Spatiotemporal dynamic of oak forest greenness in response to climate change derived drought

Daghestani, Maryam; Motlagh, Mohadeseh Ghanbari; Halimi, Mansour

doi:10.1590/01047760202430013269

Maryam Daghestani ^✤✤Corresponding author: maryam.daghestani811@gmail.com

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Department of agriculture and natural resources, Abhar Branch, Islamic Azad university, Abhar, Iran

https://orcid.org/0000-0002-5626-0384

Mohadeseh Ghanbari Motlagh

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Department Environmental and Forest Sciences, Faculty of of Natural resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

https://orcid.org/0000-0001-7680-7175

Mansour Halimi

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Department of Climatology, Tarbiat Modares University, Tehran, Iran

https://orcid.org/0000-0002-2900-9311

^✤Corresponding author: maryam.daghestani811@gmail.com

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Figure 1:
Location of the Study area (Kohgiloyeh and Boyerahmad Province of IRAN).

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Figure 2:
The variation of oak forest areas using threshold EVI (EVI>0.2) -a, and variation of oak forest areas using threshold NDVI (NDVI>0.25) -b.

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Figure 3:
20 years average of inter-annual MODIS Vegetation Indices Fluctuation.

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Figure 4:
20 Years trend of spatial average of NDVI/EVI-Jun and MAM-PDSI&SPI.

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Figure 5:
Variation of oak forest area using experimental threshold NDVI/EVI greenness.

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Figure 6:
The temporal relationship between spatial average NDVI/EVI- Jun and MAM-PDSI & SPI.

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Figure 7:
Spatial Distribution of MAM-PDSI in 2000-2020 period and 2050 under 2 scenarios RCP2.6 and RCP8.5.

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Figure 8:
Developed RCP2.6 PDSI-based GWR model for predicting Oak Forest EVI- Jun 2050 -a, and Developed RCP8.5 PDSI-based GWR model for predicting Oak Forest EVI- Jun 2050 - b.

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Figure 9:
Developed RCP8.5 and RCP2.6 PDSI-based GWR model for predicting Oak Forest EVI-Jun 2050 under 2 scenarios.

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Figure 10:
Spatial variation (CV%) of greenness of oak forests during the study period and 2 climate change scenarios.

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Table 1:
Classification of drought severity based on SPI index.

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Table 2:
Drought severity classification based on Palmer index.

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References Parameters Formula Index number McKee at al. (1993) SPI: Drought index P i: Average rainfall each year P¯: Average rainfall during the study period SD: Standard deviation of precipitation in the study period g(x) probability density function of gamma distribution x is the amount of precipitation, Π(α) represents the gamma function, α is the shape parameter, β is the scale parameter. The parameter values of this formula must be greater than 0. n number of rainfall observations, x¯: average rainfall of the desired time period Since the gamma function for 0 precipitation cannot be defined and the real precipitation data always includes a large number of observations with zero precipitation, the cumulative probability function of precipitation (H(x)) is calculated in another way. q is the probability of zero rainfall and p = 1 - q. H(x) is the cumulative probability function. In these relationships, c and d are constant values. d 1 = 3.43278 d 2 = 0.18929 d 3 = 0.003308 c 0 = 2.535537 c 1 = 0.802853 c 2 = 0.030328 S P I = P i − P ¯ S D g ( x ) = 1 β α Π ( α ) x ( α − 1 ) e ( − x / β ) α = 1 4 A 1 + 1 + 4 A 3 β = x ¯ 4 A A = ln ( x ¯ ) − ∑ ln ( X ) n g ( x ) = 1 Π ( α ) ∫ 0 x t α − 1 e − 1 d t H ( x ) = q + p g ( x ) S P I = − t − c 0 + c 1 t + c 2 t 2 1 + d 1 t + d 2 t 2 + d 3 t 3 0 < H x ≤ 0.5 S P I = + t − c 0 + c 1 t + c 2 t 2 1 + d 1 t + d 2 t 2 + d 3 t 3 0.5 < H x < 1 SPI 1 Palmer (1965) and Alley (1984) PET: Potential Evapotranspiration ET: evapotranspiration PR: Moisture retention potential R: Moisture retention PRO: Runoff Potential RO: Runoff () L: moisture loss PL: Potential moisture loss AWC: Available water content Ss: Surface soil moisture content Su: Subsoil moisture content Coefficients α, β, γ and δ which are coefficients of water balance, i represents the month of the year. P: Actual precipitation each month P^: Hypothetical precipitation D: changes in humidity Z: Humidity anomaly index D¯i is the absolute mean values of D for the ith month. K i is climatic characteristics. Z i Humidity anomaly index of each month X i: Palmer drought severity index for each specific month P R = A W C − S u + S s P R O = A W C − P R = S u + S s P L = P E T − S s × S u / A W C + S s α i = E T ¯ i P E T ¯ i β i = R ¯ i P R ¯ i γ i = R O ¯ i P R O ¯ i δ i = L ¯ i P L ¯ i D = P − P ^ P ^ = α i P E + β i P R + γ i P R O + δ i P L Z = D × K i K i ' = 1.5 × l o g 10 P E T ¯ i + R ¯ i + R O ¯ i P ¯ i + L ¯ i + 2.8 D ¯ i + 0.5 K i = 17.67 K i ' ∑ j = 1 12 D ¯ i × K i ' X i = 0.897 X i − 1 + 1 3 Z i PDSI 2 Tucker et al. (1979) PNIR: Reflection in the infrared region RRED: Reflection in the red region N D V I = P N I R − R R E D P N I R + R R E D NDVI 3 Huete et al. (2002) EVI: Enhanced Vegetation Index PNIR: reflectance in the near infrared region PRED: Reflection in the red area RBLUE: reflection in the blue band L = -1, C 1 = -6 C 2 = -7.5 are adjustment factor or soil background, atmospheric resistance factor or atmospheric correction, respectively. E V I = P N I R − P R E D P N I R + C 1 R R E D − C 2 R B L U E + 1 1 + L EVI 4 Ji and Peters (2003) n: number of series observations, xj and x k are the data of jm and km series, respectively r: correlation coefficient r = ∑ i = 1 n ( x i − x ¯ ) ( y i − y ¯ ) ∑ i = 1 n ( x i − x ¯ ) 2 ∑ i = 1 n ( y i − y ¯ ) 2 (Pearson Correlation Coefficient) 5 Fotheringham et al. (2002) y: EVI/NDVI in location u x: SPI/PDSI2050 in location u. β: model spatial weighted y i ( u ) = β 0 i ( u ) + β 1 i ( u ) x 1 i + β 2 i ( u ) x 2 i + ... + β m i ( u ) x m i + GWR (Geographicaly Weigted Regression) 6

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Table 4:
The Spatio-temporal correlation among NDVI/EVI-JUN threshold and 20 years average monthly rainfall, SPI and PDSI (*P-value= 0.05, ** P-value= 0.01).

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Class	Index Value
Extremely wet	More than 2
Very wet	1.5 to 1.99
Moderate wet	1 to 1.49
Near normal	-0.99 to 0.99
Moderate dry	-1 to -1.49
Severely dry	-1.49 to -1.99
Extremely dry	-2 and less

References	Parameters	Formula	Index	number
McKee at al. (1993)	SPI: Drought index P _i: Average rainfall each year $\bar{P}$ : Average rainfall during the study period SD: Standard deviation of precipitation in the study period g(x) probability density function of gamma distribution x is the amount of precipitation, Π(α) represents the gamma function, α is the shape parameter, β is the scale parameter. The parameter values of this formula must be greater than 0. n number of rainfall observations, $\bar{x}$ : average rainfall of the desired time period Since the gamma function for 0 precipitation cannot be defined and the real precipitation data always includes a large number of observations with zero precipitation, the cumulative probability function of precipitation (H(x)) is calculated in another way. q is the probability of zero rainfall and p = 1 - q. H(x) is the cumulative probability function. In these relationships, c and d are constant values. d ₁ = 3.43278 d ₂ = 0.18929 d ₃ = 0.003308 c ₀ = 2.535537 c ₁ = 0.802853 c ₂ = 0.030328	$S P I = \frac{P_{i} - \bar{P}}{S D}$ $g (x) = \frac{1}{β^{α} Π (α)} x^{(α - 1)} e^{(- x / β)}$ $α = \frac{1}{4 A} (1 + \sqrt{1 + \frac{4 A}{3}})$ $β = \frac{\bar{x}}{4 A}$ $A = \ln (\bar{x}) - \frac{\sum \ln (X)}{n}$ $g (x) = \frac{1}{Π (α)} \int_{0}^{x} t^{α - 1} e^{- 1} d t$ $H (x) = q + p g (x)$ $S P I = - [t - \frac{c_{0} + c_{1} t + c_{2} t^{_{2}}}{1 + d_{1} t + d_{2} t^{2} + d_{3} t^{3}}]$ $0 < H (x) \leq 0.5$ $S P I = + [t - \frac{c_{0} + c_{1} t + c_{2} t^{_{2}}}{1 + d_{1} t + d_{2} t^{2} + d_{3} t^{3}}]$ $0.5 < H (x) < 1$	SPI	1
Palmer (1965PALMER, W. C, Meteorological drought. vol. 30. US department of commerce, Weather Bureau Washington, DC, USA, U.S. 58p, 1965. https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf https://www.ncdc.noaa.gov/temp-and-preci... ) and Alley (1984ALLEY, W. M. The Palmer drought severity index: limitations and assumptions. Journal of climate and applied meteorology, v. 23, n. 7, p. 1100-1109, 1984. )	PET: Potential Evapotranspiration ET: evapotranspiration PR: Moisture retention potential R: Moisture retention PRO: Runoff Potential RO: Runoff () L: moisture loss PL: Potential moisture loss AWC: Available water content Ss: Surface soil moisture content Su: Subsoil moisture content Coefficients α, β, γ and δ which are coefficients of water balance, i represents the month of the year. P: Actual precipitation each month $\hat{P}$ : Hypothetical precipitation D: changes in humidity Z: Humidity anomaly index ${\bar{D}}_{i}$ is the absolute mean values of D for the ith month. K _i is climatic characteristics. Z _i Humidity anomaly index of each month X _i: Palmer drought severity index for each specific month	$P R = A W C - (S u + S s)$ $P R O = A W C - P R = S u + S s$ $P L = ((P E T - S s) \times S u) / A W C + S s$ $α_{i} = \frac{{\bar{E T}}_{i}}{{\bar{P E T}}_{i}}$ $β_{i} = \frac{{\bar{R}}_{i}}{{\bar{P R}}_{i}}$ $γ_{i} = \frac{{\bar{R O}}_{i}}{{\bar{P R O}}_{i}}$ $δ_{i} = \frac{{\bar{L}}_{i}}{{\bar{P L}}_{i}}$ $D = P - \hat{P}$ $\hat{P} = α_{i} P E + β_{i} P R + γ_{i} P R O + δ_{i} P L$ $Z = D \times K_{i}$ $K_{_{i}}^{'} = 1.5 \times l o g_{10} \frac{[\frac{{\bar{P E T}}_{i} + {\bar{R}}_{i} + {\bar{R O}}_{i}}{{\bar{P}}_{i} + {\bar{L}}_{i}} + 2.8]}{{\bar{D}}_{i}} + 0.5$ $K_{i} = \frac{17.67 K_{i}^{'}}{\sum_{j = 1}^{12} {\bar{D}}_{i} \times K_{i}^{'}}$ $X_{i} = 0.897 X_{i - 1} + \frac{1}{3} Z_{i}$	PDSI	2
Tucker et al. (1979)	P_NIR: Reflection in the infrared region R_RED: Reflection in the red region	$N D V I = \frac{P_{N I R} - R_{R E D}}{P_{N I R} + R_{R E D}}$	NDVI	3
Huete et al. (2002HUETE, A.; DIDAN, K.; MIURA, T.; et al. Overview of the radiometric and biophysical performance of the modis vegetation indices. Remote sensing of environment, v. 83, n, 1-2, p. 195-213, 2002. )	EVI: Enhanced Vegetation Index P_NIR: reflectance in the near infrared region P_RED: Reflection in the red area R_BLUE: reflection in the blue band L = -1, C ₁ = -6 C ₂ = -7.5 are adjustment factor or soil background, atmospheric resistance factor or atmospheric correction, respectively.	$E V I = {[\frac{P_{N I R} - P_{R E D}}{P_{N I R} + C_{1} R_{R E D} - C_{2} R_{B L U E} + 1}]}_{1 + L}$	EVI	4
Ji and Peters (2003JI, L.; PETERS, A. J. Assessing vegetation response to drought in the northern great plains using vegetation and drought indices. Remote sensing of Environment, v. 87, n. 1, p. 85-98, 2003. )	n: number of series observations, x_j and x _k are the data of jm and km series, respectively r: correlation coefficient	$r = \frac{\sum_{i = 1}^{n} (x_{i} - \bar{x}) (y_{i} - \bar{y})}{\sqrt{\sum_{i = 1}^{n} {(x_{i} - \bar{x})}^{2}} \sqrt{\sum_{i = 1}^{n} {(y_{i} - \bar{y})}^{2}}}$	(Pearson Correlation Coefficient)	5
Fotheringham et al. (2002)	y: EVI/NDVI in location u x: SPI/PDSI2050 in location u. β: model spatial weighted	$y_{i} (u) = β_{0 i} (u) + β_{1 i} (u) x_{1}_{i} + β_{2 i} (u) x_{2}_{i} + ... + β_{m i} (u) x_{m}_{i} +$	GWR (Geographicaly Weigted Regression)	6

	Jun NDVI/ monthly rainfall Correlation(R)	Jun EVI/ monthly rainfall Correlation(R)	Jun NDVI/ monthly SPI Correlation(R)	Jun EVI/ monthly PDSI Correlation(R)	Jun NDVI/ monthly PDSI Correlation(R)	Jun EVI/ monthly SPI Correlation(R)
Jan	0.17	0.12	0.17	0.19	0.2	0.16
Feb	0.21*	0.26*	0.20	0.27*	0.23	0.21
Mar	0.41*	0.44*	0.37*	0.41*	0.36*	0.37*
Apr	0.42*	0.43*	0.35*	0.38*	0.39*	0.35*
May	0.40**	0.42*	0.35*	0.35*	0.34*	0.31*
Jun	0.17	0.12	0.17	0.20	0.19	0.11
Jul	0.012	0.15	0.00	00.00	0.0	0.0
Aug	0.01	0.07	0.01	00.00	0.0	0.0
Sep	0.00	0.05	0.04	00.00	0.09	0.0
Oct	0.00	0.09	0.12	0.09	0.18	0.11
Nov	0.052	0.11	0.12	0.08	0.08	0.05
Dec	0.095	0.14	0.15	0.17	0.11	0.15

S P I = - [t - \frac{c_{0} + c_{1} t + c_{2} t^{_{2}}}{1 + d_{1} t + d_{2} t^{2} + d_{3} t^{3}}]

S P I = + [t - \frac{c_{0} + c_{1} t + c_{2} t^{_{2}}}{1 + d_{1} t + d_{2} t^{2} + d_{3} t^{3}}]

K_{_{i}}^{'} = 1.5 \times l o g_{10} \frac{[\frac{{\bar{P E T}}_{i} + {\bar{R}}_{i} + {\bar{R O}}_{i}}{{\bar{P}}_{i} + {\bar{L}}_{i}} + 2.8]}{{\bar{D}}_{i}} + 0.5

E V I = {[\frac{P_{N I R} - P_{R E D}}{P_{N I R} + C_{1} R_{R E D} - C_{2} R_{B L U E} + 1}]}_{1 + L}

r = \frac{\sum_{i = 1}^{n} (x_{i} - \bar{x}) (y_{i} - \bar{y})}{\sqrt{\sum_{i = 1}^{n} {(x_{i} - \bar{x})}^{2}} \sqrt{\sum_{i = 1}^{n} {(y_{i} - \bar{y})}^{2}}}

y_{i} (u) = β_{0 i} (u) + β_{1 i} (u) x_{1}_{i} + β_{2 i} (u) x_{2}_{i} + ... + β_{m i} (u) x_{m}_{i} +

[1] ^✤Corresponding author: maryam.daghestani811@gmail.com

Code	Class	Index Value
D ₄	Most severe drought	less -4
D ₃	Severe drought	-4 to -3
D ₂	Moderate drought	-3 to -
D ₁	Mild drought	-2 to -1
N	Near Normal	-1 to +1
W ₁	Mild wet	+1 to +2
W ₂	Moderate wet	+2 to +3
W ₃	Severe wet	+3 to +4
W ₄	Most severe wet	More than +4

Brasil

Brasil

Spatiotemporal dynamic of oak forest greenness in response to climate change derived drought

ABSTRACT

Background:

Results:

Conclusion:

HIGHLIGHTS