Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas
2.2. Data Acquisition and Treatment
2.2.1. Ground Survey Data
Forest Types | Biomass Function |
---|---|
Birch forest | YS = (H/(−0.7161 + 1.7316H))V |
YB = (H/(71.1504 + 2.3594H))V | |
YF = (D/(52.1765 + 31.5260D))V | |
YR = (D/(−7.7814 + 5.2684D))V | |
Korean pine forest | YS = (N/(369.6842 + 1.5593N))V |
YB = 0.1807D0.1196H−0.1086V | |
YF = 0.2835D−0.2246H−0.2737V | |
YR = H/(−7.4947 + 5.4H))V) | |
Larch forest | YS = 0.2638D0.6084H−0.3052V |
YB = 0.0893D0.5372H−0.5798V | |
YF = (D/(−0.320.565 + 69.0763D))V | |
YR = (D/(44.2954 + 1.182D))V | |
Populus forest | YS = 0.2472D0.134H−0.0461V |
YB = (D/(70.9902 + 8.7291D))V | |
YF = (D/(−71.0595 + 22.4834D))V | |
YR = (D/(−7.72 + 0.1178D))V | |
Pinus sylvestris forest | YS = 0.3063D0.2638H0.0044V |
YB = 0.0809D0.3271H−0.282V | |
YF = (D/(−71.0595 + 22.4834D))V | |
YR = (D/(−25.67 + 7.3893D))V | |
Mixed broad-leaved forest | YS = 0.5193D0.1529H−0.0981V |
YB = (D/(79.2051 + 1.2886D))V | |
YF = (D/(−84.3635 + 42.2363D))V | |
YR = (H/(−6.079 + 5.4717H))V | |
Mixed coniferous broad-leaved forest | YS = (D/(−1.3193 + 2.0249D))V |
YB = (D/(58.0685 + 5.6768D))V | |
YF = (D/(−507.269 + 78.1762D))V | |
YR = (H/(−2.1908 + 5.6433H))V | |
Mixed coniferous forest | YS = (D/(0.6756 + 2.1D))V |
YB = 0.0631D0.3538H−0.2781V | |
YF = (D/(−156.921 + 43.4676D))V | |
YR = (D/(14.289 + 4.883D))V |
2.2.2. Remote Sensing Data
3. Methodology
3.1. Models
3.1.1. Multiple Linear Regression (MLR)
3.1.2. Deep Neural Networks (DNN)
3.1.3. Convolutional Neural Network (CNN)
3.2. Evaluation Indicators
3.2.1. Model Evaluation Indicators
3.2.2. Spatial Autocorrelation Analysis
3.2.3. Sen’s Slope Estimator
3.2.4. Mann–Kendall Statistical Test
3.2.5. Hurst exponent
4. Results
4.1. Descriptive Statistics
4.2. Evaluation of Models
4.3. Optimal Model for Prediction
4.4. Spatial Autocorrelation Analysis of Carbon Stocks
4.5. Trends in the Spatial Transformation of Carbon Stocks
4.6. Future Trends in Forest Carbon Stocks
5. Discussion
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Remote Sensing Data | Bands, Indices or Parameters | Definition or Calculation Formula |
---|---|---|
Original band | BLUE(B1) | Water penetration, distinguishing soil vegetation |
GREEN(B2) | Distinguishing different vegetation | |
RED(B3) | Observation of roads, bare soil, and vegetation types | |
NIR(B4) | Estimating biomass and distinguishing wet soils | |
SWIR1(B5) | Distinguishing the road, bare leaking soil | |
SWIR2(B6) | Heat distribution mapping, rock identification | |
Vegetation Index | Normalized Difference Vegetation Index (NDVI) [35] | (NIR − RED)/(NIR + RED) |
Difference Vegetation Index (DVI) [36] | NIR − RED | |
Ratio Vegetation Index (RVI) [37] | NIR/RED | |
Atmospheric Ratio Vegetation Index (ARVI) [38] | (NIR − (2 × RED − BLUE))/(NIR + (2 × RED − BLUE)) | |
Soil-Adjusted Vegetation Index (SAVI) [39] | ((NIR − RED)/(NIR + RED + 0.5))(1 + 0.5) | |
Weighted Difference Vegetation Index (WDVI) [40] | NIR − 0.5 × RED | |
Modified Soil-Adjusted Vegetation Index (MSAVI) [41] | (2NIR + 1 − ((2 × NIR + 1) − 8 × (NIR − RED))0.5)/2 | |
Modified Normalized Difference Water Index (MNDWI) [42] | (NIR − GREEN)/(NIR + GREEN) | |
Land Surface Temperature (LST) [43] | T/(1 + (λT/ρ)lnε); λ is the central wavelength of thermal infrared band, ρ = 1.438 × 10−2 m·k; ε is the surface specific emissivity | |
Normalized Difference Building and Soil Index (NDBSI) [44] | (SI + IBI)/2; SI and IBI represent the soil index and building index, respectively | |
Wetness (WET) [45] | β1BLUE + β2GREEN + β3RED + β4NIR + β5SWIR1 + β6SWIR2; β1–6 are the different coefficients corresponding to different sensor types | |
Texture features | Angular Second Moment (ASMB1–6) | The texture metrics are calculated from the grayscale co-occurrence matrix around each pixel in each band. The grayscale co-occurrence matrix is a list of the frequency of occurrence of different combinations of pixel luminance values (grayscale) in an image. It calculates the number of times a pixel with value X is adjacent to a pixel with value Y in a specific direction and distance, and then derives statistics from this table [46]. |
Contrast (CONTB1–6) | ||
Correlation (CORRB1–6) | ||
Variance (VARB1–6) | ||
Inverse Difference Moment (IDMB1–6) | ||
Sum Average (SAVGB1–6) | ||
Sum Variance (SVARB1–6) | ||
Sum Entropy (SENTB1–6) | ||
Entropy (ENTB1–6) | ||
Difference variance (DVARB1–6) | ||
Difference entropy (DENTB1–6) | ||
Information Measure of Corr. 1 (IMCORR1B1–6) | ||
Information Measure of Corr. 2 (IMCORR2B1–6) | ||
Max Corr. Coefficient (MAXCORRB1–6) | ||
Dissimilarity (DISSB1–6) | ||
Inertia (INERB1–6) | ||
Cluster Shade (SHADEB1–6) | ||
Cluster prominence (PROMB1–6) |
Year | CS-Mean (Mg C ha−1) | CS-Standard Deviation (Mg C ha−1) | CS-Median (Mg C ha−1) |
---|---|---|---|
1989 | 74.42 | 38.35 | 71.65 |
1999 | 73.62 | 40.41 | 67.29 |
2009 | 77.25 | 24.41 | 78.55 |
2019 | 72.46 | 21.87 | 70.57 |
Combination | MLR | DNN | CNN | |||
---|---|---|---|---|---|---|
MAE | R2 | MAE | R2 | MAE | R2 | |
1 | 17.58 | 0.34 | 16.87 | 0.51 | 16.32 | 0.54 |
2 | 18.16 | 0.29 | 17.09 | 0.48 | 16.87 | 0.52 |
3 | 18.01 | 0.35 | 17.32 | 0.42 | 16.34 | 0.54 |
4 | 17.96 | 0.31 | 17.29 | 0.45 | 17.11 | 0.49 |
5 | 17.60 | 0.34 | 16.84 | 0.51 | 16.38 | 0.53 |
6 | 17.87 | 0.32 | 16.98 | 0.49 | 16.90 | 0.51 |
7 | 18.21 | 0.28 | 17.13 | 0.47 | 16.23 | 0.57 |
8 | 17.54 | 0.35 | 17.28 | 0.46 | 16.27 | 0.56 |
9 | 17.71 | 0.32 | 17.32 | 0.41 | 16.97 | 0.50 |
10 | 17.97 | 0.31 | 17.19 | 0.43 | 16.84 | 0.52 |
Mean | 17.86 | 0.32 | 17.13 | 0.46 | 16.62 | 0.53 |
Change Directions | Future Trends | Percentage |
---|---|---|
Continuous decline | Positive significant decline | 0.01 |
Positive decline | 0.06 | |
Rise in the past but declining trend in the future | Reverse significant decline | 0.04 |
Reverse decline | 0.29 | |
Decline in the past but rising trend in the future | Reverse significant rise | 0.13 |
Reverse rise | 0.41 | |
Continuous rise | Positive significant rise | 0.01 |
Positive rise | 0.05 |
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Zhang, X.; Jia, W.; Sun, Y.; Wang, F.; Miu, Y. Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning. Forests 2023, 14, 483. https://doi.org/10.3390/f14030483
Zhang X, Jia W, Sun Y, Wang F, Miu Y. Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning. Forests. 2023; 14(3):483. https://doi.org/10.3390/f14030483
Chicago/Turabian StyleZhang, Xiaoyong, Weiwei Jia, Yuman Sun, Fan Wang, and Yujie Miu. 2023. "Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning" Forests 14, no. 3: 483. https://doi.org/10.3390/f14030483