Vegetation mapping was conducted over Mongolian land using GPM/DPR satellite data. Backscatter signals from the Ku and Ka band radar were analyzed for different land cover types from 2014-2018. The signals showed incidence angle dependency and seasonal variations that correlated with land cover. Forest had higher backscatter than grass, while desert was lowest. Combining the Ku and Ka band signals using PCA clearly distinguished forest, grass and desert. This new remote sensing method using precipitation radar provides an effective way to continuously map and monitor vegetation dynamics for detecting land degradation.

1. 2020.03.18
Environmental Information Department
Programmer: Nyamsuren. B
Email: nyamkansn@gmail.com
1
Vegetation mapping by using GPM/DPR
over the Mongolian land
Information and Research institute of
Meteorology, Hydrology and Environment

2. 2020.03.18
Байгаль Орчны Мэдээллийн Сангийн
хэлтсийн программист: Б.Нямсүрэн
1
GPM/DPR хиймэл дагуулаар Монгол
орны Ургамлын Зураглал хийх
Ус Цаг Уур, Орчны Судалгаа,
Мэдээллийн Хүрээлэн

3. Агуулга
 Монголын газрын бүрхэвчийн төрөл
 Ургамлын ач холбогдол
Оршил
 Монгол дахь Ургамлын доройтол
 Монгол дахь Ургамлын зураглал
Үндсэн асуудал
 Global Precipitation Measurement Mission (GPM)
 GPM/DPR радар, Ku/Ka цацрагийн утгууд
Ашигласан материал
• MODIS LAI, Хур тунадасны мэдээ харьцуулах
• NRCS ойсон цацрагийн характеристикууд,
тэдгээрийн зураглал.
Үр дүн
 Хэлэлцүүлэг
 Цаашдын судалгаа
Хэлэлцүүлэг
2Background Problem Method Result Discussion

4. Монгол орны газрын бүрхэвч (MODIS) УЦУОСМХ
Ой (~11%)
Хээр (~66.5%)
Цөл (~22.5%)
Source: http://www.eic.mn/land/gis.php
Mongolia is the landlocked country.
Total area is approximately 1,565,000 square km.
Located in the combined parts of Siberian forest biomes,
Central Asian steppe, and Gobi deserts. Land surface mainly
dominated by grassland. From northern to the south vegetation
density decreases gradually. Climate has 4 season with extreme
high changes hot summer average (+270 C), cold winter (-270 C).
3Background Problem Method Result Discussion

5. Монгол дахь ургамлын доройтол
Уур амьсгалын өөрчлөлт
- Ган
- Цөлжилт (76.9% area affected)
Хүний үйл ажиллагаа
- Уул уурхай
- Бэлчээрийн даац хэтрэлт
https://montsame.mn/en/read/130466
https://news.un.org/en/story http://zamineservices.com/
https://twitter.com/UNDPMongolia/status
Background Problem Method Result Discussion 5

6. Remote sensing
Vegetation dynamic GPM satellite
http://www.surf-forecast.com/breaks/El-Mongol
Designed to observe Earth’s precipitation
- High cost
- Limited coverage area
- Human labor intervention
- Low cost
- Large coverage area
- Less labor intervention
- Continuously mapping
Survey mapping Satellite mapping
Research goal : 1. Try to develop new method for vegetation by precipitation radar
2. Analyze vegetation dynamic for detecting the land degradation
Background Problem Method Result Discussion 6

7. Vegetation indicators in Remote Sensing
Currently in Remote sensing we use
optical sensors which directly see
the Earth from the Space. But we
can not see the Earth under the
cloud cover, haze and at night time.
Microwave Ku,Ka band can
penetrate the cloud cover and
able to detect at night time.
NDVI is ratio of Red, NIR
waves part of the Sunlight.
EVI, SAVI, RVI, ...
7
Sunlight = Electromagnetic Spectrum Ku, Ka

8. Vegetation indicators in Remote Sensing
SAR approach has several
weaknesses:
Microwave Ku,Ka band scan
narrow incidence angle, more
frequent observation.
SAR is complex approach
use microwave low
frequencies X,C,S,L, etc…
8
Sunlight = Electromagnetic Spectrum Ku, Ka

9. NASA ба JAXA-ийн хамтарсан төсөл
Орбит: Circular, non-sunsynchronous
Хөөргөсөн он: 2014.Feb.28
Жин: 3850 kg
Хүч чадал: 1950 W
Ажиглалтын хүрээ: 65°
GPM хиймэл дагуул нь глобал
түвшинд хур тунадасны ажиглалт
хийх зориулалттай.
Global Precipitation Mission – GPM
Background Problem Method Result Discussion 9

10. Global Precipitation Measurement Mission
GPM – Global Precipitation Measurement, launched 2014.Feb
Images source: https://pmm.nasa.gov/gpm
10Background Problem Method Result Discussion

11. GPM satellite Instruments
Build on success of TRMM
 Designed life: 5 years
 GPM Microwave Imager (GMI)
- Passive sensor installed from NASA.
- Multi-channel, conical scanning,
13 channels 10GHz – 183GHz
- Sense to total precipitation within all
cloud layers
Dual-frequency Precipitation Radar
(DPR)
- Active sensor developed from JAXA.
- Consist of Ka-band PR, Ku-band PR.
- Ku-band measure moderate-to-heavy
rain
- Ka-band measure frozen precipitation
and light rain
- Ku/Ka-band provide rain drop size
distribution in the cloud.
11Background Problem Method Result Discussion

12. DPR/ Ku and Ka band radar signal
Vegetation Characteristics
Normalized radar cross
section (NRCS or σ°)
backscattered values were
used for the calculation.
(2014 - 2018, May- Sep 5 km)
Without precipitation condition radar signals
retrieve the information about the land
surface.
Incidence angle Ku band ±17 degree,
Ka band ±8.5 degree scanning.
12Background Problem Method Result Discussion
Ku, Ka
band

13. Compare σ0 with Precipitation and MODIS/LAI
Based on map derived from ESA CCI 2015 landcover map:
13Background Problem Method Result Discussion
Forest
Grass
Desert
The Seasonal variation of: (2014-2017)
 MCD15A3H MODIS Leaf Area Index/FPAR 4 days composite data
 The precipitation data of 129 meteorological stations were used
In order to compare with the Ku and Ka band backscattered values.

14. Scheme to detect σ0 characteristic
GPM
NRCS σ0
map
Forest Incidence
Seasonal
variation
Seasonal
variation
Grass Incidence
Seasonal
variation
Seasonal
variation
Desert Incidence
Seasonal
variation
Seasonal
variation
GTOPO30
ESA CCI
Precipitation
MODIS/LAI
?
?
?
Ku and Ka signal
Background Problem Method Result Discussion 14

15. Characteristic of signals…
GPM
NRCS σ0
map
Forest Incidence
Seasonal
variation
Seasonal
variation
Grass Incidence
Seasonal
variation
Seasonal
variation
Desert Incidence
Seasonal
variation
Seasonal
variation
GTOPO30
ESA CCI
Precipitation
MODIS/LAI
?
?
?
Ku/Ka signal
Background Problem Method Result Discussion 15
Is there any relation???

16. Incidence angle dependency: Seasonal variation:
16Background Problem Method Result Discussion
NRCS σ° backscattering from the Forest

17. Incidence angle dependency: Seasonal variation:
17Background Problem Method Result Discussion
NRCS σ° backscattering from the Grass

18. Incidence angle dependency: Seasonal variation:
18Background Problem Method Result Discussion
NRCS σ° backscattering from the Desert

19. Compare backscatter from different Land
covers
 Ku-band backscatter with
incidence angle dependency.
 Ka-band backscatter with
incidence angle dependency.
Background Problem Method Result Discussion 19
Desert
Desert
Grass
Grass
Forest Forest

20.  Ku-band backscatter with
incidence angle dependency.
 Ka-band backscatter with
incidence angle dependency.
Background Problem Method Result Discussion 20
~3° ~3°
Compare backscatter from different Land
covers
Forest
Grass
Desert
Desert
Grass
Forest

21. From the Seasonal Aspect
~8°
~8°
Background Problem Method Result Discussion 21

22. Background Problem Method Result Discussion
Mapping – False color composite σ0
at ~3° and ~8°
22
KuPR:
R = σ0(3°)*1.4
G = σ0(7°+8°)
B = σ0(8°)*1.2
KaPR:
R = σ0(3°)*1.4
G = σ0(7°+8°)
B = σ0(8°)*1.2
MCD43A4:
R = B1
G = B4
B = B3

23. Mapping – Unsupervised clustering σ0
at ~3° to ~8°
Background Problem Method Result Discussion 23
KuPR: Forest and grass distinguished sufficiently.
KaPR: Desert and grass discriminated significantly.
Combination of these two radars backscatter might can distinguish
the forest, grass, and desert clearly.

24. Combination of KuPR and KaPR
+
concatenate
KuPR σ0 KaPR σ0
PCA (3 component) =
σ0
(2° ~ 8°)
Background Problem Method Result Discussion 24
σ0
(2° ~ 8°)
Explained total variance ~ 90 %

25. Combination of KuPR, KaPR radar
Background Problem Method Result Discussion 25
White: Water bodies
Black: Forest
Green: Grass land
Yellow-green: Desert

26. Discussion – compared with NDVI
NRCS vs NDVI map
KuPR KaPR σ0(2°~8°)
maps compared with
MOD13A2 16 days
1km NDVI maps in
2014, 2015, 2016,
2017, 2018 years
respectively.
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0.
0
0.
0
0.6
5
0.
0
0.
0
0.
0.6
5
0.6
5
0.6
5
0.6
5

27. Discussion
Background Problem Method DiscussionResult
 GPM/DPR радарын ойлт нь хазайлтын өнцөг
нэмэгдэхэд Ойн бүсээс тогтмол (бага), харин Цөлийн
бүсээс буурах хандлага ажиглагдсан.
 Монголын нөхцөлд ургамал ургах улирал буюу 5-9-р
сар дахь Ku ба Ka сигналын 3° - 8° хазайлтын
өнцөгтэй ажиглалтын утгууд нь ургамлын зураглал
хийхэд хүрэлцээтэй.
 Ku-сигналь Ойн зураглал хийхэд тохиромжтой, Ka-
сигналь цөлийн бүсийг газраас ялган ангилахад
тохиромжтой.
27

28. Discussion
Background Problem Method DiscussionResult 28
The results showed same characteristics
with the ground radar experiments of Ku-
band (13 GHz) in Ulaby et al, study.
Dense
Dense
Sparse
Sparse

29. 2. Journal Paper

30. Thank you for your attention…
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