Theory, Data, and Methods

2019 ◽  
pp. 156-202
Author(s):  
Eda Ustaoglu ◽  
Arif Çagdaş Aydinoglu
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Information System ◽  
Land Use Change ◽  
Model Development ◽  
Agent Based ◽  
Change Models ◽  
Multi Scale ◽  
Land Use Dynamics ◽  
Scale Characteristics

Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.

scholarly journals An open and extensible framework for spatially explicit land use change modelling in R: the lulccR package (0.1.0)

2015 ◽  
Vol 8 (4) ◽  
pp. 3359-3402 ◽  
Author(s):  
S. Moulds ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Global Environmental Change ◽  
Model Development ◽  
Spatially Explicit ◽  
Change Models ◽  
R Programming Language ◽  
Global Environmental ◽  
R Programming ◽  
Scientific Results

Abstract. Land use change has important consequences for biodiversity and the sustainability of ecosystem services, as well as for global environmental change. Spatially explicit land use change models improve our understanding of the processes driving change and make predictions about the quantity and location of future and past change. Here we present the lulccR package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of different models; (3) different aspects of the modelling procedure must be performed in different environments because existing applications usually only perform the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the widely used CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a dataset included with the package. It is envisaged that lulccR will enable future model development and comparison within an open environment.

Integrating remote sensing and machine learning into environmental monitoring and assessment of land use change

2021 ◽  
Vol 27 ◽  
pp. 1239-1254
Author(s):  
Hong Anh Thi Nguyen ◽  
Tip Sophea ◽  
Shabbir H. Gheewala ◽  
Rawee Rattanakom ◽  
Thanita Areerob ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Environmental Monitoring

scholarly journals Land Use Dynamics and Optimization from 2000 to 2020 in East Guangdong Province, China

2021 ◽  
Vol 13 (6) ◽  
pp. 3473
Author(s):  
Yong Lai ◽  
Guangqing Huang ◽  
Shengzhong Chen ◽  
Shaotao Lin ◽  
Wenjun Lin ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Landscape Connectivity ◽  
Land Use Changes ◽  
Research Result ◽  
Global Environmental Change ◽  
Ecological Processes ◽  
Buffer Zones ◽  
Conflict Zones ◽  
Land Use Dynamics

Anthropogenic land-use change is one of the main drivers of global environmental change. China has been on a fast track of land-use change since the Reform and Opening-up policy in 1978. In view of the situation, this study aims to optimize land use and provide a way to effectively coordinate the development and ecological protection in China. We took East Guangdong (EGD), an underdeveloped but populous region, as a case study. We used land-use changes indexes to demonstrate the land-use dynamics in EGD from 2000 to 2020, then identified the hot spots for fast-growing areas of built-up land and simulated land use in 2030 using the future land-use simulation (FLUS) model. The results indicated that the cropland and the built-up land changed in a large proportion during the study period. Then we established the ecological security pattern (ESP) according to the minimal cumulative resistance model (MCRM) based on the natural and socioeconomic factors. Corridors, buffer zones, and the key nodes were extracted by the MCRM to maintain landscape connectivity and key ecological processes of the study area. Moreover, the study showed the way to identify the conflict zones between future built-up land expansion with the corridors and buffer zones, which will be critical areas of consideration for future land-use management. Finally, some relevant policy recommendations are proposed based on the research result.

scholarly journals Current and Future Land Use Characters of a National Central City in Eco-Fragile Region—A Case Study in Xi’an City Based on FLUS Model

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 286
Author(s):  
Dingrao Feng ◽  
Wenkai Bao ◽  
Meichen Fu ◽  
Min Zhang ◽  
Yiyu Sun
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Development ◽  
Urban Expansion ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Pattern Change ◽  
Land Use Dynamics ◽  
The Future ◽  
Xi’An City

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

Machine-learning algorithms for land use dynamics in Lake Haramaya Watershed, Ethiopia

2021 ◽  
Author(s):  
Gezahegn Weldu Woldemariam ◽  
Degefie Tibebe ◽  
Tesfamariam Engida Mengesha ◽  
Tadele Bedo Gelete
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Land Use Dynamics

An open framework for agent based modelling of agricultural land use change

2014 ◽  
Vol 61 ◽  
pp. 19-38 ◽  
Author(s):  
Dave Murray-Rust ◽  
Derek T. Robinson ◽  
Eleonore Guillem ◽  
Eleni Karali ◽  
Mark Rounsevell
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Agent Based ◽  
Open Framework ◽  
Agent Based Modelling

scholarly journals How model paradigms affect our representation of future land-use change

2020 ◽  
Author(s):  
Calum Brown ◽  
Ian Holman ◽  
Mark Rounsevell
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
System Development ◽  
Active Management ◽  
Integrated Modelling ◽  
Agent Based Model ◽  
Agent Based ◽  
Modelling Framework ◽  
Land Use Models ◽  
Landscape Scales

Abstract. Land use models operating at regional to global scales are almost exclusively based on the single paradigm of economic optimisation. Models based on different paradigms are known to produce very different results, but these are not always equivalent or attributable to particular assumptions. In this study, we compare two pan-European land use models that are based on the same integrated modelling framework and utilise the same climatic and socio-economic scenarios, but which adopt fundamentally different model paradigms. One of these is a constrained optimising economic-equilibrium model and the other is a stochastic agent-based model. We run both models for a range of scenario combinations and compare their projections of spatial and aggregate land use change and ecosystem service supply. We find that the agent-based model projects more multifunctional and heterogeneous landscapes in most scenarios, providing a wider range of ecosystem services at landscape scales, as agents make individual, time-dependent decisions that reflect economic and non-economic motivations. This tendency also results in food shortages under certain scenario conditions. The optimisation model, in contrast, maintains food supply through intensification of agricultural production in the most profitable areas, sometimes at the expense of active management in large, contiguous parts of Europe. We relate the principal differences observed to underlying model assumptions, and hypothesise that optimisation may be appropriate in scenarios that allow for coherent political and economic control of land systems, but not in scenarios where economic and other scenario conditions prevent the normal functioning of price signals and responses. In these circumstances, agent-based modelling allows explicit consideration of behavioural processes, but in doing so provides a highly flexible account of land system development that is harder to link to underlying assumptions. We suggest that structured comparisons of parallel, transparent but paradigmatically distinct models are an important method for better understanding the potential scope and uncertainties of future land use change.

An agent-based approach to explore the effect of voluntary mechanisms on land use change: A case in rural Queensland, Australia

2010 ◽  
Vol 91 (12) ◽  
pp. 2615-2625 ◽  
Author(s):  
Diego Valbuena ◽  
Arnold K. Bregt ◽  
Clive McAlpine ◽  
Peter H. Verburg ◽  
Leonie Seabrook
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Agent Based
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