Predicting future plantation forest development in response to policy initiatives: A case study of the Warren River Catchment in Western Australia

Thayse Nery, Rohan Sadler, Ben White, Maksym Polyakov

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The Warren River Catchment in the south-west of Western Australia exemplifies how inconsistent forestry investment policies can have adverse environmental effects especially related to dryland salinity. The catchment experienced a rapid expansion of plantation forest in the late 1990s due to tax benefits for forestry Managed Investment Schemes. This paper assesses the landscape effect of incentive based policies by measuring the land use and land cover (LULC) response in the catchment, and forecasts LULC change. The paper applies a spatial modelling procedure that integrates Markov transition probabilities, multilayer perceptron neural network and cellular automata to provide an accurate forecast of LULC change over 35 years from 1979 to 2014. In the first stage, geospatial analysis determines the spatial drivers of LULC conversions. Second, Markov transition probability matrices are estimated, and the multilayer perceptron was trained to determine a model for every transition based on spatial drivers. Finally, a cellular automata model was applied to forecast spatially explicit changes in LULC to 2025 under the current policy regime. The predictive power of the model was validated with Kappa statistics, and vis-a-visa null model. The simulation forecasts an increase in the agricultural areas by 2025 compared to 2014; whereas harvested native forest areas were predicted to decrease, contributing to a slight increase of the native forest areas. Despite government efforts to increase the areas of plantations, the model predicts a decrease in this land use due to a progressive reduction in tax incentives provided to Managed Investment Schemes. These results will assist decision-makers in improving policy, by working through the long-term implications of policy incentives for forestry in terms of broader landscape objectives related to salinity and conservation.

Original languageEnglish
Pages (from-to)299-310
Number of pages12
JournalEnvironmental Science & Policy
Volume92
DOIs
Publication statusPublished - Feb 2019

Cite this

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title = "Predicting future plantation forest development in response to policy initiatives: A case study of the Warren River Catchment in Western Australia",
abstract = "The Warren River Catchment in the south-west of Western Australia exemplifies how inconsistent forestry investment policies can have adverse environmental effects especially related to dryland salinity. The catchment experienced a rapid expansion of plantation forest in the late 1990s due to tax benefits for forestry Managed Investment Schemes. This paper assesses the landscape effect of incentive based policies by measuring the land use and land cover (LULC) response in the catchment, and forecasts LULC change. The paper applies a spatial modelling procedure that integrates Markov transition probabilities, multilayer perceptron neural network and cellular automata to provide an accurate forecast of LULC change over 35 years from 1979 to 2014. In the first stage, geospatial analysis determines the spatial drivers of LULC conversions. Second, Markov transition probability matrices are estimated, and the multilayer perceptron was trained to determine a model for every transition based on spatial drivers. Finally, a cellular automata model was applied to forecast spatially explicit changes in LULC to 2025 under the current policy regime. The predictive power of the model was validated with Kappa statistics, and vis-a-visa null model. The simulation forecasts an increase in the agricultural areas by 2025 compared to 2014; whereas harvested native forest areas were predicted to decrease, contributing to a slight increase of the native forest areas. Despite government efforts to increase the areas of plantations, the model predicts a decrease in this land use due to a progressive reduction in tax incentives provided to Managed Investment Schemes. These results will assist decision-makers in improving policy, by working through the long-term implications of policy incentives for forestry in terms of broader landscape objectives related to salinity and conservation.",
keywords = "Land-use policy, Plantation forest, Multilayer perceptron, Cellular automata, Western Australia, LAND-COVER CHANGE, CELLULAR-AUTOMATA MODELS, URBAN-GROWTH, MARKOV-CHAIN, SIMULATION, SALINITY, MITIGATION, BENEFITS, DYNAMICS, DRIVERS",
author = "Thayse Nery and Rohan Sadler and Ben White and Maksym Polyakov",
year = "2019",
month = "2",
doi = "10.1016/j.envsci.2018.11.021",
language = "English",
volume = "92",
pages = "299--310",
journal = "Environmental Science & Policy",
issn = "1462-9011",
publisher = "Elsevier",

}

TY - JOUR

T1 - Predicting future plantation forest development in response to policy initiatives

T2 - A case study of the Warren River Catchment in Western Australia

AU - Nery, Thayse

AU - Sadler, Rohan

AU - White, Ben

AU - Polyakov, Maksym

PY - 2019/2

Y1 - 2019/2

N2 - The Warren River Catchment in the south-west of Western Australia exemplifies how inconsistent forestry investment policies can have adverse environmental effects especially related to dryland salinity. The catchment experienced a rapid expansion of plantation forest in the late 1990s due to tax benefits for forestry Managed Investment Schemes. This paper assesses the landscape effect of incentive based policies by measuring the land use and land cover (LULC) response in the catchment, and forecasts LULC change. The paper applies a spatial modelling procedure that integrates Markov transition probabilities, multilayer perceptron neural network and cellular automata to provide an accurate forecast of LULC change over 35 years from 1979 to 2014. In the first stage, geospatial analysis determines the spatial drivers of LULC conversions. Second, Markov transition probability matrices are estimated, and the multilayer perceptron was trained to determine a model for every transition based on spatial drivers. Finally, a cellular automata model was applied to forecast spatially explicit changes in LULC to 2025 under the current policy regime. The predictive power of the model was validated with Kappa statistics, and vis-a-visa null model. The simulation forecasts an increase in the agricultural areas by 2025 compared to 2014; whereas harvested native forest areas were predicted to decrease, contributing to a slight increase of the native forest areas. Despite government efforts to increase the areas of plantations, the model predicts a decrease in this land use due to a progressive reduction in tax incentives provided to Managed Investment Schemes. These results will assist decision-makers in improving policy, by working through the long-term implications of policy incentives for forestry in terms of broader landscape objectives related to salinity and conservation.

AB - The Warren River Catchment in the south-west of Western Australia exemplifies how inconsistent forestry investment policies can have adverse environmental effects especially related to dryland salinity. The catchment experienced a rapid expansion of plantation forest in the late 1990s due to tax benefits for forestry Managed Investment Schemes. This paper assesses the landscape effect of incentive based policies by measuring the land use and land cover (LULC) response in the catchment, and forecasts LULC change. The paper applies a spatial modelling procedure that integrates Markov transition probabilities, multilayer perceptron neural network and cellular automata to provide an accurate forecast of LULC change over 35 years from 1979 to 2014. In the first stage, geospatial analysis determines the spatial drivers of LULC conversions. Second, Markov transition probability matrices are estimated, and the multilayer perceptron was trained to determine a model for every transition based on spatial drivers. Finally, a cellular automata model was applied to forecast spatially explicit changes in LULC to 2025 under the current policy regime. The predictive power of the model was validated with Kappa statistics, and vis-a-visa null model. The simulation forecasts an increase in the agricultural areas by 2025 compared to 2014; whereas harvested native forest areas were predicted to decrease, contributing to a slight increase of the native forest areas. Despite government efforts to increase the areas of plantations, the model predicts a decrease in this land use due to a progressive reduction in tax incentives provided to Managed Investment Schemes. These results will assist decision-makers in improving policy, by working through the long-term implications of policy incentives for forestry in terms of broader landscape objectives related to salinity and conservation.

KW - Land-use policy

KW - Plantation forest

KW - Multilayer perceptron

KW - Cellular automata

KW - Western Australia

KW - LAND-COVER CHANGE

KW - CELLULAR-AUTOMATA MODELS

KW - URBAN-GROWTH

KW - MARKOV-CHAIN

KW - SIMULATION

KW - SALINITY

KW - MITIGATION

KW - BENEFITS

KW - DYNAMICS

KW - DRIVERS

U2 - 10.1016/j.envsci.2018.11.021

DO - 10.1016/j.envsci.2018.11.021

M3 - Article

VL - 92

SP - 299

EP - 310

JO - Environmental Science & Policy

JF - Environmental Science & Policy

SN - 1462-9011

ER -