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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

Plot of mean OOB error based on 50 repetitions over the cumulative number of variables used (a) and plot of variable importance measures based on 50 repetitions in descending order (b) for the model F1F2.
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f0025: Plot of mean OOB error based on 50 repetitions over the cumulative number of variables used (a) and plot of variable importance measures based on 50 repetitions in descending order (b) for the model F1F2.

Mentions: By plotting mean class and overall error based on 50 RF models, there was a clear trend of error stabilisation above 100 trees for all classification scenarios, validating the ntree parameter, as RF does not induce an over adjustment in the model above the convergence point (Breiman, 2001). Variable selection was not performed on the 4 class models (i.e. F1, H1) due to the lower number of variables produced at this level and considering that only the proportion of votes from this level were inputted into two of the 9 class models. Class specific variable importance also showed clear trends with Sparse, Shadow, Grass, Crop 1 and Crop 2 dominated by spectral and textural features and Scrub, Trees, Hedges and Margins dominated by spectral and geometric features (Table 1). The distinction between Grass and Scrub was predominantly driven by textural and geometric variables with Scrub having higher standard deviation in GLCM and lower area. For H1H2 and F1F2, the top ranked variable for 7 of the 9 classes was proportion of votes, with the 4 class vote correctly attributed to the 9 class category (i.e. Noncrop for Trees) in all cases. A trend of significant decrease in permutation importance for the first ten ranked variables was present for all three models (Fig. 5b). Variables from 20 and above had little influence on classification accuracy as mean OOB error was stabilised (Fig. 5a).


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)

Plot of mean OOB error based on 50 repetitions over the cumulative number of variables used (a) and plot of variable importance measures based on 50 repetitions in descending order (b) for the model F1F2.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0025: Plot of mean OOB error based on 50 repetitions over the cumulative number of variables used (a) and plot of variable importance measures based on 50 repetitions in descending order (b) for the model F1F2.
Mentions: By plotting mean class and overall error based on 50 RF models, there was a clear trend of error stabilisation above 100 trees for all classification scenarios, validating the ntree parameter, as RF does not induce an over adjustment in the model above the convergence point (Breiman, 2001). Variable selection was not performed on the 4 class models (i.e. F1, H1) due to the lower number of variables produced at this level and considering that only the proportion of votes from this level were inputted into two of the 9 class models. Class specific variable importance also showed clear trends with Sparse, Shadow, Grass, Crop 1 and Crop 2 dominated by spectral and textural features and Scrub, Trees, Hedges and Margins dominated by spectral and geometric features (Table 1). The distinction between Grass and Scrub was predominantly driven by textural and geometric variables with Scrub having higher standard deviation in GLCM and lower area. For H1H2 and F1F2, the top ranked variable for 7 of the 9 classes was proportion of votes, with the 4 class vote correctly attributed to the 9 class category (i.e. Noncrop for Trees) in all cases. A trend of significant decrease in permutation importance for the first ten ranked variables was present for all three models (Fig. 5b). Variables from 20 and above had little influence on classification accuracy as mean OOB error was stabilised (Fig. 5a).

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