Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China

doi:10.17521/cjpe.2021.0373

Abstract

Abstract:

Aims Plant diversity monitoring is the basis of biodiversity assessment and developing conservation policy. Traditional forest plant diversity monitoring is mainly based on field surveys, which is difficult to quickly obtain the spatial distribution and dynamic change of forest plant diversity. The development of remote sensing technology provides an important tool for assessing forest plant diversity at the regional scale. In this study, we explored two methods of forest plant diversity estimation based on Sentinel-2A satellite images and field data in three selected national nature reserves (Liangshui, Fenglin, and Hunchun).

Methods We used two methods to estimate forest plant diversity: (1) Direct estimation based on spectral diversity at the pixel and cluster scales, respectively; (2) Indirect estimation based on random forest regression. The spectral diversity was calculated based on the coefficient of variation and convex hull area at the pixel scale, respectively. K-means clustering method was used for cluster analysis to calculate the spectral diversity between clusters. For the indirect estimation, we used 10-fold cross validation to select characteristic variables for later diversity calculation.

Important findings Our results showed that: (1) At the pixel scale, the estimation accuracy of Shannon-Wiener diversity index based on convex hull area (R²= 0.74) was better than that of coefficient of variation (R²= 0.60); (2) The pixel-based estimation accuracy of Shannon-Wiener diversity index outperformed clustering basis (R²= 0.59); (3) Based on six feature variables, the Shannon-Wiener diversity index was best estimated using the random forest regression algorithm (R²= 0.79); (4) Both the Simpson diversity index and species richness could not be accurately estimated by the above methods. Our findings indicate the capability of Sentinel-2A satellite images to estimate the Shannon-Wiener diversity index, providing reference and basis for forest plant diversity estimation at a large scale.

Key words: forest plant diversity, Sentinel-2A, spectral diversity, cluster analysis, random forest regression

ZHOU Kai-Ling, ZHAO Yu-Jin, BAI Yong-Fei. Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China[J]. Chin J Plant Ecol, 2022, 46(10): 1251-1267.

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Figures/Tables 9

References 96

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样地 Sample plot	地理位置 Geographical position	郁闭度 Crown density	物种丰富度 Number of species	Shannon-Wiener多样性指数 Shannon-Wiener diversity index	Simpson多样性指数 Simpson diversity index
1	129.19° E, 48.12° N	0.85	6	1.56	0.76
2	129.19° E, 48.12° N	0.75	10	1.89	0.82
3	129.19° E, 48.12° N	0.80	7	1.67	0.79
4	129.18° E, 48.13° N	0.85	10	1.92	0.82
5	129.18° E, 48.13° N	0.75	10	1.96	0.83
6	131.11° E, 43.44° N	0.70	11	0.87	0.83
7	131.11° E, 43.44° N	0.60	10	0.88	0.85
8	131.11° E, 43.44° N	0.65	8	0.77	0.78
9	131.07° E, 43.47° N	0.60	6	0.65	0.74
10	131.07° E, 43.47° N	0.70	4	0.44	0.56
11	131.07° E, 43.47° N	0.60	8	0.73	0.74
12	128.90° E, 47.18° N	0.60	5	1.34	0.80
13	128.89° E, 47.18° N	0.70	7	1.27	0.66
14	128.89° E, 47.18° N	0.70	6	1.56	0.82
15	128.89° E, 47.18° N	0.65	7	1.01	0.73
16	128.89° E, 47.19° N	0.60	6	1.17	0.72
17	128.89° E, 47.18° N	0.60	4	0.96	0.67
18	128.86° E, 47.20° N	0.70	10	2.06	0.86
19	128.86° E, 47.20° N	0.70	11	2.17	0.84
20	128.86° E, 47.20° N	0.70	9	1.82	0.79

样地 Sample plot	地理位置 Geographical position	郁闭度 Crown density	物种丰富度 Number of species	Shannon-Wiener多样性指数 Shannon-Wiener diversity index	Simpson多样性指数 Simpson diversity index
1	129.19° E, 48.12° N	0.85	6	1.56	0.76
2	129.19° E, 48.12° N	0.75	10	1.89	0.82
3	129.19° E, 48.12° N	0.80	7	1.67	0.79
4	129.18° E, 48.13° N	0.85	10	1.92	0.82
5	129.18° E, 48.13° N	0.75	10	1.96	0.83
6	131.11° E, 43.44° N	0.70	11	0.87	0.83
7	131.11° E, 43.44° N	0.60	10	0.88	0.85
8	131.11° E, 43.44° N	0.65	8	0.77	0.78
9	131.07° E, 43.47° N	0.60	6	0.65	0.74
10	131.07° E, 43.47° N	0.70	4	0.44	0.56
11	131.07° E, 43.47° N	0.60	8	0.73	0.74
12	128.90° E, 47.18° N	0.60	5	1.34	0.80
13	128.89° E, 47.18° N	0.70	7	1.27	0.66
14	128.89° E, 47.18° N	0.70	6	1.56	0.82
15	128.89° E, 47.18° N	0.65	7	1.01	0.73
16	128.89° E, 47.19° N	0.60	6	1.17	0.72
17	128.89° E, 47.18° N	0.60	4	0.96	0.67
18	128.86° E, 47.20° N	0.70	10	2.06	0.86
19	128.86° E, 47.20° N	0.70	11	2.17	0.84
20	128.86° E, 47.20° N	0.70	9	1.82	0.79

植被指数 Vegetation index	计算公式 Calculate formula	参考文献 Reference
TCARI	3[(R_699.19 - R_668.98) - 0.2(R_699.19 - R_550.67)(R_699.19/R_668.98)]	Kim et al., 1994
OSAVI	(1 + 0.16)(R₇₅₀ - R₇₀₅)/(R₇₅₀+ R₇₀₅+ 0.16)	Wu et al., 2008
OSAVI2	(1 + 0.16)(R₈₀₀ - R₆₇₀)/(R₈₀₀+ R₆₇₀+ 0.16)	Rondeaux et al., 1996
DATT	(R₈₅₀ - R₇₁₀)/(R₈₅₀ - R₆₈₀)	Datt, 1999
DATT2	R₈₅₀/R₇₁₀	Datt, 1999
Gitelson	1/R₇₀₀	Gitelson et al., 1999
SR1	R₇₅₀/R₇₀₀	Gitelson & Merzlyak, 1997
SR2	R₇₀₀/R₆₇₀	McMurtrey III et al., 1994
SR3	R₇₃₀/R₇₀₆	Zarco-Tejada et al., 2003
SR4	R₆₇₅/R₇₀₀	Gitelson et al., 2003
MSI	R₁₆₀₀/R₈₁₉	Hunt & Rock, 1989
NDII	(R₈₁₉ - R₁₆₄₉)/(R₈₁₉+ R₁₆₄₉)	Hardisky et al., 1983
CRI1	1/R₅₁₀ - 1/R₅₅₀	Gitelson et al., 2002
CRI2	1/R₅₁₀ - 1/R₇₀₀	Gitelson et al., 2002
ARI	1/R₅₅₀ - 1/R₇₀₀	Sims & Gamon, 2002
PSRI	(R₆₈₀ - R₅₀₀)/R₇₅₀	Merzlyak et al., 1999
NDVI	(R_750.66 - R_704.6)/(R_750.66+ R_704.6)	Gitelson & Merzlyak, 1994
GNDVI	(R₇₈₃ - R₅₆₀)/(R₇₈₃+ R₅₆₀)	Rozenstein et al., 2019
TNDVI	((R₈₄₂ - R₆₆₅)/(R₈₄₂+ R₆₆₅) + 0.5)^0.5	Rozenstein et al., 2019
WDVI	R₈₄₂ - R₆₆₅ × 0.5	Rozenstein et al., 2019
NDI45	(R₇₀₅ - R₆₆₅)/(R₇₀₅+ R₆₆₅)	Delegido et al., 2011
SAVI	(1 + L) × (R_799.09 - R_680.045)/(R_799.09 + R_680.045+ L) (L = 0.5)	Huete, 1988
SAVI2	R_799.09/(R_680.045+ b/a) (a = 0.969 1, b = 0.084 726)	Major et al., 1990
ARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1) ARVI = (R_799.09 - RB)/(R_799.09+ RB)	Kaufman & Tanre, 1992
SARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1, L = 0.5) SARVI = (1 + L)(R_799.09 - RB)/(R_799.09+ RB + L)	Kaufman & Tanre, 1992
EVI	G(R_799.09 - R_680.045)/(R_799.09 + C₁R_680.045 - C₂R_444.5+ L) (G = 2.5, C₁= 6, C₂= 7.5, L = 1)	Huete et al., 1997
IRECI	(R₇₈₃ - R₆₆₅)/(R₇₀₅/R₇₄₀)	Frampton et al., 2013
IPVI	R₈₄₂/(R₈₄₂+ R₆₆₅)	Rozenstein et al., 2019
PSSRA	R₇₈₃/R₆₆₅	Rozenstein et al., 2019
RVI	R₈₄₂/R₆₆₅	Rozenstein et al., 2019

植被指数 Vegetation index	计算公式 Calculate formula	参考文献 Reference
TCARI	3[(R_699.19 - R_668.98) - 0.2(R_699.19 - R_550.67)(R_699.19/R_668.98)]	Kim et al., 1994
OSAVI	(1 + 0.16)(R₇₅₀ - R₇₀₅)/(R₇₅₀+ R₇₀₅+ 0.16)	Wu et al., 2008
OSAVI2	(1 + 0.16)(R₈₀₀ - R₆₇₀)/(R₈₀₀+ R₆₇₀+ 0.16)	Rondeaux et al., 1996
DATT	(R₈₅₀ - R₇₁₀)/(R₈₅₀ - R₆₈₀)	Datt, 1999
DATT2	R₈₅₀/R₇₁₀	Datt, 1999
Gitelson	1/R₇₀₀	Gitelson et al., 1999
SR1	R₇₅₀/R₇₀₀	Gitelson & Merzlyak, 1997
SR2	R₇₀₀/R₆₇₀	McMurtrey III et al., 1994
SR3	R₇₃₀/R₇₀₆	Zarco-Tejada et al., 2003
SR4	R₆₇₅/R₇₀₀	Gitelson et al., 2003
MSI	R₁₆₀₀/R₈₁₉	Hunt & Rock, 1989
NDII	(R₈₁₉ - R₁₆₄₉)/(R₈₁₉+ R₁₆₄₉)	Hardisky et al., 1983
CRI1	1/R₅₁₀ - 1/R₅₅₀	Gitelson et al., 2002
CRI2	1/R₅₁₀ - 1/R₇₀₀	Gitelson et al., 2002
ARI	1/R₅₅₀ - 1/R₇₀₀	Sims & Gamon, 2002
PSRI	(R₆₈₀ - R₅₀₀)/R₇₅₀	Merzlyak et al., 1999
NDVI	(R_750.66 - R_704.6)/(R_750.66+ R_704.6)	Gitelson & Merzlyak, 1994
GNDVI	(R₇₈₃ - R₅₆₀)/(R₇₈₃+ R₅₆₀)	Rozenstein et al., 2019
TNDVI	((R₈₄₂ - R₆₆₅)/(R₈₄₂+ R₆₆₅) + 0.5)^0.5	Rozenstein et al., 2019
WDVI	R₈₄₂ - R₆₆₅ × 0.5	Rozenstein et al., 2019
NDI45	(R₇₀₅ - R₆₆₅)/(R₇₀₅+ R₆₆₅)	Delegido et al., 2011
SAVI	(1 + L) × (R_799.09 - R_680.045)/(R_799.09 + R_680.045+ L) (L = 0.5)	Huete, 1988
SAVI2	R_799.09/(R_680.045+ b/a) (a = 0.969 1, b = 0.084 726)	Major et al., 1990
ARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1) ARVI = (R_799.09 - RB)/(R_799.09+ RB)	Kaufman & Tanre, 1992
SARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1, L = 0.5) SARVI = (1 + L)(R_799.09 - RB)/(R_799.09+ RB + L)	Kaufman & Tanre, 1992
EVI	G(R_799.09 - R_680.045)/(R_799.09 + C₁R_680.045 - C₂R_444.5+ L) (G = 2.5, C₁= 6, C₂= 7.5, L = 1)	Huete et al., 1997
IRECI	(R₇₈₃ - R₆₆₅)/(R₇₀₅/R₇₄₀)	Frampton et al., 2013
IPVI	R₈₄₂/(R₈₄₂+ R₆₆₅)	Rozenstein et al., 2019
PSSRA	R₇₈₃/R₆₆₅	Rozenstein et al., 2019
RVI	R₈₄₂/R₆₆₅	Rozenstein et al., 2019