The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania
Abstract
:1. Introduction
1.1. Background to the Study—The REDD+ Initiative
1.2. Use of Remote Sensing Data to Map Above Ground Biomass
1.3. The Current Study
2. Materials and Methods
2.1. Study Area
2.2. NAFORMA Field Data
2.3. RapidEye Satellite Data
2.4. RapidEye Preprosessing—Radiometric Calibration
- ρλ = TOA reflectance for band λ
- Lλ = Radiance for band λ
- θSZ = Local solar zenith angle
- d = (1 − 0.01672 × cos (0.01745 × (0.9856 × (Julian Day Image − 4)))
2.5. RapidEye Preprosessing—Image Segmentation to Obtain The MMU
2.6. Cloud and Cloud Shadow Masking
2.7. Extraction of Remote Sensing Parameters for Developing Models for AGB
2.8. Reviewing the Field Plot Data with Respect to the Rapideye Data
- -
- The field data (15 m circle plots—circa 700 m2) cover 27 RapidEye pixels. However, the precision of the plot geolocation taken in the field (Garmin C60) had limited accuracy (+/−7 m). There are also known problems in changing between the Arc60 datum of topographic maps of East Africa used in the field survey, and that of the satellite reference datum, UTM-WGS84 [92];
- -
- there were a number of discrepancies in the geolocation of various RapidEye scenes, which after the review, were addressed by shifting images to a reference data base of Landsat scenes. Over 50 sample site images were shifted by up to 12 pixels, 30 in the X direction and 41 in the Y direction;
- -
- temporal differences; the field data collection took place over three years; satellite data were only available for one of these years. On average, the difference between field data collection and satellite image acquisition was 10 months (see Supplementary Materials Figure S1), with most of the field data collected after the image acquisition;
- -
- data collected in the field gave no systematic estimation of the respective cover of trees, shrubs, or other land cover classes, despite being foreseen in the original protocol. On a number of occasions, when given, the canopy cover did not correspond to that seen from the satellite image—perhaps due to problems of geolocation between the data sets, or differences in the time between the field visit and the image acquisition. Also, canopy density is known to be difficult to measure with accuracy from the ground [8];
- -
- the land cover classification given to the field teams was not adapted to providing adequate field data for calibrating remote sensing data. The vast majority of plots were classified as ‘woodland’, without further elaboration;
- -
- even if the land cover has not changed throughout the year, its condition does, especially in the tropics, predominantly due to seasonality. It may be in a lush green phase, drying out, exceptionally dry, burnt, or flooded. All these present different spectral signatures for the same land cover. We removed 33 plots that were burnt, 9 that were flooded, and 13 that had cloud or cloud shadow; and
- -
- finally, we found that the field plot was not always representative of the 1 ha image object on the remote sensing data.
2.9. Models for Predicting Above Ground Biomass
2.10. Interpolating the Results to the National Level
2.11. The Relative Efficiency to Measure the Improvement in Precision Brought by the Remote Sensing Data
3. Results
3.1. Model Results
3.2. Analysis at the National Level and by Ecoregion
3.3. Relative Efficiency
4. Discussion
4.1. Practical and Operational Consderations to Imprvve AGB Estimates from RapidEye Data
4.1.1. Co-Location of the Data Sets
4.1.2. Data Collected
4.1.3. Data Cleaning
4.1.4. Spectral Signatures
4.1.5. Ancillary Data
4.2. Reviewing the Results
4.2.1. Estimation by Ecozone
4.2.2. Relative Efficiency
4.2.3. Model Results
- (i)
- The limited sensitivity of satellite sensors to variations in canopy height and tree diameter in dense forests. Specifically, optical sensors’ radiometers (such as RapidEye) tend to saturate at high biomass in dense forest where there is a weak reflectance-biomass relationship, e.g., [103]. For this reason, the combined use optical data in combination with SAR and LiDAR data would improve results as shown in [104,105];
- (ii)
- we were limited to the use of single date imagery with most of the images acquired in the dry season when many seasonal forests have lost their leaves and are spectrally similar to low biomass grasslands; and
- (iii)
- RF is trained on a dataset where high biomass values represent the tail of the frequency distribution and therefore its performance decreases as the biomass increases.
5. Conclusions
Supplementary Materials
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Vegetation | Area (ha) | AGB (t/ha) | Share of Carbon Stock (%) |
---|---|---|---|
Forest | 3,364,457 | 59.5 | 11.5 |
Woodland | 44,726,246 | 27.7 | 73.5 |
Bushland | 6,445,471 | 11 | 4.4 |
Grassland | 8,242,245 | 2.9 | 2.3 |
Cultivated land | 22,248,092 | 5.9 | 8 |
Water | 1,162,552 | 0.3 |
Channel | Spectral Band Name | Spectral Range (nm) |
---|---|---|
1 | Blue | 440–510 |
2 | Green | 520–590 |
3 | Red | 630–685 |
4 | Red Edge | 690–730 |
5 | Near infra-red | 760–850 |
Single Band Reflectance of Objects | Equation | Acronym |
Mean bands 1 to 5 | BLUE, GREEN, RED, REdge, NIR | R1–R5 |
Standard deviation bands 1 to 5 | SD1–SD5 | |
Simple Ratios | ||
Ratio of mean to standard deviation for all bands | [Mean band #]/[SD band #] | e.g., R1SD1 |
Standard deviation. ratios | [SD band #]/[SD band #] | e.g., SD1SD2 |
Red Vegetation Index | NIR/RED | RVI |
Green Vegetation Index | NIR/GREEN | GVI |
Green Red Vegetation Index | GREEN/RED | GRVI |
Other Indices | ||
Normalised Difference Vegetation Index [87] | (NIR − RED)/(NIR + RED) | NDVI |
Enhanced Vegetation Index [88] | 2.5 × ((NIR − RED))/((NIR + 6 × RED − 7.5 × BLUE + 1)) | EVI |
Soil Adjusted Vegetation Index [89] | (NIR − RED)/((NIR + RED + L)) × (1 + L) | SAVI |
Shadow Index [85] | ((1 − BLUE) × (1 − GREEN) × (1 − RED))0.33 | Shadow_Index |
Modified Transverse Vegetation Index [86] | (1.5 × (1.2 × ([R5] − [R2]) − 2.5 × ([R3] − [R2])))/((2 × [R5] + 1)^2 − (6 × [R5] − 5 × ([R3])^0.5) − 0.5)^0.5 | MTVI |
Modified Chlorophyll Absorption Reflectance Index [90] | (([NIR] − [RED]) − 0.2 × ([NIR] − [GREEN])) × ([NIR]/[RED]) | MCARI |
Bare Soil Index | ((([NIR] + [RED]) − ([REdge] + [BLUE]))/(([NIR] + [RED]) + ([REdge] + [BLUE]))) + 1 | Bare_soil |
Shadow to Soil Ratio | ShadowIndex/Bare_Soil | Shadsoil |
GLCM Texture Features | (1) In all directions, 0°, 45°, 90°, 135° | |
(2) For combined and for individual bands | ||
Homogeneity | See Haralick [91] | GCLM_Hom |
Contrast | GCLM_Con | |
Dissimilarity | GCLM_Diss | |
Angular 2nd moment | GCLM_Ang2 | |
Entropy | GCLM_Entro | |
Mean | GCLM_Mean | |
Variance | GCLM_StdDev | |
Correlation | GCLM_Corr | |
Object Proportions | ||
Bare Soil | Percentage classed as bare soil | Rel_soil |
Shadow | Percentage classed as woody | Rel_shad |
Model | RMSE | MAE | R2 | relRMSE |
---|---|---|---|---|
Generalized Exponential Regression | 44.55 | 36.44 | 0.32 | 21% |
Generalized Linear Regression | 41.81 | 35.07 | 0.39 | 20% |
Random Forest | 30.00 | 21.64 | 0.69 | 14% |
SVM | 39.08 | 29.76 | 0.42 | 19% |
Afromontane | Zanzibar-Inhambane | Somalia-Masai | Zambezian | Lake Victoria | |
---|---|---|---|---|---|
Area (km2) | 43,500 | 77,100 | 238,965 | 502,052 | 40,300 |
Count * | 5 | 6 | 19 | 41 | 4 |
Minimum | 17.3 | 15.1 | 9.8 | 11.6 | 17.8 |
Maximum | 27.7 | 30.4 | 22.4 | 34.3 | 25.9 |
Mean | 20.9 | 21.5 | 16.7 | 25.2 | 22.3 |
SD | 4.9 | 5.6 | 4.1 | 4.6 | 3.3 |
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Hojas Gascón, L.; Ceccherini, G.; García Haro, F.J.; Avitabile, V.; Eva, H. The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania. Forests 2019, 10, 107. https://doi.org/10.3390/f10020107
Hojas Gascón L, Ceccherini G, García Haro FJ, Avitabile V, Eva H. The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania. Forests. 2019; 10(2):107. https://doi.org/10.3390/f10020107
Chicago/Turabian StyleHojas Gascón, Lorena, Guido Ceccherini, Francisco Javier García Haro, Valerio Avitabile, and Hugh Eva. 2019. "The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania" Forests 10, no. 2: 107. https://doi.org/10.3390/f10020107