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A platform for research: civil engineering, architecture and urbanism
Reconstructing historical forest spatial patterns based on CA-AdaBoost-ANN model in northern Guangzhou, China
https://orcid.org/0000-0003-1943-0185
Highlights An example was presented to demonstrate how to reconstruct the historical forest spatial patterns. CA-Adaboost-ANN model can be used in reconstructing a region forest as well as a forest stand. This novel method could be also used for prediction of future scenarios.
Abstract Influenced by natural and man-made factors—especially urbanization—regional forest landscapes and structures are in a dynamic process of constant change. It is of great significance to reconstruct the historical pattern of forest landscapes and construct maps of forest landscapes for long time series. Based on the investigation of Fengshui and carbon sequestration forests in northern Guangzhou city, this study combined with an artificial neural network (ANN) improved using the AdaBoost algorithm to create a cellular automaton (CA) to reconstruct species compositions and spatial distributions of historical forests. The model had the best effect at a 30 m spatial scale. At 30 m spatial scale, compared with the actual forest community’s spatial distribution, the overall accuracy of forest community distribution reconstructed by our retrospective model in 2000 was 89.17 %, the Lee Sallee index was 0.7972, and the landscape similarity index was 84.62 %. In 1990, the overall accuracy was 86.78 %, the Lee Sallee index was 0.7604, and the landscape similarity index was 80.84 %. This study provides an effective method for the reconstruction of forest vegetation patterns and the prediction of future scenarios.
Reconstructing historical forest spatial patterns based on CA-AdaBoost-ANN model in northern Guangzhou, China
https://orcid.org/0000-0003-1943-0185
Highlights An example was presented to demonstrate how to reconstruct the historical forest spatial patterns. CA-Adaboost-ANN model can be used in reconstructing a region forest as well as a forest stand. This novel method could be also used for prediction of future scenarios.
Abstract Influenced by natural and man-made factors—especially urbanization—regional forest landscapes and structures are in a dynamic process of constant change. It is of great significance to reconstruct the historical pattern of forest landscapes and construct maps of forest landscapes for long time series. Based on the investigation of Fengshui and carbon sequestration forests in northern Guangzhou city, this study combined with an artificial neural network (ANN) improved using the AdaBoost algorithm to create a cellular automaton (CA) to reconstruct species compositions and spatial distributions of historical forests. The model had the best effect at a 30 m spatial scale. At 30 m spatial scale, compared with the actual forest community’s spatial distribution, the overall accuracy of forest community distribution reconstructed by our retrospective model in 2000 was 89.17 %, the Lee Sallee index was 0.7972, and the landscape similarity index was 84.62 %. In 1990, the overall accuracy was 86.78 %, the Lee Sallee index was 0.7604, and the landscape similarity index was 80.84 %. This study provides an effective method for the reconstruction of forest vegetation patterns and the prediction of future scenarios.
Reconstructing historical forest spatial patterns based on CA-AdaBoost-ANN model in northern Guangzhou, China
https://orcid.org/0000-0003-1943-0185
Highlights An example was presented to demonstrate how to reconstruct the historical forest spatial patterns. CA-Adaboost-ANN model can be used in reconstructing a region forest as well as a forest stand. This novel method could be also used for prediction of future scenarios.
Abstract Influenced by natural and man-made factors—especially urbanization—regional forest landscapes and structures are in a dynamic process of constant change. It is of great significance to reconstruct the historical pattern of forest landscapes and construct maps of forest landscapes for long time series. Based on the investigation of Fengshui and carbon sequestration forests in northern Guangzhou city, this study combined with an artificial neural network (ANN) improved using the AdaBoost algorithm to create a cellular automaton (CA) to reconstruct species compositions and spatial distributions of historical forests. The model had the best effect at a 30 m spatial scale. At 30 m spatial scale, compared with the actual forest community’s spatial distribution, the overall accuracy of forest community distribution reconstructed by our retrospective model in 2000 was 89.17 %, the Lee Sallee index was 0.7972, and the landscape similarity index was 84.62 %. In 1990, the overall accuracy was 86.78 %, the Lee Sallee index was 0.7604, and the landscape similarity index was 80.84 %. This study provides an effective method for the reconstruction of forest vegetation patterns and the prediction of future scenarios.
Reconstructing historical forest spatial patterns based on CA-AdaBoost-ANN model in northern Guangzhou, China
Zhan, Xin (author) / Yu, Shixiao (author) / Li, Yide (author) / Zhou, Zhang (author) / Cao, Honglin (author) / Tang, Guangda (author)
2023-11-04
Article (Journal)
Electronic Resource
English
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