotemporal sample weighting schemes that manage the influence of persistent and formerly changed areas. The proposed sample weighting schemes allocate higher weights to more recently changed areas based on the inverse temporal and spatiotemporal distance from previous changes occurring at a location or within the location’s neighborhood. Four spatiotemporal DL models (CNN-LSTM, CNN-GRU, CNN-TCN, and ConvLSTM) were used to compare the sample weighting schemes to forecast the LC changes of the Columbia-Shuswap Regional District in British Columbia, Canada, using data obtained from the MODIS annual LC dataset and other auxiliary spatial variables. The results indicate that the presented weighting schemes facilitated improvement over no sample weighting and the common inverse frequency weighting scheme for multi-year LC change forecasts, lowering errors due to quantity while reducing overall allocation error severity. This research study contributes to strategies for addressing the characteristic imbalances of multitemporal LC change datasets for DL modeling endeavors

Assessing the Impact of Neighborhood Size on Temporal Convolutional Networks for Modeling Land Cover Change

Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past studies have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models. However, these studies do not adequately assess the association between neighborhood size and DL model capability to forecast LCCs, where neighborhood size refers to the spatial extent captured by each data sample. The objectives of this research study were to: (1) evaluate the effect of neighborhood size on the capacity of DL models to forecast LCCs, specifically Temporal Convolutional Networks (TCN) and Convolutional Neural Networks (CNN-TCN), and (2) assess the effect of auxiliary spatial variables on model capacity to forecast LCCs. First, each model type and neighborhood setting configuration was assessed using data derived from multitemporal MODIS LC for the Regional District of Bulkley-Nechako, Canada, comparing subareas exhibiting different amounts of LCCs with trends obtained for the full region. Next, outcomes were compared with three other study regions. The modeling results were evaluated with three-map comparison measures, where the real-world LC for the next timestep, the real-world LC for the previous timestep, and the forecasted LC for the next year were used to calculate correctly transitioned areas. Across all regions explored, it was observed that increasing neighborhood sizes improved the DL model’s capabilities to forecast short-term LCCs. CNN–TCN models forecasted the most correct LCCs for several regions while reducing error due to quantity when provided additional spatial variables. This study contributes to the systematic exploration of neighborhood sizes on selected spatiotemporal DL techniques for geographic applications

Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks

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