TY - JOUR
T1 - Expressing the spatio-temporal pattern of farmland change in arid lands using landscape metrics
AU - Sun, Bo
AU - Zhou, Qiming
N1 - Funding Information:
We greatly appreciate the contributions of three anonymous reviewers, who provided insight and constructive comments on earlier versions of this manuscript, and thank Dr. Raynor Shaw for his valuable suggestions and conscientious English language editing. The research was supported by the International Science and Technology Collaboration Project of China (2010DFA92720-24), Natural Science Foundation of China (NSFC) General Research Grant (41471340) and Research Grants Council (RGC) of Hong
Publisher copyright:
Copyright © 2015 Elsevier Ltd. All rights reserved.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Identifying, recording and monitoring land cover change on the Earth's surface is a complex procedure. Spatio-temporal modelling is an effective approach to simplifying and simulating the process. Existing spatio-temporal modelling methods are typically based on either, an overlay of multi-temporal land cover maps, or temporal trend analysis of spatial pattern indices. Consequently, an understanding of the spatial dynamics of any changes is either fragmental in the former case, or invisible in the latter case, due to the lack of adequate geographical location information.In the arid zone of western China, a widely accepted belief is that rapid farmland expansion and the subsequent abandonment of the farms, or their mis-management, would lead to soil salinisation and desertification. In order to better understand the spatio-temporal pattern of farmland change, this paper proposes an integrated approach that combines the two methods of pixel-based trajectory analysis and class-level spatial pattern metrics. Multi-temporal remote sensing images were collected beginning in 1994, a year that captured the initial effects of the period of rapid farmland expansion. Historical change trajectories were established for each pixel and categorized according to change types (i.e. expanding or shrinking). The spatial dynamics of farmland change can then be illustrated by mapping the change trajectory classes. Spatial patterns of farmland change were quantified by employing distribution-related landscape metrics, such as indices of interspersion (IJI), connectivity (COHESION) and isolation (ENN), to analyse farmland development models of the two river basins in the study area. Shape indices, including overall shape (nLSI) and edge shape (FRAC), were applied to appraise the structural stability of the farmlands over time. Results indicate that, over the past two decades, the area subject to farmland expansion was significantly larger than that experiencing farmland abandonment. The relatively rapid expansion of farmland exhibited a concentrated pattern, and generally followed a layer-based development model. The study showed that the proposed research method effectively visualized and quantified the spatio-temporal dynamics of farmland change.
AB - Identifying, recording and monitoring land cover change on the Earth's surface is a complex procedure. Spatio-temporal modelling is an effective approach to simplifying and simulating the process. Existing spatio-temporal modelling methods are typically based on either, an overlay of multi-temporal land cover maps, or temporal trend analysis of spatial pattern indices. Consequently, an understanding of the spatial dynamics of any changes is either fragmental in the former case, or invisible in the latter case, due to the lack of adequate geographical location information.In the arid zone of western China, a widely accepted belief is that rapid farmland expansion and the subsequent abandonment of the farms, or their mis-management, would lead to soil salinisation and desertification. In order to better understand the spatio-temporal pattern of farmland change, this paper proposes an integrated approach that combines the two methods of pixel-based trajectory analysis and class-level spatial pattern metrics. Multi-temporal remote sensing images were collected beginning in 1994, a year that captured the initial effects of the period of rapid farmland expansion. Historical change trajectories were established for each pixel and categorized according to change types (i.e. expanding or shrinking). The spatial dynamics of farmland change can then be illustrated by mapping the change trajectory classes. Spatial patterns of farmland change were quantified by employing distribution-related landscape metrics, such as indices of interspersion (IJI), connectivity (COHESION) and isolation (ENN), to analyse farmland development models of the two river basins in the study area. Shape indices, including overall shape (nLSI) and edge shape (FRAC), were applied to appraise the structural stability of the farmlands over time. Results indicate that, over the past two decades, the area subject to farmland expansion was significantly larger than that experiencing farmland abandonment. The relatively rapid expansion of farmland exhibited a concentrated pattern, and generally followed a layer-based development model. The study showed that the proposed research method effectively visualized and quantified the spatio-temporal dynamics of farmland change.
KW - Arid zone
KW - Change trajectory analysis
KW - Farmland change
KW - Landscape metrics
KW - Spatio-temporal process
UR - http://www.scopus.com/inward/record.url?scp=84939641373&partnerID=8YFLogxK
U2 - 10.1016/j.jaridenv.2015.08.007
DO - 10.1016/j.jaridenv.2015.08.007
M3 - Journal article
AN - SCOPUS:84939641373
SN - 0140-1963
VL - 124
SP - 118
EP - 127
JO - Journal of Arid Environments
JF - Journal of Arid Environments
ER -