Eintrag weiter verarbeiten
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition
Gespeichert in:
| Zeitschriftentitel: | Remote Sensing |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Remote Sensing, 12, 2020, 15, S. 2411 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Phan, Thanh Noi Kuch, Verena Lehnert, Lukas W. Phan, Thanh Noi Kuch, Verena Lehnert, Lukas W. |
|---|---|
| author |
Phan, Thanh Noi Kuch, Verena Lehnert, Lukas W. |
| spellingShingle |
Phan, Thanh Noi Kuch, Verena Lehnert, Lukas W. Remote Sensing Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition General Earth and Planetary Sciences |
| author_sort |
phan, thanh noi |
| spelling |
Phan, Thanh Noi Kuch, Verena Lehnert, Lukas W. 2072-4292 MDPI AG General Earth and Planetary Sciences http://dx.doi.org/10.3390/rs12152411 <jats:p>Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.</jats:p> Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition Remote Sensing |
| doi_str_mv |
10.3390/rs12152411 |
| facet_avail |
Online Free |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMzM5MC9yczEyMTUyNDEx |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMzM5MC9yczEyMTUyNDEx |
| institution |
DE-Zi4 DE-Gla1 DE-15 DE-Pl11 DE-Rs1 DE-14 DE-105 DE-Ch1 DE-L229 DE-D275 DE-Bn3 DE-Brt1 DE-Zwi2 DE-D161 |
| imprint |
MDPI AG, 2020 |
| imprint_str_mv |
MDPI AG, 2020 |
| issn |
2072-4292 |
| issn_str_mv |
2072-4292 |
| language |
English |
| mega_collection |
MDPI AG (CrossRef) |
| match_str |
phan2020landcoverclassificationusinggoogleearthengineandrandomforestclassifiertheroleofimagecomposition |
| publishDateSort |
2020 |
| publisher |
MDPI AG |
| recordtype |
ai |
| record_format |
ai |
| series |
Remote Sensing |
| source_id |
49 |
| title |
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_unstemmed |
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_full |
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_fullStr |
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_full_unstemmed |
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_short |
Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_sort |
land cover classification using google earth engine and random forest classifier—the role of image composition |
| topic |
General Earth and Planetary Sciences |
| url |
http://dx.doi.org/10.3390/rs12152411 |
| publishDate |
2020 |
| physical |
2411 |
| description |
<jats:p>Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.</jats:p> |
| container_issue |
15 |
| container_start_page |
0 |
| container_title |
Remote Sensing |
| container_volume |
12 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792346845328965642 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T17:45:52.402Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Land+Cover+Classification+using+Google+Earth+Engine+and+Random+Forest+Classifier%E2%80%94The+Role+of+Image+Composition&rft.date=2020-07-27&genre=article&issn=2072-4292&volume=12&issue=15&pages=2411&jtitle=Remote+Sensing&atitle=Land+Cover+Classification+using+Google+Earth+Engine+and+Random+Forest+Classifier%E2%80%94The+Role+of+Image+Composition&aulast=Lehnert&aufirst=Lukas+W.&rft_id=info%3Adoi%2F10.3390%2Frs12152411&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792346845328965642 |
| author | Phan, Thanh Noi, Kuch, Verena, Lehnert, Lukas W. |
| author_facet | Phan, Thanh Noi, Kuch, Verena, Lehnert, Lukas W., Phan, Thanh Noi, Kuch, Verena, Lehnert, Lukas W. |
| author_sort | phan, thanh noi |
| container_issue | 15 |
| container_start_page | 0 |
| container_title | Remote Sensing |
| container_volume | 12 |
| description | <jats:p>Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.</jats:p> |
| doi_str_mv | 10.3390/rs12152411 |
| facet_avail | Online, Free |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMzM5MC9yczEyMTUyNDEx |
| imprint | MDPI AG, 2020 |
| imprint_str_mv | MDPI AG, 2020 |
| institution | DE-Zi4, DE-Gla1, DE-15, DE-Pl11, DE-Rs1, DE-14, DE-105, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161 |
| issn | 2072-4292 |
| issn_str_mv | 2072-4292 |
| language | English |
| last_indexed | 2024-03-01T17:45:52.402Z |
| match_str | phan2020landcoverclassificationusinggoogleearthengineandrandomforestclassifiertheroleofimagecomposition |
| mega_collection | MDPI AG (CrossRef) |
| physical | 2411 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| publisher | MDPI AG |
| record_format | ai |
| recordtype | ai |
| series | Remote Sensing |
| source_id | 49 |
| spelling | Phan, Thanh Noi Kuch, Verena Lehnert, Lukas W. 2072-4292 MDPI AG General Earth and Planetary Sciences http://dx.doi.org/10.3390/rs12152411 <jats:p>Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.</jats:p> Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition Remote Sensing |
| spellingShingle | Phan, Thanh Noi, Kuch, Verena, Lehnert, Lukas W., Remote Sensing, Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition, General Earth and Planetary Sciences |
| title | Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_full | Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_fullStr | Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_full_unstemmed | Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_short | Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| title_sort | land cover classification using google earth engine and random forest classifier—the role of image composition |
| title_unstemmed | Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition |
| topic | General Earth and Planetary Sciences |
| url | http://dx.doi.org/10.3390/rs12152411 |