Application of remote sensing-based spectral variability hypothesis to improve tree diversity estimation of seasonal tropical forest considering phenological variations

Figures & data

Figure 1. Location map of the study area (i.e. Nandhaur Landscape). Field inventory plots location are overlaid on false colour composite of Sentinel-2 (R: NIR, G: Red, B: Green) image dated 8th December, 2018.

Figure 2. Ombrothermic diagram of mean temperature and monthly rainfall of the study area (Source: Eddy Covariance Flux Tower Site, Haldwani-http://asiaflux.net/index.php?page_id=61).

Figure 3. Asynchrony in phenological phases of the dominant trees (Dominance based on Important Value Index (IVI)) of different forest type.

Table 1. Sentinel-2 datasets used in the study.

Sl. No.	Satellite	Date of acquisition
1.	Sentinel-2A	22 January 2018
2.	Sentinel-2A	21 February 2018
3.	Sentinel-2B	18 March 2018
4.	Sentinel-2A	22 April 2018
5.	Sentinel-2B	7 May 2018
6.	Sentinel-2A	21 June 2018
7.	Sentinel-2B	4 October 2018
8.	Sentinel-2B	23 November 2018
9.	Sentinel-2A	18 December 2018

Table 2. Variation in tree species diversity and richness of each forest type and IVI of dominant species.

Forest type and no. of plots	Mean and range of tree species richness at plot level	Overall tree richness	Tree species diversity (H') at plot level		Tree species diversity (H') range (GBH ≥ 30cm)	Dominant species and IVI
			Tree with GBH ≥ 30cm	Including Tree with GBH < 30cm
Dry deciduous forest (20)	6 (2-13)	40	1.43	1.52	0.48 − 2.12	Holoptelea integrifolia (50.78), Acacia catechu (24.26), Lagerstroemia parviflora (20.65), Tectona grandis (18.71), Haldina cordifolia (18.65)
Moist deciduous forest (22)	8 (3-13)	41	1.42	1.47	0.59 − 2.27	Shorea robusta (99.34), Mallotus philippensis (28.59), Syzygium cumini (22.45), Terminalia tomentosa (14.21), Holoptelea integrifolia (12.46)
Semi-evergreen forest (19)	8 (3-14)	54	1.4	1.42	0.45 − 1.89	Syzygium cumini (68.5), Mallotus philippensis (42.78), Shorea robusta (20.91), Trewia nudiflora (14.19), Cordia myxa (12.98)

Figure 4. Temporal variation of R² between tree diversity (H') and Rao’s Q index based on NDVI for different forest types and the overall landscape (all forest type combined) over the year.

Table 3. NDVI based Multi-temporal SVH models developed for different types.

Sl. No.	SVH Models	Months	Relation between tree diversity (H') and multi-temporal Rao’s Q index
			Dry deciduous	Moist deciduous	Semi-evergreen
1.	Model−1	April, May	0.688	0.490	0.386
2.	Model-2	March, April, May	0.488	0.489	0.466
3.	Model-3	March, April, May, June (Summer months)	0.356	0.460	0.474
4.	Model-4	January, February, November, December (Winter months)	0.046	0.352	0.084
5.	Model-5	March, April, May, June, December	0.268	0.490	0.449
6.	Model-6	March, April, May, June, October, December	0.252	0.495	0.450
7.	Model-7	January, March, April, May, June, October, December	0.227	0.467	0.382
8.	Model-8	January, March, April, May, June, October, November, December	0.193	0.513	0.377
9.	Model-9	All images	0.179	0.503	0.341

Figure 5. Relation between tree diversity (H') and NDVI derived multi-dimensional Rao’s Q index for Dry deciduous forest.

Figure 6. Relation between tree diversity (H') and NDVI derived multi-dimensional Rao’s Q index for Moist deciduous forest.

Figure 7. Relation between tree diversity (H') and NDVI derived multi-dimensional Rao’s Q index for Semi-evergreen forest.

Figure 8. Relation of tree diversity (H') and Rao’s Q index derived from NDVI with Moisture Stress Index (MSI) of Sentinel-2 (22 April, 2018 for dry- & moist deciduous and 7 May, 2018 for Semi-evergreen forest).

Figure 9. Annual trend of NDVI computed from Sentinel-2 for different forest types.

Figure 10. Composite Rao’s Q index-based tree diversity map at 0.1 ha scale derived from multi-date NDVI.