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Continuous and consistent land use/cover change estimates using socio-ecological data

Continuous and consistent land use/cover change estimates using socio-ecological data
Continuous and consistent land use/cover change estimates using socio-ecological data
A growing body of research shows the importance of land use/cover change (LULCC) on modifying the Earth system. Land surface models are used to stimulate land-atmosphere dynamics at the macroscale, but model bias and uncertainty remain that need to be addressed before the importance of LULCC is fully realized. In this study, we propose a method of improving LULCC estimates for land surface modeling exercises. The method is driven by projectable socio-ecological geospatial predictors available seamlessly across sub-Saharan Africa and yielded continuous (annual) estimates of LULCC at 5 km × 5 km spatial resolution. The method was developed with 2252 sample area frames of 5 km × 5 km consisting of the proportion of several land cover types in Kenya over multiple years. Forty-three socio-ecological predictors were evaluated for model development. Machine learning was used for data reduction, and simple (functional) relationships defined by generalized additive models were constructed on a subset of the highest-ranked predictors p ≤ 10) to estimate LULCC. The predictors explained 62 and 65 % of the variance in the proportion of agriculture and natural vegetation, respectively, but were less successful at estimating more descriptive land cover types. In each case, population density on an annual basis was the highest-ranked predictor. The approach was compared to a commonly used remote sensing classification procedure, given the wide use of such techniques for macroscale LULCC detection, and outperformed it for each land cover type. The approach was used to demonstrate significant trends in expanding (declining) agricultural (natural vegetation) land cover in Kenya from 1983 to 2012, with the largest increases (declines) occurring in densely populated high agricultural production zones. Future work should address the improvement (development) of existing (new) geospatial predictors and issues of model scalability and transferability.
2190-4979
55-73
Marshall, Michael
5a048221-bc6a-4152-9198-dffebb05a2d0
Norton-Griffiths, Michael
c707b728-ff3b-4afa-9648-9220a32e4e93
Herr, Harvey
6de39916-a880-4d49-b651-7c183ede7253
Lamprey, Richard
e0674adc-a987-4a30-a732-099eefdeed44
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Vagen, Tor
c7f4d147-e9c4-4e41-b03b-2ec2e56d8e35
Okotto-Okotto, Joseph
a8cb5abe-ee03-4c93-978b-b02a02350e26
et al.
Marshall, Michael
5a048221-bc6a-4152-9198-dffebb05a2d0
Norton-Griffiths, Michael
c707b728-ff3b-4afa-9648-9220a32e4e93
Herr, Harvey
6de39916-a880-4d49-b651-7c183ede7253
Lamprey, Richard
e0674adc-a987-4a30-a732-099eefdeed44
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Vagen, Tor
c7f4d147-e9c4-4e41-b03b-2ec2e56d8e35
Okotto-Okotto, Joseph
a8cb5abe-ee03-4c93-978b-b02a02350e26

et al. (2017) Continuous and consistent land use/cover change estimates using socio-ecological data. Earth System Dynamics, 8 (1), 55-73. (doi:10.5194/esd-8-55-2017).

Record type: Article

Abstract

A growing body of research shows the importance of land use/cover change (LULCC) on modifying the Earth system. Land surface models are used to stimulate land-atmosphere dynamics at the macroscale, but model bias and uncertainty remain that need to be addressed before the importance of LULCC is fully realized. In this study, we propose a method of improving LULCC estimates for land surface modeling exercises. The method is driven by projectable socio-ecological geospatial predictors available seamlessly across sub-Saharan Africa and yielded continuous (annual) estimates of LULCC at 5 km × 5 km spatial resolution. The method was developed with 2252 sample area frames of 5 km × 5 km consisting of the proportion of several land cover types in Kenya over multiple years. Forty-three socio-ecological predictors were evaluated for model development. Machine learning was used for data reduction, and simple (functional) relationships defined by generalized additive models were constructed on a subset of the highest-ranked predictors p ≤ 10) to estimate LULCC. The predictors explained 62 and 65 % of the variance in the proportion of agriculture and natural vegetation, respectively, but were less successful at estimating more descriptive land cover types. In each case, population density on an annual basis was the highest-ranked predictor. The approach was compared to a commonly used remote sensing classification procedure, given the wide use of such techniques for macroscale LULCC detection, and outperformed it for each land cover type. The approach was used to demonstrate significant trends in expanding (declining) agricultural (natural vegetation) land cover in Kenya from 1983 to 2012, with the largest increases (declines) occurring in densely populated high agricultural production zones. Future work should address the improvement (development) of existing (new) geospatial predictors and issues of model scalability and transferability.

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More information

Accepted/In Press date: 4 January 2017
Published date: 8 February 2017
Additional Information: Publisher Copyright: © Author(s) 2017.

Identifiers

Local EPrints ID: 476227
URI: http://eprints.soton.ac.uk/id/eprint/476227
ISSN: 2190-4979
PURE UUID: e20e81a6-2cc5-4c1b-9a19-b1f67dc7c8bb
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 14 Apr 2023 16:48
Last modified: 06 Jun 2024 01:54

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Contributors

Author: Michael Marshall
Author: Michael Norton-Griffiths
Author: Harvey Herr
Author: Richard Lamprey
Author: Tor Vagen
Author: Joseph Okotto-Okotto
Corporate Author: et al.

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