Remote Sensing of Environment and Disaster Laboratory

Institute of Industrial Science, The University of Tokyo, Japan

Mongolia Grassland Sustainability Evaluation Through Its
Responsiveness to Precipitation From 2001 to 2019

photo

YAN Guanyu, TAKEUCHI Wataru
Institute of Industrial Science, The University of Tokyo, Japan
1. ABSTRACT

15.5: “Desertification, landdegradation and drought”

With 95 percent of the agricultural lands are pastures, the sustainability of grassland and herding activities is of great importance to Mongolia. In natural conditions, we can assume
the vegetation growth falls into a good correlation with natural factors, and with impacts of urbanization and the trend of overgrazing, the relationship between factors like
precipitation and grassland production is presumed to be changing and less correlated. This research utilizes monthly MODIS-NDVI for vegetation condition, monthly GSMaP data for
precipitation, monthly KBDI data for doughtiness, and MODIS Snow coverage data for snow coverage to calculate the Pearson Coefficients of Mongolia in the first two decades in
Mongolia during the spring-summer season alongside with point observations to make vertical temporal observations. The NDVI-GSMaP pair has a meaningful correlation in the South
while the NDVI-KBDI explains better in the North because water is the more limiting factor in the arid south and drought might impact the relatively humid North. NDVI-SNOW pair
provides many points of interest to further integrate other possible sources of information like winter disasters. With the points sampling into the monthly and yearly records,
patterns of potential irrigation and harvesting/grazing can be found in the monthly records even if much of the curve follows the natural order of growth and decay. Also, the pixels
categorized as meaningful positive or negative groups are not that high in the Pearson Correlation scale with half of all the pixels displaying no correlation between vegetation growth
and the chosen climatic factors according to the statistics further indicating third party interference during the research period. Which, given the economical and agricultural activities
in Mongolia, it could be assumed that most of the interference is grazing. This research paves the way for further analyzing the possible factors and reasons behind suchabnormalities.

Keywords: NDVI, GSMaP, MODIS, KBDI, Snow Coverage, Pearson Coefficients

3. METHODOLOGY

2. BACKGROUND
With the advances of remote sensing and the accumulation ofrecords, it is more and more convenient for us to conduct
temporal research over large scales. According to the UN's FAO
Agency, Pastures make up almost 95 percent of agricultural land
among which about 70 percent have degraded to someextent(“Mongolia at a Glance” n.d.). Due to climate change,
natural hazards, and overgrazing, there have been reports ofincreasing grassland degradation which put heavy risks uponthe livelihood of many people in Mongolia. With the collapse of
the Soviet Union, Mongolia underwent a social-economicaltransformation which led to a sharp increase in urbanization
especially in the capital city of Ulaanbaatar shifting the burden ofhuman activities across the country. With the transition into themarket economy, the nomadic groups conducting animalhusbandry rely more heavily on the raising of cashmere
producing goats which further contributes to the overgrazing
problem.
Usually, with the increase of precipitation, the growth of vegetation would also increase, which in the products of remotesensing data, can be reflected by the normalized difference vegetation index, known as NDVI, an indicator widely used tomonitor vegetation growth status and phenology patterns(Carlson and Ripley 1997)(Zhao et al. 2011; Pettorelli et al. 2005).
However, in the cases of Mongolia, the amount of vegetation growth we can observe is what's left of grazing activities andnatural hazards, that might not be in good correlation with regards to precipitation, that is obtained from the GSMaP
product. But what exactly is the reality in the past two decades concerning the responses of vegetation growth toprecipitation is the first step to figure out how to correctly monitor and evaluate the current grassland and environmental
preservation efforts and will be the focus of this study. We are using the collection of past 19 years of monthly records of
NDVI and GSMaP for vegetation growth and precipitation to conduct correlation analysis of those two factors on a nationallevel with a few close-up analyses upon selected samples to evaluate the responsiveness of vegetation growth toprecipitation in Mongolia. Apart from precipitation, we are also using KBDI(Keetch-Byram Drought Index) and Snow
Coverage Duration (later referred as SNOW) data to observe the vegetation’s response to those factors to conduct a morecomprehensive evaluation of the sustainability in Mongolia.

Data Preprocessing

For the NDVI product, we are using the MOD13A3 data. For the precipitation data, we are using theGSMaP data from JAXA compiled to monthly data. And by the daily GSMaP records, we are also
deriving daily records of KBDI later compiled to monthly mean. For the snow coverage data, we areusing the MOD10A2 Snow Coverage data from the MODIS platform. The discrepancies in the data
range rise in the availability of those data. All the correlations are conducted within the available range.
Also, we are using land type data provided by The Mongolia Environmental Information Center(Wang,Rich, and Price 2003; “EIC GeoNetwork” n.d.).
There are three pairs of correlation to analysis in our research: the NDVI-GSMaP pair, the NDVI-KBDI
pair, the NDVI-SNOW pair. To further understand the detailed monthly changes of the NDVI-GSMaP,NDVI-KBDI, NDVI-SNOW correlation records, we are picking up 6 sets of sampling points eachcontaining 3 points of records in the yearly data and monthly data. Their correlation level distinguishes
those 6 sets and whether they are in natural parks or not to tell if there are potential humaninterferences, e.g., grazing activities.The author only collected the yearly data samples for the NDVISNOW pair.

4. RESULTS & DISCUSSION
Correlations
Fig. 1 Correlation Between NDVI and Precipitation 2001 to 2019, Mongolia

Fig Grp. 1 Yearly Change of NDVI and KBDIIn this paper, we try to evaluate theenvironmental sustainability issue byFig. 2 Correlation Between NDVI and KBDI 2007 to 2019, Mongolia
investigating the vegetation temporal
Fig. 3 Correlation Between NDVI and SNOW 2002 to 2019, Mongolia
response to climatic factors like
precipitation, droughts, and snow coverduration. First, we collected and processed
monthly records of NDVI, GSMaP, KBDI,
and Snow Cover Duration data in theextent of two decades from 2001 to 2019.
With the general spatial distribution
characteristics of those data searched andunderstood, we further conducted
correlation analysis of those variables inthree pairs of bivariate Pearson Correlation
analysis with sampling sites slicing into
single pixels’ yearly and monthly records
Fig Grp. 2 Yearly Change of NDVI and Precipitationfor vertical data investigation. And at theend, we did a bivariate correlation matrix ofthe three pairs of maps to verify that ourresults and data indeed sit in a realistic
logical premise.
From the data, results, and statistics, we can conclude
that the general spatial distribution of NDVI andPrecipitation follows the patterns of decreasing in thedirection from Northeast to Southwest while the KBDI
and Snow Cover take a separated part of Southeastand Northwest component. These two distinct climatic
patterns also show in the Pearson Correlation result
we get, showing the NDVI-GSMaP pair has ameaningful correlation in the South while the NDVIKBDI explains better in the North because water is themore limiting factor in the arid south and drought might
impact the relatively weaker North. And NDVI-SNOW
pair provides many points of interest to further
integrate other possible sources of information likewinter disasters.

It is easy to assume that with GSMaP explaining the Southern region and KBDI covers the Northern region would become a pretty good explaining model, however with a vertical investigation from a pixel level perspective into the monthly andyearly records, we realize that patterns of potential irrigation and harvesting/grazing can be found in the monthly records even if much of the curve follows the natural order of growth and decay. Also, the pixels categorized as meaningful positive or
negative groups are not that high in the Pearson Correlation scale with half of all the pixels displaying no correlation between vegetation growth and the chosen climatic factors according to the statistics further indicating third party interference
during the research period. Which, given the economical and agricultural activities in Mongolia, it is assumed that most of the interference is grazing.
A low correlation number means the local environment potentially suffers from a certain number of human activities like grazing, and to verify such disturbances, we need to acquire the geological data on such activities. In the future research andnext part of the current investigation theme, the author believes the traditional tent house utilized by the people in Mongolia would be a great source of reference and has the potential to solve the low correlation value problem we discovered in thisresearch.

5. REFERENCES
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Carlson, Toby N., and David A. Ripley. 1997. “On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index.” Remote Sensing of Environment. https://doi.org/10.1016/s0034-4257(97)00104-1.
“EIC GeoNetwork.” n.d. Accessed February 26, 2021. http://www.eic.mn:8080/geonetwork/srv/eng/main.home.
“Mongolia at a Glance.” n.d. Accessed February 26, 2021. http://www.fao.org/mongolia/fao-in-mongolia/mongolia-at-a-glance/en/.
Wang, J., P. M. Rich, and K. P. Price. 2003. “Temporal Responses of NDVI to Precipitation and Temperature in the Central Great Plains, USA.” International Journal of Remote Sensing. https://doi.org/10.1080/01431160210154812.
Sekiyama Ayako, Shimada Sawahiko, Yokohama Michinari and Toyoda Hiromichi. "Measuring biomass and its responses to precipitation of grassland in Mongolia using MODIS data." (2015): 28-33.

For further details, contact: Mr. YAN Guanyu, Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: yangy@iis.u-tokyo.ac.jp)