1 Use of High Resolution Satellite Images for Agricultural Land Use Assessment in Romania Dr. G. STANCALIE, Dr. A. MARICA, Dr. E. SAVIN, S.CATANA, C. FLUERARU.

11

Use of High Resolution Satellite Use of High Resolution Satellite Images for Agricultural Land Use Images for Agricultural Land Use

Assessment in RomaniaAssessment in Romania

Dr. G. STANCALIE, Dr. Dr. G. STANCALIE, Dr. AA.. MARICA MARICA, ,

Dr. E. SAVIN, Dr. E. SAVIN, SS.CATANA, C. FLUERARU.CATANA, C. FLUERARU

National Meteorological AdministrationNational Meteorological Administration

Bucharest, RomaniaBucharest, Romania

Workshop on Climatic Analysis and Mapping for Agriculture, Bologna, Italy, 14-17 June 2005

22

Presentation outlookPresentation outlook

Introduction The Present Romanian Agrometeorological Monitoring System The Improved Integrated Support System for the Agrometeorological Warning and the Identification of the Areas with Agricultural Risk in Romania

Useful Satellite Sensors for Agrometeorological Monitoring Satellite – Derived Information for Agricultural Monitoring

The Land Cover/Land Use Mapping General Methods for the Generation of the Land Use Map Spatial Resolution Enhancement by Fusion Procedure Multispectral Classification

o Unsupervised vs Supervised Classifications for the land use mappingo Improving Classification Using Multi-temporal Images & Multi-sensor Images

Statistic Validation of the Land Use Classes Land use mapping based on TERRA/ASTER data

Conclusions

33

IntroductionIntroduction

The agrometeorological activity undergoing within National Meteorological Administration (NMA), integrates complex issues concerning the current and future evolution of the vegetation state of the crops and water supply of soils with respect to the meteorological parameters evolution, being a particularly important activity whose final objective is to elaborate/edit the agrometeorological bulletins and disseminate information at the level of the decision making factors in agriculture and private farmers.Important infrastructure modernization achievements have been accomplished by the implementation of the National Integrated Meteorological System (SIMIN project) and the development trend of the Romanian agrometeorological monitoring system. Since orbital sensing technologies have undergone unprecedented development, the use of multispectral satellite data in conjunction with traditional means is able to ensure the improvement of the classical determination methods of the agrometeorological parameters, greatly contributing to improve management of agrometeorological phenomena, like the drought.

There is a need for accurate agricultural land use assessment; in this respect land use classifications derived from high resolution satellite data proved to be valuable products.

44

The Present Romanian Agrometeorological Monitoring SystemThe Present Romanian Agrometeorological Monitoring System

Processed data (outputs):

AGROMONITORING Module of reception, selection and extraction of meteorological data for interested stations and parameters (Synop Programme) Module of high processing of data on information structures (air temperatures, soil temperatures precipitations, evapotranspiration, soil humidity); Module of data storing and manipulation used as input for agrometeorological models

- Agrometeorological indices (thermal and hydric)- Monotoring of the vegetation state of the crops - Dynamic of the soil humidity (deficits/excess)- Potential and real evapotranspiration - Elaboration/editing agrometeorological bulletins

ServerAgrometeorological

database

Agrometeorological stations

- Primary data:- Agro-meteo observations and measurements. - Automatic data pick-up - Data primary processing

Meteorological stations Sinoptic/Automatic

- Air temperatures min./max. - Precipitations, - Relative humidity- Sunshine duration- Wind speed

Real-time monitoring of meteorological and agrometeorological

parameters

ServerMeteorological database

(Oracle)

DISEMINATION

55

The Improved Integrated Support System for the The Improved Integrated Support System for the Agrometeorological Warning and the Identification of the Agrometeorological Warning and the Identification of the

Areas with Agricultural Risk in RomaniaAreas with Agricultural Risk in Romania

In the last period an important modernization of the Romanian meteorological infrastructure, based on high technology, has been achieved by the implementation of the National Integrated Meteorological System (SIMIN project), conceived as an integrated system, receiving and processing the data coming from multiple observation sub-systems (surface, radar, satellite sub-systems, etc.) and a lot of types of equipment. Within this project several systems with major significance have been provided: meteorological DOPPLER Radar Network, automatic Weather Network, lightning detection network, satellite reception stations for Meteosat Second Generation, telecommunication networks, visualization system (Nex-REAP) integrating all available information, etc.

The main benefit of this investment is relevant for weather forecasting, allowing the detection, surveillance and anticipation of severe meteorological phenomena, which have also an important impact on the agriculture. SIMIN includes a quasi-real time dissemination components of the meteorological information, so that the local authorities and the other users could be promptly informed about the occurrence of severe meteorological events, being able to take, in time, the necessary steps to avoid disasters and damages.

66

Interoperable System for MeteorologyInteroperable System for Meteorology

DATA PROCESSING

DECISION PRODUCTS ELABORATION

IP TELECONF

COF

DATA PROCESSING DECISION PRODUCTS

ELABORATIONTC

TC

DATA BASE and CLIMATOLOGY

RFC

-METCOMPUTEData processing and

data storage

USERS:Strategicprivate

Automated weather stationsand actual weather reports

Automated weather stations

Dedicated automaticagro-meteostations

Automated air qualitystations (fixed and mobile)

TC

TC

MODELING

TC

TC

SIMIN

INTEROPERATE

ISS CLIMA

ISS AGRO

END PRODUCTSWarnings & ForecastsStudiesRisk mapping

MET TRAINExcellence training Center Workshop, seminars, conferences at national and international level

LEGENDRFC – Reg.forecast centerCOF – Center Operating FacilitiesTC – Communication terminalISS – Integrated Support System

Satellite multi-mission receiving stationNOAA-AVHRR

SPOT-VEGETATIONTERRA-MODIS

77

The Information Flowchart of the Integrated Support System The Information Flowchart of the Integrated Support System for Agrometeorological Warning and Identification of for Agrometeorological Warning and Identification of

Agricultural Hazard AreasAgricultural Hazard Areas

Satellite multi-mission receiving stationNOAA-AVHRRSPOT-VEGETATIONTERRA-MODIS

Agrometeorological Stations (fixed and mobile)

Automatic meteorological

stations

Regional Meteorological Centers

Subsystem for the management of the agrometeorological database

- Main function: data storage, analyzing and data updating. - The input data for the analysis procedures will allow to determine a certain number of hazard indicators of the territory in various agro-climatic conditions.

ServerUsers- strategic - private

Subsystem for the management of the thematic, cartographic database

-The GIS info-plans and the required information to a structural analysis of the territory.

-The GIS database can be personalized and completed through introducing new layers in view to update meta-data associated to each thematic info-plan.

Subsystem for displaying the structural vulnerability- Better knowledge of the agroclimatic vulnerability structure;- Crisis management and long term planning having in view the definition of the vulnerability context and its dynamics at various spatial scales (regional, national);- Elaborated results in map-drawing format.

88

Useful Satellite Sensors for Agrometeorological Monitoring

NOAA-AVHRR (optical, 4 or 5 channel, broad band scanner, visible to thermal IR, 2400 km swath, 1 km at nadir);

Terra/Aqua-MODIS (optical, 250 m, 1 Km, 1330 km swath, 36 spectral bands: an imaging spectrometer, sees every point on Earth every 1-2 days);

SPOT/VEGETATION (Optical, 1km, broad band scanner, visible to near IR, spectral bands, synthesis 10-days products);

MED

IUM

R

ESO

LUTIO

N

Landsat-ETM+ 7 spectral bands: 3 – visible, 2 - near IR, 1 – medium IR, 1 – thermal IR, spatial resolution– 30 m, 15 m (PAN only), temporal resolution – 8 days, swath width -185 km; SPOT 4 Pan and 4 spectral bands: ground resolution: PAN - 10m, green, red, near IR, medium IR – 20m, scene size 60 x 60 Km2; SPOT 5 Pan and 4 spectral bands: ground resolution: PAN - 2.5 & 5m, green, red,near IR, medium IR – 10m, scene size 60 x 60 Km2; Terra/Aqua-ASTER (optical, 10 m, 14 channels, swath of 60 km at nadir, spatial resolution: VNIR-15 m,  SWIR-30 m, TIR- 90 m). IRS LISS MODE: 5 spectral bands: 2- visible (green, red), 2 – near and mean IR, 1 bleu synthetic, spatial resolution – 25 m; PAN MODE: 1 spectral band – visible, spatial resolution - 5 m

HIG

H R

ES

OLU

TIO

N

99

Satellite – Derived Information for Satellite – Derived Information for Agricultural MonitoringAgricultural Monitoring

Assimilation of remotely sensed data into numerical prediction models (e.g. SWAT, crop models)

Prediction Monitoring and early warning Assessment of impacts for extreme meteo events

Land use type Intensity and areal extent Use of satellite data as input for crop model yield estimates.

Earth Observations from satellites are highly complementary to those collected by in-situ systems.

Satellites are often necessary for the provision of synoptic, wide-area coverage and provision of the frequent information required to put in-situ information into broader spatial monitoring of drought conditions.

CRAIOVA

00.5

11.5

22.5

33.5

44.5

55.5

ET

c (m

m/d

ay)

-50

-40

-30-20

-10

0

10

2030

40

50

Re

l. e

rro

r (%

)

ETc(CROPWAT)

ETc(RS)

Rel. error (%).

1010

Satellite – Derived Information for Satellite – Derived Information for AgrometeorologyAgrometeorology

The geo-referenced information, obtained from optical and radar images could be used in determination of certain parameters required for agrometeorology:land surface temperature; evapotranspiration;land (vegetation) albedo;soil moisture;snow cover;fraction of green vegetation, vegetation state;biophysical crop parameters (LAI, FPAR, etc.)

land cover

land use

1111

The Land Cover/Land Use MappingThe Land Cover/Land Use Mapping

Requirements for the achievement of the land coverRequirements for the achievement of the land cover//land use from high land use from high resolution images:resolution images:

The structure of this type of information must be at the same time The structure of this type of information must be at the same time cartographic and statistic;cartographic and statistic;

It must be suited to be produced at various scales, so as to supply It must be suited to be produced at various scales, so as to supply answers adapted to the different decision making levels;answers adapted to the different decision making levels;

Up-dating of this piece of information must be performed fast and easily.Up-dating of this piece of information must be performed fast and easily.

The developed methodology implies the following main stages:The developed methodology implies the following main stages: Preliminary activities for data organizing and selection;Preliminary activities for data organizing and selection; Computer-assisted photo-interpretation and quality control of the obtained Computer-assisted photo-interpretation and quality control of the obtained

results;results; Vectorisation of the obtained maps (optional);Vectorisation of the obtained maps (optional); Database validation at the level of the studied geographic area;Database validation at the level of the studied geographic area; Obtaining the final documents, in cartographic, statistic and tabular form.Obtaining the final documents, in cartographic, statistic and tabular form.

1212

Satelite Data Processing and Satelite Data Processing and AnalysisAnalysis

Optical satellite data (LANDSAT–ETM, IRS–PAN/LISS, SPOT-Optical satellite data (LANDSAT–ETM, IRS–PAN/LISS, SPOT-AN/XS, ASTER) have been used to perform the analysis for AN/XS, ASTER) have been used to perform the analysis for land use inventory purposes.land use inventory purposes.

A series of specific processing operations for the images were A series of specific processing operations for the images were performed, using the ERDAS Imagine and ENVI softwares: performed, using the ERDAS Imagine and ENVI softwares:

Geometric correction and geo-referencing in different map Geometric correction and geo-referencing in different map projection system; projection system;

Image improvement (contrast enhancing, slicking, selective Image improvement (contrast enhancing, slicking, selective contrast, combinations between spectral bands, re-contrast, combinations between spectral bands, re-sampling operation); sampling operation);

Classifications and grouping;Classifications and grouping; Statistic analyses (for the characterization of classes, the Statistic analyses (for the characterization of classes, the

selection of the instructing samples, conceiving selection of the instructing samples, conceiving classifications).classifications).

1313

General Methods for the General Methods for the Generation of the Land Use MapGeneration of the Land Use Map

M anualprocessing

channel

VHRm ultispectral

im age product

M anualprocessing channel

M erging ofim age products

VHR PAN+

HR m ultispectralim age products

Sem i-autom aticprocessing channel

M erging ofim age products

HR PAN+

HR m ultispectralim age products

Sem i-autom aticprocessing channel

H Rm ultispectral

im age product

Land use map

Satellite data: SPOT, IRS, LANDSAT, ASTER

1414

Semi-automatic Generation of the Semi-automatic Generation of the Land Use MapLand Use Map

Topographic maps

Topographic maps

Hydrographic network Administrative boundaries

Available land usein situ data

IRS PAN+ LISSSPOT PAN +XS

IRS PAN+LANDSAT ETMSPOT PAN + ASTER

HRimage product

Geometric correction Subset/Mosaic

Radiometric enhancement

Pre-processed HR image product

InterpretationClassifications

Map editing

Land use map

1515

Spatial Resolution EnhancementSpatial Resolution Enhancement by by Fusion ProcedureFusion Procedure

(Band substitution +IHS transformation)(Band substitution +IHS transformation)

Resam pled H Rimage product

RGB to IHStransformation

Resam pling

Geocoded H RImage product

Geometric correction

Multispectral H RImage product

(SPOT XS, LANDSAT TMIRS LISS, IKONOS XS)

Matched HR/VH Rimage product

H istogram m atching

Geocoded HR/VH Rimage product

Geometric correction

PAN HR/VHRimage product

(SPOT PAN, LANDSAT TM,IRS PAN, IKONOS PAN, KVR)

Multispectral mergedimage product

IHS to RGB transform

Multispectral mergedimage product(IHS system )

Substitution o fIntensity channe l

1616

Exemple of fusion for IRS imagesExemple of fusion for IRS images

IRS Pan ImageFine geometrical resolution

(5 m)

IRS Pan+LISS fusion image (5m)

Advantages:Fine geometrical resolution

Rich multispectral information

IRS LISS image Rich multispectral information (25 m)

1717

Statistics of the Land Use Classes

3% 4% 2%

41%

8%1%11%

1%

29%

culturi agro (deprimavara)culturi agro (detoamna)teren agricol(necultivat)padure

sol nud + terendegradatapa

zone locuite

constructii

pasuni, vii, livezi

Land use classes Surface(km2)winter crop 42.0summer crop

57.8 uncultivated soil

24.9

forest 590.2bare soil 121.7water 10.7villages 152.2constructions

19.6pastures, vineyards 424.2Total 1443.3

IRS derived land use map

(Arges basin)

1818

Multispectral ClassificationMultispectral Classification

• Pixel based classification Pixel based classification Non-supervised classification – “n” classes Regrouping – following interpretation rules Emphases cultivated zones Supervised classification based on training areas Regrouping

• Parcel based classificationParcel based classification Uses neo-channels (derived from PAN) : texture and variance Non-supervised classification based on dynamic clusters (mobile centers) Emphases cultivated areas vs. urban zones Regrouping

• Combination of the two type of classificationsCombination of the two type of classifications

• Image segmentationImage segmentation in order to identify homogenous areas (parcels) for radiometric and textural point of view.

1919

The result of pixels based classification

The result of area based classification

Result of the combined classifications

Winter cropsSummer cropsPastures, vineyardsUrban zonesUrban zones (big building)

wateruncultivatedforestBare soil

Land use/cover mapLand use/cover map

2020

IRS PAN/LISS - DIRS PAN/LISS - Derivederived LLand Useand Use

Western Romanian Plain (Crisul Alb basin)

2121

Statistic Validation of the Land Use Classes

Histograms associated to land use classes in the radiometric channels of the IRS data

Classes discrimination test using the separability cell array

2222

Land use mapping based on TERRA/ASTER data

1) Geo-referencing of the ASTER dataThe images have been co-registered to WGS84 datum and UTM projection zone 34 and rotated with different angles in order to co-locate and analyzed with topographic maps.

2) Detection of cloud and water Water, clouds and cloud shadows have been separated and the land patterns have been emphasized for the classification procedures.

3) Data classification proceduresUnsupervised classification: find the right number of classes with a specified number of iterations.Supervised classification: based on training areas using a priori knowledge of the number of classes, as well as knowledge concerning statistical aspects of the classes. Regrouping

4) CorrectionAfter classifications, corrections have been applied in order to eliminate the isolated pixels.

5)Validation of results Validation included checking methods based on “in situ“sampling focused on the classification precision, the exactness of the geographic boundaries and the homogeneousness of the occupation structure.

6) Integration in a GIS environment allowing obtaining thematic maps at different scales.

2323ASTER satellite image, 15 m resolution, Crisul Alb basin

(subset zone)

Un-supervised classification, raster format

Un-supervised classification converted in vector format

Land Use Obtained by Un-supervised Classification of TERRA/ASTER Data

2424

Land Use Obtained by Un-supervised Classification of TERRA/ASTER Data (cont.)

A – image resulted after Conversion Raster to Vector

A B

B – image resulted after applying a Majority Filter (Neighboroght Statistics)

In order to include the pixels that belong to very small polygons into the land use units a special procedure has been applied using the Neighbourhood Statistics function from Arc Map software.

The result was a simplified classification in raster format.

This raster was converted in vector format by Convert Raster to Features (GRID) of Arc Map software.

2525

Supervised classification in 7 land use classes + “urban”class

urban

ASTER image – color composite

Land Use Obtained from Supervised Classification Based on Training Areas

2626

Unsupervised vs Unsupervised vs Supervised Supervised

Classifications for the Classifications for the land use mappingland use mapping

Unsupervised classification: 7 classes + urban obtained by merging 50 classes

ASTER image

2727

Land Use Obtained from TERRA/ASTER

Data (Detail)

2828

Improving Classification UsingImproving Classification Using

Multi-temporal Images Multi-temporal Images

In classifying satellite data different land use show similar spectral signatures during the vegetation period. This is especially true for grassland of different use and cereals of different stages of maturity.

Due to the presence of different plant communities and varying forms of cultivation grassland can in general not be separated from cereals in the feature space using only one image.

The spectral differences within grassland could be higher than the differences between grassland and cereals or other crops.

Therefore multi-temporal images should be used to improve separability.

2929

Use of Multi-temporal & Multi-sensor Images for Crop Identifications

SPOT4 XS 18 April 2000 IRS-LISS 4 August 2000

3030

Use of Multi-temporal & Multi-sensor Images for Crop Identifications

ASTER – September .2003 SPOT 4 – April 2000 IRS-LISS – August 2000

3131

ConclusionsConclusions

The new investments in the Romanian meteorological integrated system will contribute to improving the monitoring system of the agrometeorological parameters, using highly efficient methodologies and techniques (mathematical modeling, GIS, remote sensing), in order to evaluate the vegetation state of the crops, of the moisture and soil water deficit dynamics, for the optimization of the agricultural management.The results, such as the agrometeorological bulletins (diagnosis and forecasts) will be disseminate via Internet at different customers including the decision making factors in agriculture, extension services, insurance companies, farmers, media, etc.The high resolution satellite data proved to be useful for the agricultural land use assessment being tailored to multiscale requirements. Covering wide areas satellite imagery comes in a full range of resolutions from 20 m down to 2.5 m, for work on regional or local scales

(from 1:100 000 to 1:10 000). These images are also useful for observing and analysing the evolution of land surfaces to try and understand changes affecting vast areas or precise locations.