Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models
<p>Location map of the six provinces in East China as the study area.</p> "> Figure 2
<p>The flowchart of Land cover mapping in East China and experiment for WRF Simulation.</p> "> Figure 3
<p>Landsat scenes utilized in this study, including row and path IDs.</p> "> Figure 4
<p>Some typical data used in Random Forest. (<b>a</b>) 30 m DEM dataset; (<b>b</b>) high-resolution imagery (0.6 m); with specific identification on the imagery (<b>b</b>) as follows: (<b>c</b>) Grasslands and Forest lands (marked by green dot); (<b>d</b>) Water bodies (marked by blue dot); (<b>e</b>) Urban (marked by red dot); (<b>f</b>) Croplands and Plastic greenhouses (marked by orange dot).</p> "> Figure 5
<p>The distribution of the training dataset and the testing dataset.</p> "> Figure 6
<p>30 m Land cover map product for East China.</p> "> Figure 7
<p>Distribution of major land cover types across six study provinces.</p> "> Figure 8
<p>Comparison of landcover types across different datasets.</p> "> Figure 9
<p>Difference between simulated and observed 2 m temperatures. (<b>a</b>–<b>c</b>) at 16:00 on 13 August 2020, and (<b>d</b>–<b>f</b>) at 02:00 on 14 August 2020 (shading, units: °C). (<b>a</b>,<b>d</b>) Original surface data, (<b>b</b>,<b>e</b>) New surface data, (<b>c</b>,<b>f</b>) New surface data + mosaic.</p> "> Figure 10
<p>Time series of (<b>a</b>) the error and (<b>b</b>) the root mean square error of 2 m temperature.</p> ">
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Data
3.2. Land Cover Classification Scheme for the East China Region
3.3. Image Classification
3.4. WRF Modelling Using New Land Cover Data
4. Results
4.1. Land Cover Distribution
4.2. Accuracy Assessment
4.3. Comparisons with Other Products
4.4. WRF Model Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Provinces | Landsat Image Acquisition Time (Month) |
---|---|
Anhui | March 2016, April 2018 |
Fujian | February 2017, October 2017, March 2018 |
Jiangsu | August 2015, October 2015, March 2016, May 2017 |
Jiangxi | October 2015, March 2016, September 2016, February 2018, April 2018 |
Shandong | October 2015, May 2017, September 2017, April 2018 |
Shanghai | August 2015 |
Zhejiang | August 2015, March 2016, February 2017, November 2017, March 2018 |
Land Cover (Code) | Definition |
---|---|
Urban and built-up lands (1) | Any natural or artificial surface that prevents water from directly penetrating into the soil, mainly consisting of transportation land, building land, industrial and mining land, and rooftops within urban areas. |
Croplands (2) | Land used for planting crops, including paddy fields, irrigated dry land, rainfed dry land, vegetable plots, pasture lands mainly for crops interspersed with fruit trees and other economic trees, as well as tea plantations, coffee plantations, and other economic shrub planting areas. |
Grasslands (3) | Covered by herbaceous vegetation, with forest and shrub coverage less than 10%, including grasslands, meadows, urban grasslands, etc. |
Forest lands (4) | Land covered with trees and a canopy cover of more than 30%, including deciduous broadleaf forest, evergreen broadleaf forest, deciduous, coniferous forest, evergreen coniferous forest, mixed forest, as well as sparse forest land with a canopy cover of 10% to 30%. |
Wetlands (5) | A transitional zone between land and water bodies, often or perennially covered with shallow standing water (freshwater, brackish water, or saltwater) or overly moist soil, predominantly growing hydrophytic or hygrophytic herbaceous or woody plants. Includes inland marshes, lake marshes, river floodplain wetlands, forest/shrub wetlands, peat bogs, mangroves, salt marshes, etc. |
Water bodies (6) | Areas covered by liquid water, including rivers, lakes, reservoirs, ponds, etc. |
Bare lands (7) | Natural covered land with vegetation cover less than 10%, including bare soil, bare rocks, saline-alkali soil, desert, sandy land, gravel land, etc. |
Plastic greenhouses (8) | Agricultural land used for vegetable cultivation utilizing facilities such as greenhouses and sheds. |
Shrublands (9) | Land covered with shrubs and a shrub coverage greater than 30%, including mountain shrubland, deciduous and evergreen shrubland, as well as desert shrubland with coverage greater than 10% in desert areas. |
Cloud (unclassified) (10) | Areas covered by clouds in satellite imagery, not classified into any specific land cover type due to obscuration. |
Class | Percentage of Different Land Cover Types (%) | ||||||
---|---|---|---|---|---|---|---|
Anhui | Fujian | Jiangsu | Jiangxi | Shandong | Shanghai | Zhejiang | |
Urban | 8.29 | 4.92 | 17.94 | 5.28 | 21.28 | 42.19 1 | 9.96 |
Croplands | 43.31 1 | 10.28 | 57.06 1 | 22.42 | 38.11 1 | 22.95 | 18.69 |
Grasslands | 3.79 | 2.70 | 2.49 | 3.97 | 6.85 | 6.27 | 0.30 |
Forest lands | 33.03 | 77.72 1 | 5.65 | 60.69 1 | 9.58 | 11.38 | 66.82 1 |
Wetlands | 1.17 | 0.46 | 0.66 | 0.05 | 1.03 | 0.30 | 0.03 |
Water bodies | 6.97 | 1.41 | 12.34 | 4.95 | 3.85 | 5.40 | 2.71 |
Bare lands | 3.00 | 2.45 | 2.53 | 2.57 | 14.74 | 11.44 | 1.48 |
Plastic greenhouses | 0.44 | 0.05 | 1.31 | 0.06 | 4.23 | 0.00 | 0.01 |
Shrublands | 0.00 | 0.01 | 0.00 | 0.00 | 0.34 | 0.06 | 0.00 |
Class | Urban | Cropland | Grassland | Forest | Wetland | Water | Bare Land | Plastic Greenhouses | Shrubland | User Acc. |
---|---|---|---|---|---|---|---|---|---|---|
Urban | 7308 1 | 246 | 6 | 122 | 88 | 41 | 156 | 27 | 40 | 0.91 |
Croplands | 67 | 2555 1 | 73 | 8 | 3 | 265 | 49 | 0 | 0 | 0.85 |
Grasslands | 46 | 439 | 2300 1 | 32 | 3 | 5 | 238 | 49 | 205 | 0.69 |
Forest lands | 30 | 10 | 81 | 3259 1 | 0 | 3 | 26 | 0 | 237 | 0.89 |
Wetlands | 318 | 77 | 566 | 60 | 3679 1 | 211 | 129 | 20 | 2 | 0.73 |
Water bodies | 46 | 48 | 5 | 11 | 26 | 6607 1 | 0 | 2 | 0 | 0.98 |
Bare lands | 351 | 217 | 92 | 110 | 37 | 14 | 4053 1 | 12 | 35 | 0.82 |
Plastic greenhouses | 124 | 204 | 15 | 73 | 0 | 2 | 5 | 1547 1 | 0 | 0.79 |
Shrublands | 584 | 51 | 45 | 585 | 0 | 0 | 24 | 18 | 2045 1 | 0.61 |
Producer Acc. | 0.82 | 0.66 | 0.72 | 0.77 | 0.96 | 0.92 | 0.87 | 0.92 | 0.80 |
Class | TP 1 | FN 1 | FP 1 | TN 1 | Precision 1 | Recall | TNR 1 | F1 Score |
---|---|---|---|---|---|---|---|---|
Urban | 7308 | 726 | 1566 | 30,467 | 0.82 | 0.91 | 0.95 | 0.86 |
Croplands | 2555 | 465 | 1292 | 35,755 | 0.66 | 0.85 | 0.97 | 0.74 |
Grasslands | 2300 | 1017 | 883 | 35,867 | 0.72 | 0.69 | 0.98 | 0.71 |
Forest lands | 3259 | 387 | 1001 | 35,420 | 0.77 | 0.89 | 0.97 | 0.82 |
Wetlands | 3679 | 1383 | 157 | 34,848 | 0.96 | 0.73 | 1.00 | 0.83 |
Water bodies | 6607 | 138 | 541 | 32,781 | 0.92 | 0.98 | 0.98 | 0.95 |
Bare lands | 4053 | 868 | 627 | 34,519 | 0.87 | 0.82 | 0.98 | 0.84 |
Plastic greenhouses | 1547 | 423 | 128 | 37,969 | 0.92 | 0.79 | 1.00 | 0.85 |
Shrublands | 2045 | 1307 | 519 | 36,196 | 0.80 | 0.61 | 0.99 | 0.69 |
Products | Overall Accuracy (%) | Sensor | Classification Method | Resolution | Year | References |
---|---|---|---|---|---|---|
IGBP-DIS | 66.9 | AVHRR | Unsupervised Classification | 1 km | 1992–1993 | [34] |
UMD | 65.0 | AVHRR | Unsupervised/Decision Tree Classification | 1 km | 1992–1993 | [35] |
GLC 2000 | 68.6 | SPOT4 | Unsupervised Classification | 1 km | 1999–2000 | [36] |
GlobCover | 67.5 | MERSI | Supervised/Unsupervised Classification | 300 m | 2009 | [37] |
CCI-LC | 74.1 | MERIS, SPOT | Unsupervised Classification | 300 m | 2008–2012 | [23] |
MCD12Q1 | 74.8 | MODIS | Supervised Classification/Decision Tree/Neural Network | 500 m | 2013 | [38] |
GlobeLand30 | 80.0 | LANDSAT, HJ-1A/B | Pixel/Object-based and Knowledge Rule Classification | 30 m | 2010, 2020 | [39] |
Class | GlobeLand30 (2010) | MODIS (2013) | ModelLand30 (2015–2018) | |||
---|---|---|---|---|---|---|
Area (km2) | Proportion (%) | Area (km2) | Proportion (%) | Area (km2) | Proportion (%) | |
Urban | 58,330.4 | 7.32 | 20,539.5 | 2.73 | 90,918.9 | 11.42 |
Croplands | 384,160.9 | 48.24 | 386,304 | 51.31 | 248,857.7 | 31.25 |
Grasslands | 32,970.8 | 4.14 | 75,582 | 10.04 | 29,177.4 | 3.66 |
Forest lands | 279,741.1 | 35.13 | 244,495.5 | 32.47 | 332,869.8 | 41.79 |
Wetlands | 4710.3 | 0.59 | 7913.75 | 1.05 | 4617.9 | 0.58 |
Water bodies | 34,335.5 | 4.31 | 14,502 | 1.93 | 41,461.7 | 5.21 |
Bare lands | 676.8 | 0.08 | 2228.75 | 0.30 | 39,316.8 | 4.94 |
Plastic greenhouse | NA | NA | NA | NA | 8687.9 | 1.09 |
Shrublands | 725.7 | 0.09 | 1194.75 | 0.16 | 538.7 | 0.07 |
Experiments | Averaged Root Mean Square Error (°C) | Mean Error (°C) | Mean Absolute Error (°C) |
---|---|---|---|
Original surface data | 3.36 | −1.42 | 1.99 |
New surface data | 3.09 | −0.64 | 1.47 |
New surface data + mosaic | 3.01 | −0.57 | 1.36 |
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Ma, B.; Shao, Y.; Yang, H.; Lu, Y.; Gao, Y.; Wang, X.; Xie, Y.; Wang, X. Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models. Remote Sens. 2024, 16, 3759. https://doi.org/10.3390/rs16203759
Ma B, Shao Y, Yang H, Lu Y, Gao Y, Wang X, Xie Y, Wang X. Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models. Remote Sensing. 2024; 16(20):3759. https://doi.org/10.3390/rs16203759
Chicago/Turabian StyleMa, Bingxin, Yang Shao, Hequn Yang, Yiwen Lu, Yanqing Gao, Xinyao Wang, Ying Xie, and Xiaofeng Wang. 2024. "Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models" Remote Sensing 16, no. 20: 3759. https://doi.org/10.3390/rs16203759