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(Upload on May 15 2019) [ 日本語 | English ]

Relevant issues of geographical information system (地理情報システム)






Mount Usu / Sarobetsu post-mined peatland
From left: Crater basin in 1986 and 2006. Cottongrass / Daylily

[ remote sensing | NDVI | aerial photograph ]

A GIS is a collection of information technology, data, and procedures for collecting, storing, manipulating, analyzing, and presenting maps and descriptive information about features that can be represented on maps (Huxhold & Allan 1995)
Geographic information system (GIS): A computer system, incluidng hardware and softwawre, provides tools for capturing, storing, checking, integrating, manipulating, analyzing and displaying data related to positions.
[ Software for GIS ( FOSS4G )| references ]
索引

Data format (データ型)

Vector data

The data are given by coordinates and the attributes.

Raster data

mesh Larch map
Basic structure of raster data (red mesh). Stem densities of Larix kaempferi in 1963 (left), 1973 (center), and 1990 (right), determined from aerial photographs. (Kondo & Tsuyuzaki 1999)
Most data obtained by remote sensing are recorded by raster.
Overlay
Ex. Geographic database + Landuse + Water surface + Road networks + Buildings + Elevation → Actual world
overlay

Spatial analysis (Geostatistics, 空間統計学)

  1. Dispersion pattern analysis
    Morisita's Iδ index
    Lloyd-Iwao’s m*-m
  2. Spatial autocorrelation analysis
    Ripley’s k-function
    Moran's I
    (Semi-)Variogram
and more

Software for GIS (GISソフトウェア)

FOSS4G, free open source software for geospatial
Disktop GIS: GRASS GIS*, Quantum GIS (QGIS)
WebGIS: OpenLayers
Web mapping: OpenLayers, GeoServer
Spatial database: PostGIS
Library for geospatial data: GDAL/OGR (data transfer)
Web-base platform: MapGuide, OpenSource
Coordinate transformation: PROJ4
Application: pgRouting
* geographic resources analysis support system

Reference pages for WebGIS

FreeGIS, MapServer, DM Solutions Group

Shareware
ArcGIS: The license is supported by Software License Registration System, Hokkaido University
WebGIS
Fusion of internet and GIS → brosing and oeprating the data in the field with handling a terminal of protable GPS

No specific softwares are requred → GIS is operated only by browser through internet

Remote sensing (リモートセンシング)

s.l. The measurement or aquisition of information of some property of an object or phenomenon by a recording device that is not in physical or intimate contact with the object or phenomenon at a distance.

s.s. The technique employs such devices as the camera, lasers, radio frequency receivers, radar systems, sonar, seismographs, gravimeters, mangetometers, and scintillation counters.

Characteristics of sensors

Major attributes of remotely sensed data (McGraw et al. 1998)
Attribute: explanation
Radiometric: Number of shades of grey levels and range of brightness values sensed
 Spectral: Number of wavelength bands and the extent of the electromagnetic spectrum sensed
Spatial: Size of the smallest object that can be resolved and the total area imaged
Temporal: Period between image collection of the same site and total period of image collection
HYDICE (hyperspectral digital imagery collection experiment: resolution = 0.75 m, 210 bands)

LIDAR Lidar or LiDAR (ライダー)

= Light Detection and Ranging or Laser Imaging Detection and Ranging
= Laser radar

usually using UV, visible light and near-infrared light (NIR)

iPhone LiDAR scanner
Time of flight (ToF) → possible to get 3D scanning image

direct ToF (dToF) - low accuracy
indirect ToF (iToF)

Satellite (人工衛星)

Organization

JAXA (Japan Aerospace Exploration Agency)

Formerly NASDA (National Space Development Agency of Japan)
GCOM: Global Change Observation Mission
GCOM-C (GCOM-Climate)

→ SGLI (second generation global imager)

GCOW-W (GCOM-Water)

NASA (National Aeronautics and Space Administration)

MOIDS: FPAR

Applications

↓↑ Climate - global change drivers
↓↑ Ecosystem - ecosystem processes
↓↑ Organisms - biodiversities

NPP and biodiversity vulnerability assessment of ecosystem functions
Process interaction

meteorology ⇔ ecosystem ⇔ organism

Scale interaction

region ⇔ landscape ⇔ plot

NDVI
LAI (leaf area index)
LUE (light-use efficiency) → productivity

APAR (absorbed PAR)
FPAR or fAPAR (fraction of PAR, or fraction of absorbed PAR)

a parameter used in remote sensing and in ecosystem modeling that signifies the portion of PAR used by plants

evapotranspiration
productivity
phenology (seasonality)
animal track

Satellites for ecological study (Satellite ecology)

NOAH
Terra / Aqua

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)
CERES
MISR
MODIS
MOPITT

Sentinel (1/2), launched on 04/2014 (1A), 04/2016 (1B), 06/2015 (2A) and 03/2017 (2B)

MSI (recolution: 10-20 m), etc.
Note: 3 and 5P are for ocean

Table. Comparisons between major satellites for measuring land surface characteristics. obs: observation. wl: wavelength. res: resolution ability. a: National Oceanic and Atmospheric Administration. b: multi-spectrum/panchromatic. c: Terra that loads ASTER. *: IJIS dataset (also handling MODIS and AMSR-E)
satellitecountryrotation
(d)		launch-closediscardobserved
width		sensorobs. wl
(nm)		res
(m)		remarks
SPOT1
SPOT2
SPOT3
SPOT4
SPOT5
SPOT6
SPOT7		 France26
26
26		1986.2
1990.1
1993.9
1998.3
2002.5
2015.9
2014.6		1990.12

1997.11		60 km × 2HRV1
HRV2
HRV3
HRV4		500-590
610-680
790-890
510-730		20
20
20
10
SPOTUSA261998HIVIR/VGT
LANDSAT1
LANDSAT2
LANDSAT3		USA17-181972.7
1975.1
1978.3		1978.1
1982.2
1983.3		185 kmMSS4
MSS5
MSS6
MSS7		500-600
600-700
700-800
800-1100		80
80
80
80		download
LANDSAT4
LANDSAT5		USA16/171982
1984
1993		185 kmTM1
TM2
TM3
TM4
TM5
TM7
TM6		450-520
520-600
630-690
760-900
1550-1750
2080-2350
10.4-12.5 µ		30
30
30
30
30
30
120
LANDSAT7USA161999ETM+500-90015
MOS-1
MOS-1b		Japan231987.2
1990.2		1995.3
1996.4		100 km × 2MESSR1
MESSR2
MESSR3
MESSR4		510-590
610-690
720-800
800-1100		50
50
50
50
IRS-1



IRS-C
IRS-D		 India 22 1988



1995
1997		 146 km LISS1
LISS2
LISS3
LISS4
P
P		 450-520
520-600
630-690
760-900		 72.5
72.5
72.5
72.5
5.8
5.2
NOAA a USA 1/2 1978 2700 km AVHR1
AVHR2
AVHR3
AVHR4		 580-680
725-1100
3550-3930
11.5-12.5 µ		 1.1 km
1.1 km
1.1 km
1.1 km
IKONOS Spaceimaging
→ GeoEye
→ Digitalglobe,
USA		 1999/09/24-
2015/03/31		 11.3 km MS/P b
MS-B
MS-G
MS-R
MS-FR		 526-929
445-516
506-595
632-698
757-853		 0.82-1
3.3-4
3.3-4
3.3-4
3.3-4
QuickBird-2
(QuickBird-1, failure)		 DigitalGlobe,
USA		 2001 16.8 km MS/P
MS-BL
MS-GR
MS-RD
MS-FR		 405-1053
430-545
466-620
590-710
715-918		 0.61
2.44
2.44
2.44
2.44
EOS-AM1 c < 1 1999 2330 km MODIS R/NR 250
ALOS *
ALOS-2
ALOS-3		Japan20062011.540-350
70
70/35		PALSAR
AVNIR2
PRISM
420-890
520-770		7-100
10
2.5
WorldView
WV-1
WV-2
WV-3		USA
2007-present
2009-present
2014-present


ca 1 m
Gaofen (GF-2)China2014-presentP0.8 m
3.2 m

Search files: Image Hunter for price (2022/05)

80-cm IK - $7/km2, 25 km2 min order per date
60-cm QB - $12.25/km2, 25 km2 min
50-cm 4-band WV2 - $12.25/km2, 25 km2 min (several dates support 40-cm at $13.65/km2)
75-cm J1 - $6.28/km2, 25 km2 min
30-cm 4-band WV3 - $15.75/km2, 25 km2 min
50-cm P1 - $6.25/km2, 25 km2 min
1.5-m SPOT - $2.375/km2, 100 km2 min

Launched by:

Europe
USA
Japan

Vegetation index (VI) (植生指数)

An indicator index that is used to evaluate green vegetation.
Green plants absorb photosynthetically active radiation (PAR) for photosynthesis that consists of various wavelengths (see below). We apply the plant characteristics to estimate plant cover. That is NDVI.

NDVI (normalized difference vegetation index) and related parameters

Reflection characteristics of vegetation
High absorption rate: 400-500 nm (blue) & 620-690 nm (red)

= chlorophyll absorption bands

High reflection rate: 720-1200 nm
In the case of LANDSAT/MSS

MSS5: 600-700 nm (red)
MSS7: 800-1100 nm (near-infrared)

Relative vegetation index, RVI = MSS7 / MSS5
Difference vegetation index, DVI = MSS7 - MSS5
Weighted difference vegetation index, DVIW = 2.40 × MSS7 - MSS5
Normalized difference vegetation index, NDVI

= (NIR - red)/(NIR + red) ⇒ range (-1, +1)

NIR = near-infrared

= (MSS7 - MSS5) / (MSS7 + MSS5), in the case of Landsat

Water < 0
Soil ≈ 0
Vegetation = 0.3-1.0

Normalized green-red difference index, NGRDI

= (green - red)/(green + red)

Normalized difference water index, NDWI (Gitelson et al. 2002)

= (NIR - SWIR)/(NIR + SWIR) (Gao 1996)
= (TM3 - TM5)/(TM3 + TM5): using LANDSAT-TM

SWIR = shortwave-infrared

or
= (green - NIR)/(green + NIR) (McFeeters 1996)

Normalized difference soil index, NDSI

= (TM5 - TM4)/(TM5 + TM4): using LANDSAT-TM

Soil-adjusted vegetation index, SAVI (土壌調整植生指数)
When the soil surface is exposed due to low vegetative cover (generally, < 40%), the reflectance of light in the red and near-infrared spectra influence vegetation index. This means taht the comparisons between different soil types are problematic. SAVI is proposed to correct the influence of soil brightness on NDVI.

SAVI = (NIR - RED)/(NIR + RED + L) × (1 + L),
L is the soil brightness correction factor that varies by the amount or cover of green vegetation: in very high vegetation regions, L=0; and in areas with no green vegetation, L=1. Generally, an L=0.5 works well.

GIMMS‐NDVI3g

  • Data continuity AVHRR‐MODIS‐VIIRS
    • Quantify photosynthesis on the land
    • MODIS substantial improvement over AVHRR
    • through scientific analysis, AVHRR and MODIS can be combined
    • VIIRS follow‐on instrument (NPP – Sep 2011)
  • Comprehensive analysis of NDVI trends
Requirements of NDVI data for monitoring vegetation dynamics
  • Reliable sources of consistent long time series data: the continuum of AVHRR and MODIS data provide a nearly 30‐year product of global land photosynthesis
  • Effective temporal and spatial scales: bimonthly 8 km data
  • Repetitive automated measurements: data updated every quarter
  • Continuity: methodology can be adapted to add VIIRS into the continuum
Normalized burn ratio, NBR
proposed for highlighting burned areas and estimating fire severity

NBR = (NIR - SWIR)/(NIR + SWIR)
the equation is the same with the equation of NDWI

application: estimating burn severity (BS) ⇒

BS = dNBR or ΔNBR = NBRpre-fire - NBRpost-fire

Integrated Forest Index, IFI (Huang et al. 2008)

IFIp = √[(1/NB)·Σi=1NB{(bpi - b_i)/SDi}2],
where b_i and SDi are the mean and standard deviation of selected forest training pixels for band i, bpi is the band i spectral value for pixel p, and NB is the number of spectral bands

Composite Burn Index, CBI (Key & Benson 2006)
Normalized difference red edge index (正規化レッドエッジ指数), NDRE

= (NIR - RE)/(NIR + RE), where RE = red edge (≈ 715 nm)

The indexes shown below can be calculated by RGB
Green red vegetation index, GRVI

= (G - R)/(G + R)

Visible atmospherically resistant index, VARI

= (G - R)/(G + R - B)

Red-green-blue vegetation index, RGBVI (赤緑青植生指数)

= (G2 - R×B)/(G2 + R×B)

Others: TGI, SIPI2, LCI, BNDVI, GNDVI, MCARI, etc.

Aerial photograph (航空写真)

How to get aerial photographs
See Japanese page

UAV, Unmanned aerial vehicle (ドローン)

= RPAS (remote piloted aircraft systems) by ICAO
= UAS (unmanned aircraft systems) by FAA (Federal Aviation Administration of USA)

Ex. DJI Mavic 2 Pro (2019)

SfM
= structure from motion
GNSS
= global navigation satellite system

Geostatistics (地統計学)

= spatial statistics → spatial analysis

Spatial model (空間モデル)

Advantages: fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology

Spatially explicit model (SEM, 空間明示モデル)

Imagery analysis (画像解析)

Color image

color
Additive and subtractive color combinations

additive (light) vs subtractive (paint)

(Schneider, et al. 2012)

ImageJ (ImageJ)

Fiji
ImageJ2

References (参考)

Remote Sensing

Recently, RS is tightly connected with GIS. Distinction between them is often useless.
EVI
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