This document outlines a study to estimate above-ground biomass and carbon stock in boreal forests in Mongolia using satellite data and machine learning. Boreal forests cover about 9.2% of Mongolia but have been declining in recent decades. The study aims to develop a suitable machine learning model to map forest biomass and carbon stock. Random forest was the best performing model with an R2 of 0.24 and RMSE of 33 Mg/ha. Important input features included shortwave infrared band 1, green leaf index, and radar polarization data. The predicted forest biomass ranged from 32.5-122.5 Mg/ha and carbon stock ranged from 16.5-62.5 Mg C/ha. Some reference
The document outlines a study that uses multispectral drones and ground sampling to collect vegetation data from pasture sites over three sampling periods in June, July, and August. Various vegetation indices will be calculated from the drone and ground spectrometer data to analyze changes in biomass, chlorophyll content, and other vegetation metrics over time. A total of 285 sample points will be collected and various biophysical parameters will be measured at each point to analyze temporal changes in pasture sites.
The Mongolian Geospatial Association has a board that executes the CEO and Secretary. It has 9 technical commissions and 3 member communities. The association has regular, student, institutional, honorary, and advisory members. It partners internationally and participates in activities in countries like the US, UAE, Taiwan, South Korea, and others. In 2021, the association held webinars, talks, workshops and participated in a United Nations workshop on GNSS applications. It celebrates GIS day and holds monthly geo-meetings and quarterly geo-forums.
The document provides an overview of CHCNAV's AlphaUni 300/900/1300 mobile mapping solutions. It describes the key features and performance specifications of the AlphaUni series, including its universal lidar platform design, accuracy levels, data storage capabilities, and compatibility with various installation methods for airborne, vehicle, boat, and backpack use cases. The document also introduces CHC's new BB4 UAV platform as a high-payload professional solution for airborne lidar applications.
Mongolia has been involved in space technology since 1965 under the INTERCOSMOS program. The first satellite data receiving station and weather satellite ground station were established in 1970. In 1981, J. Gurragchaa became the first Mongolian cosmonaut. In 2017, Mazaalai, Mongolia's first satellite, was launched into space.
The Space Technology Association of Mongolia is the main organization related to space technology. It has a board, CEO, secretary and various technical commissions. Members include students, regular members, institutions and honorary members. The association partners with space organizations in countries around the world and participates in international conferences and workshops on space technology.
Mongolia began developing space technology in 1965 under the INTERCOSMOS program. Some key early developments included establishing the first satellite data receiving station in 1970 and a meteorological satellite data station. The first Mongolian cosmonaut launched in 1981. More recently, Mongolia launched its first satellite, Mazaalai, in 2017.
The Association of Mongolian Geodesy and Cartography brings together members involved in fields like photogrammetry, GIS, and surveying. It has over 1500 members across categories like students, institutions, and honorary members. The Association partners with space technology organizations internationally and runs various events and programs.
Demonstration of super map ai gis technology GeoMedeelel
This document demonstrates SuperMap's AI GIS technology. It discusses geospatial deep learning and the AI GIS workflow, including data acquisition and preparation, model building and management, and model application. It provides examples of using deep learning models for tasks like object detection, segmentation, and classification of imagery. The workflow and tools for training models with SuperMap software and deploying trained models as web services are also described. A case study on building extraction is presented to illustrate the full AI GIS process.
Supermap gis 10i(2020) ai gis technology v1.0GeoMedeelel
This document provides information about SuperMap Software Co., Ltd. It includes:
1. Background information on SuperMap such as its founding date and headquarters location.
2. Market share data showing SuperMap has the largest share of the GIS software market in China.
3. An overview of SuperMap's products and technologies including distributed GIS, cross-platform GIS, 3D GIS, big data GIS, and AI GIS.
This document outlines a study to estimate above-ground biomass and carbon stock in boreal forests in Mongolia using satellite data and machine learning. Boreal forests cover about 9.2% of Mongolia but have been declining in recent decades. The study aims to develop a suitable machine learning model to map forest biomass and carbon stock. Random forest was the best performing model with an R2 of 0.24 and RMSE of 33 Mg/ha. Important input features included shortwave infrared band 1, green leaf index, and radar polarization data. The predicted forest biomass ranged from 32.5-122.5 Mg/ha and carbon stock ranged from 16.5-62.5 Mg C/ha. Some reference
The document outlines a study that uses multispectral drones and ground sampling to collect vegetation data from pasture sites over three sampling periods in June, July, and August. Various vegetation indices will be calculated from the drone and ground spectrometer data to analyze changes in biomass, chlorophyll content, and other vegetation metrics over time. A total of 285 sample points will be collected and various biophysical parameters will be measured at each point to analyze temporal changes in pasture sites.
The Mongolian Geospatial Association has a board that executes the CEO and Secretary. It has 9 technical commissions and 3 member communities. The association has regular, student, institutional, honorary, and advisory members. It partners internationally and participates in activities in countries like the US, UAE, Taiwan, South Korea, and others. In 2021, the association held webinars, talks, workshops and participated in a United Nations workshop on GNSS applications. It celebrates GIS day and holds monthly geo-meetings and quarterly geo-forums.
The document provides an overview of CHCNAV's AlphaUni 300/900/1300 mobile mapping solutions. It describes the key features and performance specifications of the AlphaUni series, including its universal lidar platform design, accuracy levels, data storage capabilities, and compatibility with various installation methods for airborne, vehicle, boat, and backpack use cases. The document also introduces CHC's new BB4 UAV platform as a high-payload professional solution for airborne lidar applications.
Mongolia has been involved in space technology since 1965 under the INTERCOSMOS program. The first satellite data receiving station and weather satellite ground station were established in 1970. In 1981, J. Gurragchaa became the first Mongolian cosmonaut. In 2017, Mazaalai, Mongolia's first satellite, was launched into space.
The Space Technology Association of Mongolia is the main organization related to space technology. It has a board, CEO, secretary and various technical commissions. Members include students, regular members, institutions and honorary members. The association partners with space organizations in countries around the world and participates in international conferences and workshops on space technology.
Mongolia began developing space technology in 1965 under the INTERCOSMOS program. Some key early developments included establishing the first satellite data receiving station in 1970 and a meteorological satellite data station. The first Mongolian cosmonaut launched in 1981. More recently, Mongolia launched its first satellite, Mazaalai, in 2017.
The Association of Mongolian Geodesy and Cartography brings together members involved in fields like photogrammetry, GIS, and surveying. It has over 1500 members across categories like students, institutions, and honorary members. The Association partners with space technology organizations internationally and runs various events and programs.
Demonstration of super map ai gis technology GeoMedeelel
This document demonstrates SuperMap's AI GIS technology. It discusses geospatial deep learning and the AI GIS workflow, including data acquisition and preparation, model building and management, and model application. It provides examples of using deep learning models for tasks like object detection, segmentation, and classification of imagery. The workflow and tools for training models with SuperMap software and deploying trained models as web services are also described. A case study on building extraction is presented to illustrate the full AI GIS process.
Supermap gis 10i(2020) ai gis technology v1.0GeoMedeelel
This document provides information about SuperMap Software Co., Ltd. It includes:
1. Background information on SuperMap such as its founding date and headquarters location.
2. Market share data showing SuperMap has the largest share of the GIS software market in China.
3. An overview of SuperMap's products and technologies including distributed GIS, cross-platform GIS, 3D GIS, big data GIS, and AI GIS.
11. Олон жилийн NOAA/NDVI мэдээгээр
NDVI-ийн өөрчлөлт, 1982-2001
7 – р сар
(0.5 х 0.5 градусын тороор)
SVIi,j = (NDVIi.j – NDVIav)/σi,j Fi,j = a * SVIi,j + b
NDVI – ийн бууралт 69%
12. NDVI - биомассын хоорондын хамаарал
Өндөр уул Хээр
Áèî ì àññ, ã NDVI áà áèî ì àññû í õàì ààðàë
Áèî ì àññ, ã 25 steppe
Хээр
NDVI áà áèî ì àññû í õàì ààðàë Altai mountains (Õýýðèéí á¿ñýýð)
2.5
(ª í äº ð óóëû í á¿ñýýð) Алтайн y = 0.8837x - 41.981
2 З
200 уулархаг R = 0.3727
20 y = 0.0906x - 11.576
а 500
2
R = 0.3656 y = 1.1502x - 68.182
г
2.0
З 160 400 R2 = 0.3603
а 15 в
simulated biomass
simulated biomass
y = 0.0359x - 4.4693
1.5 2
R = 0.2229 г 120 а 300
в 10
р 200
1.0 а
80
б
р 40
5
и 100
0.5 б NDVI о 0
и 0
0
120 140 160 180 200 220 240
м 0 50 100 150 200 250 300
NDVI о
0 40 80 120 160 200
а NDVI
с
0.0 NDVI
м
-5
120 130 140 150 160 170
а с
Ойт хээр с Цөлийн хээр с
Áèî ì àññ, ã
NDVI áà áèî ì àññû í õàì ààðàë forest steppe NDVI áà áèî ì àññû í õàì ààðàë Gobi desert
Цөлийн хээр
Ойт
14 8
(Î éò õýýðèéí á¿ñýýð) (Öº ëèéí õýýðèéí á¿ñýýð)
12 y = 0.0962x - 13.477 7 З
хээр
500 100
y = 1.2634x - 74.039
а
2
y = 0.3031x - 9.9769
З
R = 0.4936 y = 0.0652x - 7.7694
90
R2 = 0.3578
2
10 6 R = 0.3277 R2 = 0.2022
400
г 80
а
simulated biomass
simulated biomass
в
5 70
8
300 60
а
6
г 200
4
р
50
40
в
3
б
4 30
100 20
2
и
а
2 10
0
1
NDVI о 0
р
0 0 50 100 150 200 250 300 0 50 100 150 200
120 140 160 180 200 220 240 NDVI м NDVI
0
-2
а
б
120 140 160 180 200
с
и с
о
Регрессийн тэгшитгэл r с
м
Ойт хээр y = 0.0962x - 13.477 0.702
а
Өндөр уул
с y = 0.0359x - 4.4693 0.451
с
Хээр
с
y = 0.0906x - 11.576 0.605
Цөлийн хээр y = 0.0652x - 7.7694 0.572
Цөл y = 0.01401x – 1.793 0.694