Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine
"> Figure 1
<p>The workflow of the tile-based model used in this study for mapping highly heterogeneous land cover in Madagascar. One tile refers to an extent of 1° × 1°.</p> "> Figure 2
<p>Schematic representation of the hexagon random sampling process in this study. Four steps: (<b>a</b>) generation of hexagons covering the whole study area; (<b>b</b>) visual interpretation of each hexagon overlaied on high-resolution Google Earth imagery; (<b>c</b>) selection of the location of land cover types; and (<b>d</b>) obtention of the sample set of the study area.</p> "> Figure 3
<p>Overview of the 10-m circa 2018 land cover map of Madagascar (i.e., the MDG LC-10 map) derived from the Sentiel-2 dataset. (<b>a</b>) The proportions of the eight major land cover classes over the entire island. The zoomed in windows show the details ranging from the landscape view (<b>b</b>), the urban structure (<b>c</b>), and the fine grain land cover patterns (<b>d</b>).</p> "> Figure 4
<p>Visual comparison of three zoomed areas (<b>a</b>–<b>c</b>) among the MDG LC-10 map, two high resolution land cover maps (i.e., the CCI Africa LC-20 map and the FROM-GLC10 map) and very high resolution imageries from Google Earth.</p> "> Figure 5
<p>Statistical comparison of producer accuracies (PAs) of the available high-resolution land cover maps for Madagascar and the map produced in this study.</p> "> Figure 6
<p>Examples of comparison of the classification performance using our proposed method (i.e., the tile-based model) (<b>c</b>,<b>f</b>) in this study and the conventional method (i.e., the overall model) (<b>b</b>,<b>e</b>). (<b>a</b>,<b>d</b>) are zoomed high-resolution images from Google Earth. Region 1 exhibits an improvement in the misclassification between cropland and wetland. Region 2 shows that the issue of impervious areas misclassification is improved.</p> "> Figure 7
<p>Statistical comparison of producer accuracies (PAs) for the proposed method (i.e., the one tile-based model is applied to one tile) and the conventional method (i.e., one model is applied to the entire study area). The blue and orange bars represent the PAs of eight land cover classes, and the green bars indicate that the differences in PAs.</p> "> Figure 8
<p>Examples of comparisons of the classification performances using Sentinel-2 images with cloud cover below 10% (i.e., n = 4900 images, <b>b</b>,<b>e</b>) and using all available Sentinel-2 images (i.e., n = 11,083 images, <b>c</b>,<b>f</b>) over the entire island. (<b>a</b>,<b>d</b>) were derived from high-resolution Google earth imagery.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sampling Strategy and Classification Scheme
2.2. Image Processing and Feature Collection
2.3. Classification and Accuracy Assessment
2.4. Comparison Analysis Among Products and Methods
3. Results and Discussion
3.1. Ten-Meter Circa 2018 LC Map of Madagascar
3.2. Comparisons Among Google Earth Images, Two Available High-Resolution Land Cover Maps of Madagascar and the MDG LC-10 Land Cover Map
3.3. Comparisons of the Overall Model vs. the Tile-Based Model
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Class | Description |
---|---|
Cropland | Areas characterized by clear traits of intensive human activity. This varies a lot from bare fields, seeding, and crop growing to harvesting. They can be easily identified if edges or textures are visible with sufficiently large land parcels. Fruit trees are classified as forests. Bare fields are classified as bare land. Pasture could be transitional from croplands to natural grasslands. |
Forest | Areas where tree cover percentage classification to >15%; limits tree height classification to >3 m. |
Grassland | Grassland for grazing and natural grassland are identifiable. Herbaceous cover percentage classification to >15%. |
Shrubland | Areas characterized by a texture finer than tree canopies but coarser than grasslands, height between 5 and 0.3 m, and cover percentage classification to >15%. |
Wetland | Areas dominated by natural and semi-natural aquatic or regularly flooded vegetation. |
Waterbody | Areas dominated by natural waterbodies/artificial waterbodies. |
Impervious | Areas dominated by artificial surfaces and associated area(s), primarily based on artificial cover such as asphalt, concrete, sand and stone, brick, glass, and other cover materials. |
Bare land | Areas where vegetation is hardly observable but dominated by exposed soil, sand, gravel, and rock backgrounds. |
Reference Class | Mapped Class | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Cropland | Forest | Grassland | Shrubland | Wetland | Waterbody | Impervious | Bare Land | Total | PA(%) | |
Cropland | 183 | 1 | 20 | 14 | 1 | 0 | 1 | 0 | 220 | 83.2 |
Forest | 0 | 206 | 6 | 11 | 0 | 0 | 0 | 0 | 223 | 92.4 |
Grassland | 4 | 3 | 235 | 1 | 1 | 0 | 0 | 0 | 244 | 96.3 |
Shrubland | 2 | 15 | 12 | 114 | 0 | 0 | 0 | 0 | 143 | 79.7 |
Wetland | 5 | 0 | 2 | 0 | 38 | 2 | 0 | 0 | 47 | 80.9 |
Waterbody | 3 | 0 | 0 | 0 | 0 | 89 | 0 | 3 | 95 | 93.7 |
Impervious | 0 | 0 | 12 | 2 | 0 | 0 | 158 | 2 | 174 | 90.8 |
Bare land | 3 | 0 | 6 | 1 | 0 | 4 | 1 | 117 | 132 | 88.6 |
Total | 200 | 225 | 293 | 143 | 40 | 95 | 160 | 122 | 1278 | |
UA(%) | 91.5 | 91.6 | 80.2 | 79.7 | 95.0 | 93.7 | 98.8 | 95.9 | ||
OA(%): 89.2; | Kappa: 0.87. |
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Zhang, M.; Huang, H.; Li, Z.; Hackman, K.O.; Liu, C.; Andriamiarisoa, R.L.; Ny Aina Nomenjanahary Raherivelo, T.; Li, Y.; Gong, P. Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine. Remote Sens. 2020, 12, 3663. https://doi.org/10.3390/rs12213663
Zhang M, Huang H, Li Z, Hackman KO, Liu C, Andriamiarisoa RL, Ny Aina Nomenjanahary Raherivelo T, Li Y, Gong P. Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine. Remote Sensing. 2020; 12(21):3663. https://doi.org/10.3390/rs12213663
Chicago/Turabian StyleZhang, Meinan, Huabing Huang, Zhichao Li, Kwame Oppong Hackman, Chong Liu, Roger Lala Andriamiarisoa, Tahiry Ny Aina Nomenjanahary Raherivelo, Yanxia Li, and Peng Gong. 2020. "Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine" Remote Sensing 12, no. 21: 3663. https://doi.org/10.3390/rs12213663