Estimation of bioclimatic variables of Mongolia derived from remote sensing data

doi:10.1007/s11707-020-0862-9

Front. Earth Sci.

2022, Vol. 16

Issue (2) : 323-339 https://doi.org/10.1007/s11707-020-0862-9

RESEARCH ARTICLE

Estimation of bioclimatic variables of Mongolia derived from remote sensing data

Munkhdulam OTGONBAYAR¹(

), Clement ATZBERGER², Erdenesukh SUMIYA³, Sainbayar DALANTAI¹, Jonathan CHAMBERS⁴

¹. Institute of Geography and Geoecology, Mongolian Academy of Sciences (MAS), Ulaanbaatar 15170, Mongolia
². Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Vienna 1190, Austria
³. School of Arts and Sciences, National University of Mongolia (NUM), Ulaanbaatar 14201, Mongolia
⁴. Cooperazione Internazionale, Milan 50 20151, Italy

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Abstract

Global maps of bioclimatic variables currently exist only at very coarse spatial resolution (e.g. WorldClim). For ecological studies requiring higher resolved information, this spatial resolution is often insufficient. The aim of this study is to estimate important bioclimatic variables of Mongolia from Earth Observation (EO) data at a higher spatial resolution of 1 km. The analysis used two different satellite time series data sets: land surface temperature (LST) from Moderate Resolution Imaging Spectroradiometer (MODIS), and precipitation (P) from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Monthly maximum, mean, and minimum air temperature were estimated from Terra MODIS satellite (collection 6) LST time series product using the random forest (RF) regression model. Monthly total precipitation data were obtained from CHIRPS version 2.0. Based on this primary data, spatial maps of 19 bioclimatic variables at a spatial resolution of 1 km were generated, representing the period 2002–2017. We tested the relationship between estimated bioclimatic variables (SatClim) and WorldClim bioclimatic variables version 2.0 (WorldClim) using determination coefficient (R²), root mean square error (RMSE), and normalized root mean square error (nRMSE) and found overall good agreement. Among the set of 19 WorldClim bioclimatic variables, 17 were estimated with a coefficient of determination (R²) higher than 0.7 and normalized RMSE (nRMSE) lower than 8%, confirming that the spatial pattern and value ranges can be retrieved from satellite data with much higher spatial resolution compared to WorldClim. Only the two bioclimatic variables related to temperature extremes (i.e., annual mean diurnal range and isothermality) were modeled with only moderate accuracy (R² of about 0.4 with nRMSE of about 11%). Generally, precipitation-related bioclimatic variables were closer correlated with WorldClim compared to temperature-related bioclimatic variables. The overall success of the modeling was attributed to the fact that satellite-derived data are well suited to generated spatial fields of precipitation and temperature variables, especially at high altitudes and high latitudes. As a consequence of the successful retrieval of the bioclimatic variables at 1 km spatial resolution, we are confident that the estimated 19 bioclimatic variables will be very useful for a range of applications, including species distribution modeling.

Keywords bioclimatic variables MODIS land surface temperature CHIRPS precipitation

Corresponding Author(s): Munkhdulam OTGONBAYAR

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Online First Date: 14 April 2021 Issue Date: 26 August 2022

Cite this article:

Munkhdulam OTGONBAYAR,Clement ATZBERGER,Erdenesukh SUMIYA, et al. Estimation of bioclimatic variables of Mongolia derived from remote sensing data[J]. Front. Earth Sci., 2022, 16(2): 323-339.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-020-0862-9
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I2/323

Fig.1 Study area and important environmental variables. (a) Digital Elevation Model (DEM) derived from the Shuttle Radar Topography Mission (SRTM) with meteorological stations (n = 63), (b) Köppen climate classification of the world (Kottek et al., 2006), (c) estimated annual average air temperature derived from MODIS MOD11A2 (v006) (Otgonbayar et al., 2019), (d) annual total precipitation (Fick and Hijmans, 2017), (e) average annual NDVI derived from MODIS MOD13A2 (v006) (Vuolo et al., 2012), (f) Terrestrial Ecoregions of the World (Olson et al., 2001).

Tab.1 Two precipitation products with a high spatial resolution (Roca et al., 2019; Sun et al., 2018; Bai and Liu, 2018; Beck et al., 2017). Only CHIRPS was used for this study. See Appendix 1 for a performance comparison

Fig.2 Correlation matrix between WorldClim and SatClim four variables extracted from the metrological stations (n = 63). (a) monthly maximum temperature; (b) monthly minimum temperature; (c) monthly average temperature; (d) monthly total precipitation. High correlations were shown in blue and low correlations in red.

Tab.2 Inter-correlation between estimated monthly air temperatures derived from MODIS LST, and World climatic data sets; precipitation CHRIPS and World climatic data sets (n = 63). High inter-correlations (r≥0.90) are highlighted in blue

Variable name	Unit	Formula	Description
Annual mean temperature	°C	$B i o ? 1 = ∑ i = 1 i = 12 T a v g i 12$	The annual mean temperature approximates the total energy for an ecosystem.
Annual mean diurnal range	°C	$B i o ? 2 = ∑ i = 1 i = 12 (T m a x i − T m i n i) 12$	Mean of the monthly temperature range. This variable can help provide information relating to the relevance of temperature variation for different species
Isothermality	%	$B i o ? 3 = B i o ? 2 B i o ? 7 × 100$	Isothermality quantifies how large the day to night temperatures fluctuate relative to the summer to winter (annual) fluctuations. A species distribution may be influenced by larger or smaller temperature oscillation within a month relative to the year and this variable is useful for confirming such information.
Temperature seasonality (Standard deviation)	°C	$B i o ? 4 = S D {T a v g 1, ..., T a v g 12}$	Temperature seasonality is a measure of temperature change over the year. A large Standard Deviation (SD) the larger variability of temperature.
Maximum temperature of the warmest month	°C	$B i o ? 5 = max ({T m a x 1 − T m a x 12})$	Monthly maximum temperature incidence over a given year (time series) or averaged span of years. This variable is useful to test that species distribution is affected influenced by warm temperature anomaly over the year.
Minimum temperature of the coldest month	°C	$B i o ? 6 = min ? ({T m i n 1 − T m i n 12})$	Monthly minimum temperature incidence over a given year (time series) or averaged span of years. This variable is useful to test that species distribution is affected influenced by cold temperature anomaly over the year.
Annual temperature range	°C	$B i o ? 7 = B i o ? 5 − B i o ? 6$	The measure of temperature fluctuation over a given year. This variable is useful to investigate whether species distribution is affected by the range of extreme temperature conditions.
Mean temperature of wettest quarter	°C	$B i o ? 8 = ∑ i = 1 i = 3 T a v g i 3 ? o r ? Q P P T m a x$	The quarterly variable is based on 3 months interval that is a mean temperature that prevails during the wettest season. The variable is useful for analyzing how such environmental factors can influence species season distribution.
Mean temperature of driest quarter	°C	$B i o ? 9 = ∑ i = 1 i = 3 T a v g i 3 ? o r ? Q P P T m i n$	The variable provides mean temperature during the driest 3 months of the year which is useful for analyzing how such environmental factors can influence species season distribution.
Mean temperature of warmest quarter	°C	$B i o ? 10 = ∑ i = 1 i = 3 T a v g i 3 ? o r ? Q T m a x$	The variable provides mean temperature during the warmest 3 months of the year which is useful for analyzing how such environmental factors can influence species season distribution.
Mean temperature of coldest quarter	°C	$B i o ? 11 = ∑ i = 1 i = 3 T a v g i 3 ? o r ? Q T m i n$	The variable provides mean temperature during the coldest 3 months of the year which is useful for analyzing how such environmental factors can influence species season distribution.
Annual precipitation	mm	$B i o ? 12 = ∑ i = 1 i = 12 P P T i ?$	The variable is recognized by the sum of 12 monthly precipitation values which is useful ascertaining the significance of water availability to the species distributions.
Precipitation of wettest month	mm	$B i o ? 13 = max ({P P T 1, ..., P P T 12})$	The variable is recognized by total precipitation values that prevail during the wettest month. The wettest month is useful if extreme precipitation conditions during the year affect a species potential range.
Precipitation of driest month	mm	$B i o ? 14 = min ({P P T 1, ..., P P T 12})$	The variable is recognized by total precipitation values that prevail during the driest month. The driest month is useful if extreme precipitation conditions during the year affect a species potential range.
Precipitation seasonality	mm	$B i o ? 15 = SD (P P T 1, ..., P P T 12) 1 + (B i o ? 12 / 12)$	The variable is a measure of the variation monthly precipitation totals over the year. This variable is expressed by percentage where larger percentages represent greater variability of precipitation.
Precipitation of wettest quarter	mm	$B i o ? 16 = m a x ∑ i = 1 i = 3 P P T i$	This quarterly provides total precipitation during the wettest 3 months of the year which can be useful for testing how such environmental factors can influence species season distribution.
Precipitation of driest quarter	mm	$B i o ? 17 = m i n ∑ i = 1 i = 3 P P T i$	This quarterly provides total precipitation during the driest 3 months of the year which can be useful for testing how such environmental factors can influence species season distribution.
Precipitation of warmest quarter	mm	$B i o ? 18 = ∑ i = 1 i = 3 P P T i ? or ? Q T max$	This quarterly provides total precipitation during the warmest 3 months of the year which can be useful for testing how such environmental factors can influence species season distribution.
Precipitation of coldest quarter	mm	$B i o ? 19 = ∑ i = 1 i = 3 P P T i ? or ? Q m i n$	This quarterly provides total precipitation during the coldest 3 months of the year which can be useful for testing how such environmental factors can influence species season distribution.

Tab.3 Formula and description of the bioclimatic variables (O’Donnell and Ignizio, 2012). T_avg, T_max, and T_min are the monthly average, maximum and minimum air temperature, and PPT is the monthly total precipitation

Formula	Description	Range	Reference
$R 2 = 1 − ∑ i = 1 n (V e s t i − V^e s t) 2 ∑ i = 1 n (V e s t i − V ¯ e s t) 2$	The R² measures the correlation between the predicted and observed value (fraction of explained variance)	0 to 1	Richter et al. (2012)
$R M S E = 1 n ∑ i = 1 n (V e s t i − V o b s i) 2$	The RMSE is a measure of the average magnitude of errors along the 1-to-1 line	Data unit	Richter et al. (2012)
$n R M S E = R M S E R a n g e (o b s)$	Normalizing the RMSE facilitates the comparison between data sets or models with different scales. nRMSE is the ratio of the RMSE to the variance of the observed variable.	0 to ∞	Barzegar et al. (2016)

Tab.4 Performance measures used in this study: coefficient of determination (R²), root mean squared error (RMSE), and normalized RMSE (nRMSE). The three statistics represent correlation (association), error (residual), and range normalized errors (Richter et al., 2012)

Tab.5 Descriptor statistics of the estimated 19 bioclimatic variables (SatClim) for the years 2002–2017 (n = 1 575 107 pixel)

Fig.3 (a) Modeled 19 SatClim bioclimatic variables using MODIS and CHIRPS data 2002-2017, (b) WorldClim variables with gridded data 1971-2000, (c) Frequency distributions of SatClim and WorldClim

Tab.6 Summary of statistics describing the correspondence between SatClim and WorldClim data over Mongolia (R², RMSE, and nRMSE). For the comparison, 19 variables were extracted from the full image (n = 1 575 107 pixel). High correlations (R²≥0.70) are highlighted in light gray

Table A1. Summary statistics for monthly averages per climate region station-measured precipitation, CHIRPS and PERSIANN-CCS for the years 2003–2017. Each Köppen climate classification of Mongolia (Fig. 1(b)) is represented by weather stations

Fig. A1 Distribution of measured precipitation, CHIRPS, and PERSIANN-CCS times series (monthly data) for years 2002–2017.

Table A2. Descriptor statistics of the estimated monthly average, monthly minimum, monthly maximum air temperature, and monthly total precipitation for the years 2002?2017. Tmax, Tavg and Tmin are the monthly average, maximum and minimum air temperature, and PPT is the monthly total precipitation.

Fig. A2. Estimated monthly average temperature (a) derived from MODIS LST data, and monthly total precipitation retrieved from CHRIPS data (b) for the years 2002?2017.

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