Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine
Abstract
:1. Introduction
2. Study Area and Dataset
2.1. Study Area
2.2. Multi-Source Geodata for Land Cover Mapping
- Sentinel-2 Satellite ImagerySentinel-2 consists of 13 spectral bands, which includes four bands with 10 m spatial resolution, six bands with 20 m spatial resolution, and three bands with 60 m spatial resolution, as shown in Table 1. In the GEE catalog, Sentinel-2 Multispectral Instrument (MSI), Level-1C data [21] is the standard of the Sentinel-2 archive, and represents the Top Of Atmosphere (TOA) reflectance. For this study, Bands 1, 9, and 10 are excluded, as they contain only information about the atmosphere rather than land surface data, and only have a coarse spatial resolution.
- Landsat-8 Satellite ImageryLandsat-8 [22] consists of two science instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI collects data from eight spectral bands at 30 m and one panchromatic band at 15 m. TIRS conducts thermal imaging from two bands at 100 m, as shown in Table 1. In this research, Landsat-8 Surface Reflectance Tier 1 is chosen as the input data. This data is orthorectified and atmospherically corrected surface reflectance, which excludes Band 8 and 9.
- Night Time Light (NTL) DataThe Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) [23], sourced from the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite, is able to provide multi-temporal NTL data, which allows near-real-time monitoring because of its high repeat frequency. For this study, we use VIIRS DNB Composites Version 1 data, which is the temporal average radiance on a monthly basis at 742 m spatial resolution.
- Global Human Settlement Layer (GHSL)Human settlement information is derived for the four different epochs 1975, 1990, 2000, and 2014 in GHSL [24]. From the GHSL data (Built-Up Grid 1975-1990-2000-2015 (P2016)) available in the GEE catalog, the “built” class identifies the presence of built-up areas at 38 m spatial resolution.
- Shuttle Radar Topography Mission (SRTM)The SRTM V3 product (SRTM Plus) [25] is exploited in our research, which is an enhanced version of the original digital elevation model (DEM) at 30 m spatial resolution. The slope in degrees from the DEM is adopted in this research, which is the local gradient within the four-connected neighbors of each pixel.
- MODIS Land Surface Temperature (LST)MODIS is an imaging sensor on both the Terra and Aqua satellites, which aim to acquire global dynamics of the Earth. LST can be derived from the radiance at Band 31 and 32 that is measured with the Terra satellite. In this study, we chose MYD11A2 V6 [26] available in GEE as MODIS LST. MYD11A2 V6 is a simple average of the data collected within that eight-day period, which can provide both day and night LST with a 1 km spatial resolution.
3. A Framework for Land Cover Mapping from Multi-Source Data
3.1. Overview of the Proposed Framework
3.2. African Land Cover Map Generation
3.2.1. Preprocessing
3.2.2. Sampling
3.2.3. Random Forest Classifier
3.2.4. Accuracy Assessment
3.3. Selection of Input Configurations
3.4. Transferability Evaluation
- City-wise holdout cross validation: The reference samples from six training cities are used for training models and samples from the one remaining city are used for testing models. Since there are seven cities, this classification procedure was performed seven times. In this case, the information for training is independent of that used for testing, which facilitates the assessment of transferability of the trained models for each land cover class across different areas.
- Sample-wise cross validation: All training samples are randomly split into five folds, where four folds of class samples that include data from all the training cities are fed to the classifier and the remaining fold of class samples is used for testing. This experiment is used for comparison and provides an upper bound for mapping accuracy.
4. Results
4.1. Comparative Classification Accuracy of Different Input Configurations
4.2. Transferability of Trained Models
4.3. Land Cover Map of the Whole African Continent
5. Discussion
5.1. Input Configurations Analysis
5.2. Transferability Analysis of Trained Models for Each Land Cover Class across Different Cites
5.3. Implication of Our African Land Cover Map
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Sentinel-2 | Landsat-8 | |||
---|---|---|---|---|
Band | Spectral Region | Resolution (M) | Spectral Region | Resolution (M) |
Band 1 | Coastal Aerosol | 60 | Coastal Aerosol | 30 |
Band 2 | Blue | 10 | Blue | 30 |
Band 3 | Green | 10 | Green | 30 |
Band 4 | Red | 10 | Red | 30 |
Band 5 | Vegetation red edge1 | 20 | Near Infrared (NIR) | 30 |
Band 6 | Vegetation red edge2 | 20 | Short Wavelength Infrared 1 (SWIR1) | 30 |
Band 7 | Vegetation red edge3 | 20 | Short Wavelength Infrared 2 (SWIR2) | 30 |
Band 8 | NIR | 10 | Panchromatic | 15 |
Band 8A | Narrow Near Infrared | 20 | ||
Band 9 | Water vapour | 60 | Cirrus | 30 |
Band 10 | Cirrus | 60 | Thermal Infrared 1 (TIR1) | 100 |
Band 11 | SWIR1 | 20 | Thermal Infrared 2 (TIR2) | 100 |
Band 12 | SWIR1 | 20 |
Spectral Indices | Equation |
---|---|
NDBI | |
NDVI | |
MNDWI |
Type | City | Country | Climate (Köppen Climate Classification) |
---|---|---|---|
Training | Cairo | Egypt | Hot desert climate (BWh) |
Training | Cape Town | South Africa | Warm-summer Mediterranean climate (Csb) |
Training | Nairobi | Kenya | Temperate oceanic climate (Cfb), Subtropical highland climate (Cwb) |
Training | Lagos | Nigeria | Tropical wet climate (Aw) |
Training | Niamey | Niger | Hot semi-arid climate (BSh) |
Training | Lusaka | Zambia | Monsoon-influenced humid subtropical climate (Cwa) |
Training | Casablanca | Morocco | Hot-summer Mediterranean climate (Csa) |
Evaluation | Addis Ababa | Ethiopia | Subtropical highland climate (Cwb) |
Evaluation | Pretoria | South Africa | Monsoon-influenced humid subtropical climate (Cwa) |
Feature Combination | OA | Kappa |
---|---|---|
Sentinel-2 (spectral band) | 0.7551 | 0.6896 |
Sentinel-2 (spectral band + indices) | 0.7616 | 0.6981 |
Sentinel-2 (spectral band + indices) + Landsat-8 (spectral band + indices) | 0.7619 | 0.6986 |
Sentinel-2 (spectral band + indices) + Landsat-8 (spectral band + indices) + NTL | 0.7986 | 0.7452 |
Sentinel-2 (spectral band + indices) + Landsat-8 (spectral band + indices) + NTL + GHSL | 0.8043 | 0.7524 |
Sentinel-2 (spectral band + indices) + Landsat-8 (spectral band + indices) + NTL + GHSL + LST | 0.8100 | 0.7596 |
Sentinel-2 (spectral band + indices) + Landsat-8 (spectral band + indices) + NTL + GHSL + LST + SRTM | 0.8105 | 0.7602 |
Experiments | Training Set | Test Set | OA | Kappa |
---|---|---|---|---|
Fixed validation | Cape Town, Casablanca, Lagos, Lusaka, Nairobi, Niamey, and Cairo | Addis Ababa and Pretoria | 0.8105 | 0.7602 |
City-wise holdout cross validation | Casablanca, Lagos, Lusaka, Nairobi, Niamey, and Cairo | Cape Town | 0.8140 | 0.7675 |
Cape Town, Lagos, Lusaka, Nairobi, Niamey, and Cairo | Casablanca | 0.5225 | 0.4031 | |
Cape Town, Casablanca, Lusaka, Nairobi, Niamey, and Cairo | Lagos | 0.6075 | 0.5094 | |
Cape Town, Casablanca, Lagos, Nairobi, Niamey, and Cairo | Lusaka | 0.5660 | 0.4575 | |
Cape Town, Casablanca, Lagos, Lusaka, Niamey, and Cairo | Nairobi | 0.7205 | 0.6506 | |
Cape Town, Casablanca, Lagos, Lusaka, Nairobi, and Cairo | Niamey | 0.4892 | 0.3529 | |
Cape Town, Casablanca, Lagos, Lusaka, Nairobi, and Niamey | Cairo | 0.8000 | 0.7500 | |
Sample-wise cross validation | 4/5 Training samples | 1/5 Training samples | 0.9334 | 0.9168 |
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Li, Q.; Qiu, C.; Ma, L.; Schmitt, M.; Zhu, X.X. Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine. Remote Sens. 2020, 12, 602. https://doi.org/10.3390/rs12040602
Li Q, Qiu C, Ma L, Schmitt M, Zhu XX. Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine. Remote Sensing. 2020; 12(4):602. https://doi.org/10.3390/rs12040602
Chicago/Turabian StyleLi, Qingyu, Chunping Qiu, Lei Ma, Michael Schmitt, and Xiao Xiang Zhu. 2020. "Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine" Remote Sensing 12, no. 4: 602. https://doi.org/10.3390/rs12040602