Presentation on theme: "Near Realtime Forest Cover Monitoring In Ap BY DR HC MISHRA,IFS APCCF (GIS),AP."— Presentation transcript:
Near Realtime Forest Cover Monitoring In Ap BY DR HC MISHRA,IFS APCCF (GIS),AP
INTRODUCTION Andhra Pradesh is the fourth largest state in India geographical area wise with an area of 2,75,045 square Kilometers. Number of Forest Blocks in Andhra Pradesh State is about Total area of Reserved forest in Andhra Pradesh is km 2. Andhra Pradesh lies between latitudes of ’ and ’ N and longitudes of ’ and ’ E in the south east coast of the country. Geographically the Andhra Pradesh state has a hilly region in the Eastern Ghats along coast apart from hilly tracts of Nallamallais and Erramallais in Rayalseema, a plain region in the coast and plateau in the Telengana region with an average height of 100 to 1000 meters over MSL. Godavari and Krishna are the two principal rivers flowing from west to east in the Andhra Pradesh state. Southwest monsoon is the principal monsoon in the Andhra Pradesh state though north east monsoon also causes rainfall in November – December in Nellore, Chittoor and Cuddapah districts. The average rainfall in the Andhra Pradesh state is 800 – 1200 mm.
History of Forest Cover Monitoring
Classes for Previous Classification ClassDescription Dense forest> 0.4 Canopy density Open forest Canopy density Scrub forest < 0.1 Canopy density and areas with dwarf & stunted vegetation growth Blanks/OthersAreas devoid of vegetation, fallows, etc. Water Bodies Streams, ponds and lakes, waterlogged areas etc. Mangroves Land covered with mangrove vegetation irrespective of density.
New classes for Classification ClassDescription Very Dense Forest>0.70% canopy Density Moderately Dense Forest 0.40% to 0.70% canopy Density Open Forest0.10% to 0.40% canopy Density Scrub Forest<0.10% canopy Density Non ForestAreas devoid of vegetation, Fallow lands Water bodies Streams,ponds and lakes, waterlogged areas etc.
Satellite data download Geometrical rectification Radiometric Normalization SOI toposheets 1:50,000 scale Masking out non- forest areas Ground reference data NDVI Transformation Density slicing Editing Preparation of change map by Erdas matrix tool and by comparing 2007 and 2008 FCCs visually Making sub-sets of scene Accuracy Assessment Over lay boundaries Post classification correction Area statistics Ground truthing Maps New Methodology in 2007
Before Normalization After Normalization IRS P6 LISS III 29 th October 2008 Imagery Normalized with respect to IRS P6 LISS III 22 nd December 2007 Imagery
Change Matrix of Khammam District 2007( ) and 2008( ) (IRS P6 LISS III Imagery)
2007 (Data of Oct 2007 – Feb 2008) 2008 (Data of Oct 2008 – Feb 2009) Total of 2007 VDFMDFOFScrubNFWater Very Dense Forest Moderately Dense Forest Open Forest Scrub Non-Forest Water Total of Net Change Forest cover Change Matrix of
Accuracy Matrix of 2007 Classification AS PER FIELD AS PER CLASSIFIED CLASSES Very Dense Forest Moderate Dense Forest Open Forest Scrub Non - Fore st WaterTotal Very Dense Forest Moderately Dense Forest Open Forest Scrub Non-Forest Water Total Accuracy percentage is 84.63
RESULTS AND ANALYSIS 1.Total change points found in the state between 2007 and 2008 are Total net change is –107 km 2 during Positive change is 753 Hectares & negative change is 115 km 2. 3.Epicentre of -ve change is Khammam, where the net change is Ha. 4.Second circle with maximum destructions is Rajahmundry with Ha. 5.Ananthapur with -13 Ha has lowest negative change. 6.The –ve change in VSS areas is Hectares. Many VSS have cut down their own areas and some their adjacent areas. 7.In wildlife areas –ve change is -191 Hectares. 8.Paloncha forest division showed maximum negative change in the state of about -2200hacteres.
Limitations of Technology Considerable details on ground could be obscured in areas having clouds and shadows. It is difficult to interpret such areas without the help of collateral data. Young plantations and species having less chlorophyII content in their crown, do not give proper reflectance and as a result correct interpretation of such areas becomes difficult. Since resolution of data from Liss-III is 23.5m, smaller areas below 0.5 hectare cannot be captured. Variation in spectral response pattern poses problems in interpretation. Chameleon type scenerio of Decidous mixed forests often cheats the interpreter. Gregarious occurrence of bushy vegetation like Lantana and certain agricultural crops, such as sugarcane, cotton, etc., often pose problems in delineation of forest cover as their spectral response pattern is similar to that of tree canopy.
Nirmal Division Lat: ; Long: Comp. No:948 Division: Nirmal An area of 3 ha was converted in to MDF to NF Date of visit
IRS P6 LISS III Imagery 07-NOV-2008 IRS P6 LISS III Imagery 13-NOV-2007
IRS P6 LISS III Imagery 13-NOV-2007 IRS P6 LISS III Imagery 07-NOV-2008
KARIMNAGAR EAST Latitude : Longitude: ha of area converted from Open to NF Comp.No:296 Division: Karim nagar(E) Date of visit: During Dec.2009
KARIMNAGAR EAST DIVISION
SRIKAKULAM 2.5 ha Encroachment at Kadaganti (E) Beat, Longitude : Latitude : Comp.No:151 Division: Srikakulam Date of visit: 1 st and 2 nd of Dec. 2009
DivisionNarsipatna m RangeNarsipatna m BeatVedurupalli Comp1258,1259 Area-ha3.5 Latitude Longitude Datum:- Indian Bangladesh Liss III 08 November 2008 Liss III 14 November 2007
MADKURU APCFM VSS-KHAMAM DVN Compt no. 95,Talada Range
Regulukunta VSS, JR Gudem Range, West godavari
NUZVID RANGE,KRISHNA DIVISION (Compt- 123) N, E (I-B)
Sl NoDOsDON’Ts 1 Develop all databases in Geographic Lat/Long in WGS 84 datum. Avoid all other datums. 2 Concentrate on Remote Sensing based raster GIS as it is crucial for Forestry applications. Don’t ignore Raster GIS 3 Move from 2D Remote Sensing to 3D Remote Sensing. Move slowly to high resolution data. (Both 2D and 3D are desirable) Don’t be complacent with low resolution images. 4 Create a solid layer of Forest blocks by DGPS survey preferably of Dual Frequency. Don’t ignore survey of forest blocks 5 Move from desktop GIS to Enterprise GIS. A desired combination is required. Don’t be complacent with few standalone desktops. 6Develop your own Servers and have good bandwidth up to FRO level Don’t depend upon outside servers.
7 Never neglect Data Mining from day one. Preserve original and derived data in neutral formats and predetermined nomenclatures systematically in perfect media in 2-3 places in Geographic coordinates Never ignore this aspect. 8 Preserve all Metadata of the data. Avoid digitization from photocopied hard sheets, old torn papers, topo sheet forest block layers (except initially), Data of unknown datum and if done maintain the metadata information. Never forget which is garbage and which is reliable. 9 Go for a Technical Advisory Team for the State GIS. Buy hardware and software as per its advice and pursue practical user required GIS as per its advice. Rely on joint wisdom of many;rather than depending on oneself. 10 Develop library of control points as required for 3D GIS from Dual Frequency DGPS Don’t ignore it. 11 Have a Quality Control officer who may be the senior most in the wing. Always go for accuracy check before concluding a work. Put all modeling works to quality strict validation. Call a spade a spade, a garbage as garbage ie ruthlessly honest in this aspect Don’t finish a task without Quality control or accuracy check. 12Maintain hardware, Software and most importantly Livewire. Update all the three by frequent interactions, seminars and, workshops. Concentrate here and never ignore this aspect.
CONCLUSIONS 1.There is decline of forest cover in AP from the year 1999 till 2007 as per FSI reports. As per APFD’s own analysis we find loss of forest cover during , and The EYE IN THE SKY is highly reliable and gives accurate forest cover changes like deforestation and greenness very reliably. It can not be hidden by anybody. 3.VSS areas, Wild life areas, APFDC areas are not spared by the encroachers. 4. Let us THINK, ACT and not be COMPLACENT.