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Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing.

O'Connell J, Bradter U, Benton TG - ISPRS J Photogramm Remote Sens (2015)

Bottom Line: A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909).We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m(2).The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

View Article: PubMed Central - PubMed

Affiliation: School of Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT, UK.

ABSTRACT

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m(2). The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

No MeSH data available.


Related in: MedlinePlus

Processing chain identifying the four main processing steps in bold, where RF is Random Forest and signifies the classification process and Masking indicates the rule based classifier used to mask out certain MasterMap classes.
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f0010: Processing chain identifying the four main processing steps in bold, where RF is Random Forest and signifies the classification process and Masking indicates the rule based classifier used to mask out certain MasterMap classes.

Mentions: The processing chain for this study was divided up into four key processes (bold in Fig. 2), each of which is outlined in the following sections.


Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing.

O'Connell J, Bradter U, Benton TG - ISPRS J Photogramm Remote Sens (2015)

Processing chain identifying the four main processing steps in bold, where RF is Random Forest and signifies the classification process and Masking indicates the rule based classifier used to mask out certain MasterMap classes.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643754&req=5

f0010: Processing chain identifying the four main processing steps in bold, where RF is Random Forest and signifies the classification process and Masking indicates the rule based classifier used to mask out certain MasterMap classes.
Mentions: The processing chain for this study was divided up into four key processes (bold in Fig. 2), each of which is outlined in the following sections.

Bottom Line: A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909).We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m(2).The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

View Article: PubMed Central - PubMed

Affiliation: School of Biology, Faculty of Biological Sciences, University of Leeds, LS2 9JT, UK.

ABSTRACT

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m(2). The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

No MeSH data available.


Related in: MedlinePlus