Data Analysis On Tree Cover Loss In India

Data Analysis On Tree Cover Loss In India

Bhavana C.R1.

1PG Scholar,dept.of MCA Dayanand Sagar College of Engineering

Bangalore,India bhavanachitraghar80097@gmail.com

Mahendra Kumar 2.

2 Associate Proffessor,dept.of MCA Dayanand Sagar College of Engineering Bangalore,India

mahendra-mcavtu@dayanandasagar.edu

AbstractTr covr loss is a world nvironmntal issu with extensive consquncs for nvironment, climat and human welfare. It covrs sustainabl nvironmnt and convrsion of land for various purposs such as agricultur, urbanization and infrastructur tc. An ovrviw of dforstation, including causs, consquncs and solutions including th possibility. Th rasons for dforstation ar multifactd and vary from plac to plac. Ths includ agricultural xpansion, logging for timbr and fulwood, infrastructur, mining and population growth. Ths drivrs oftn intract with socio-conomic factors, policy framworks and govrnanc issus, intnsifying forst loss. Th impacts of dforstation ar varid and widsprad. Loss of forsts lads to biodivrsity dclin, biodivrsity dgradation, and habitat loss for countlss spcis of plants and animals. Tree Cover Loss also combines to climat chang, as forestland plays an important rol in saving carbon and monitoring of th watr cycl. Furthrmor, dforstation advrsly affcts local communitis and tribal communitis that dpnd on forsts for thir livlihoods and cultural idntitis. Addrssing dforstation rquirs a multi-prongd approach including stratgic planning, sustainabl land managmnt, and community involvmnt Effctiv stratgis includ providing agricultur sustainability, rforstation and dforstation policis, strngthning land rights and nvironmntal lgislation. Intrnational coopration and financial mchanisms, can also play a crucial rol in incntivizing forst consrvation. It poss significant challngs to global sustainability and ncssitats urgnt action. By undrstanding th causs, impacts, and potntial solutions, w can work towards mitigating dforstation and promoting sustainabl forst managmnt practics. It rquirs collaborativ fforts from govrnmnts, civil socity, and th privat sctor ,nsuring th prsrvation of biodivrsity, climat rgulation, and th wll-bing of prsnt and futur gnrations.

Keywords- Tree cover loss, Tree Cover loss, Forest degradation, India, Biodiversity loss.

1. INTRODUCTION

   India, with its divrs cosystms and vast forstd aras, plays a pivotal rol in global biodivrsity consrvation and climat chang mitigation fforts. Howvr, dforstation has mrgd a significant challng, thratning th country's cological balanc and th livlihoods of millions of popl who rly on forst rsourcs. Rapid urbanization, agricultural xpansion, infrastructur dvlopmnt, and logging activitis hav all contributd to th acclratd loss of tr covr across th country. In rcnt yars, advancs in rmot snsing tchnology and th availability of larg-scal datasts hav opnd nw avnus for studying dforstation from a data-drivn prspctiv. Forsts and trs play an important rol in maintaining cological balanc, biodivrsity consrvation, climat control and livlihood opportunitis. Employing advancd data analysis tchniqus, such as machin larning algorithms, spatial analysis, and tim sris

   analysis, th rsarch rvals insights into th spatial distribution of tr covr loss, tmporal trnds, and ky dforstation drivrs, including agricultural xpansion, infrastructur dvlopmnt, and logging. findings srv as a basis for futur rsarch and policy dvlopmnt, mphasizing th ncssity for nhancd monitoring and nforcmnt, as wll as th promotion of sustainabl land us practics in India. Th study rvals concrning rats of tr covr loss throughout India, with significant variability btwn various stats and cologis. Scintists hav sn a major dclin of forst covr in vulnrabl rgions lik th Cntral Indian Forstd Ara, th Eastrn Himalayas, and th Wstrn Ghats. Th approach also mphasiss th importanc of conomic forcs, population xpansion, and policy influncs in promoting dforstation procsss. This study lvrags a larg-scal datast of tr covr loss across India to analyz trnds, pattrns, and drivrs of dforstation, with th goal of informing th dvlopmnt of targtd stratgis for forst consrvation and sustainabl land us.

2. REVIEW OF LITERATURE

   1. "Prdictiv Modling of Tr Covr Loss in India Using Machin Larning" (Authors: Kumar, V., Vrma, S., & Jain, S.)Th study usd machin larning algorithms to dvlop prdictiv modls for tr covr loss in India. Th authors combind satllit imagry, climat d ata, and socioconomic variabls to train th modls. Th study dmonstratd th ability of machin larning to prdict futur tr covr and highlightd th importanc of incorporating dynamic variabls into th modling procss.

   2. "Spatial Analysis of Urban Tr Covr Loss in Indian Citis" (Authors: Singh, R., Chakraborty, T., & Joshi, P.) Th study focusd on th analysis of deforestation in Indian citis. Th authors usd high-rsolution satllit imagry and GIS tchniqus to assss spatial distribution and causs of urban tr covr loss. Th study highlightd th importanc of urban grn spacs for urban rsilinc and highlights th nd for activ planning and managmnt of urban forsts.

   3. Analyzing th Impact of Land Us Chang on Tr Covr Loss in India" (Authors: Sharma, R., Singh, S., & Patl, N.) Th papr xamins th impact of land cultivation chang on deforestation in India. Th authors analyz satllit imagry, land cultivation data, and socioconomic indicators to dtrmin th rlationship btwn land us chang and deforestation. Th study highlights th importanc of sustainabl land managmnt practics ar dvlopd to accntuat loss of tr covr.

   4. "Prdictiv Modling of Tr Covr Loss in India Using Machin Larning Approachs" (Authors: Kumar, V., Vrma, S., & Jain, S.)

      Th papr xplors th us of machin larning tchniqus for prdictiv modling of tr covr loss in India. Th authors us satllit imagry, climat data, and socioconomic variabls to dvlop modls that can prdict futur tr covr. Th study highlights th potntial of machin larning to prdict and prvnt tr covr loss.

   5. "Assssmnt of Drivrs and Pattrns of Tr Covr Loss in India" (Authors: Gupta, R., Sharma, A., & Singh, S.)

      Th papr considrs th causs and pattrns of deforestation in India. Authors analyz satllit imagry, climat data, and socio-conomic factors to idntify ky drivrs of deforestation. Th study provids insights into th spatial distribution and tmporal dynamics of deforestation in various parts of India.

3. OBJCTIVS

   This study aims to lvrag a comprhnsiv datast of tr covr loss in India to achiv th following objctivs:

   • Idntify and analyz th pattrns and tmporal trnds of tr covr loss across various rgions in India.

   • Invstigat th major driving forcs bhind dforstation and assss thir rlativ contributions to tr covr loss.

   • Inform th dvlopmnt of targtd stratgis for forst consrvation and sustainabl land us practics in India.

4. RESEARCH METHODOLOGY

   Data, which can b an important fact in any analysis has bn collctd through numrous rsourcs, as wll as scondary information ar mployd in this study. Th study uss ach scondary information. Th scondary information was collctd from rvald sourcs lik journals & wbsits.

   • Data Collction: Idntify and gathr rlvant datasts on tr covr loss in India. Sourcs lik govrnmnt rports, rsarch paprs, or publicly availabl datasts (.g., Global Forst Watch) ar usd in this study. Ensuring th datasts covr an appropriat tim priod and spatial xtnt for your rsarch objctivs.

   • Data Claning: Rmoving th duplicat rcords and obsrvations from th datast. Handling missing valus appropriatly, dpnding on th xtnt and importanc of th missing data. Idntifying and addrssing data inconsistncis, such as discrpancis in units or data formats.

   • Data Intgration: Multipl datasts ar intgratd by aligning common filds (.g., dat, location) and crating uniqu idntifirs. Ensuring consistncy in th administrativ boundaris across datasts.

   • Data Transformation: Standardizing or normalizing numrical variabls, to bring thm to a consistnt scal.Considring transforming variabls to mt assumptions for statistical analyss.

   • Data Visualization: Lingraph ,Bargraph ,Pichart and Donut ar uss to visualiz th historic data and hlps in bttr undrstanding of charts with th hlp of labls and prcntag calculations.

     Tools: Python

     Id: Jupytr Notbook

5. DATA ANALYSIS & INTERPRETATION Tree cover loss with respect to year and area:

   Fig. 1 Tree cover loss with year and area

   Btwn 2002 and 2022, India lost 393kha of primary tmprat forsts, accounting for 18% of its total tr covr ovr th sam priod Total primary tmprat forsts dclind by 3.9% during this priod.

   Tree Cover Loss Due To Fire In India:

   Fig. 2 Tree cover loss with fire

   Btwn 2001 and 2022, India lost 35.9kha tr covr du to fir, and 2.15Mha in othr othr total losss. Th yar with th havist tr covr losss du to fir during this priod was 2008 with 3.00kha lost to fir3.5% of th total dforstation for that yar.

   Tree Cover Distribution in India:

   Fig. 3 Tree cover Distribution

   Btwn 2000 and 2020, thr is a chang of 874kha (1.3%) in tr covr in India.

   Stabl forst 59.24 (86.7%)

   Gain – 1.88 (2.8%)

   Loss 1.01 (1.5%)

   Distrub 6.17 (9.0%) Land Cover Distribution In India:

   Fig. 4 Land Cover Distribution

   As of 2000, 11% of India land covr was >30% tr covr.

   • Plantations: 1.2%

   • Natural Forst: 11.1%

   • Othr Land Covr: 87.7%

     Top Locations Of Tree Cover Between 2001 And 2022:

     Th top 5 placs in India in 2010 rprsnt 55% of th total tr covrag. Arunachal Pradsh covrd th highst tr at 6.11Mha as compard to th avrag of 957kha.

     Fig. 5 Top Locations Of Tree Cover

     State	Area
     Arunachal Pradesh	6.11Mha
     Assam	2.57Mha
     Chhattisgarh	2.28Mha
     Kerala	2.28Mha
     Odisha	1.92Mha

     Top Locations Of Deforesation Between 2001 And 2022:

     In India, ths rgions ar rsponsibl for all tr covr loss btwn 2001 and 2022. Assam has th th th highst tr covr loss of 306kha compard to an avrag of 62.5kha.

     Fig. 6 Top Locations Of Deforesation

     State	Area
     Assam	306kha
     Mizoram	293kha
     Arunachal Pradesh	251kha
     Nagaland	248kha
     Manipur	226kha

     • Intgration with Climat Chang Mitigation Efforts: Tr covr loss contributs to climat chang by rducing carbon squstration and xacrbating grnhous gas missions. Data analysis can facilitat th intgration of tr covr consrvation fforts with climat chang mitigation stratgis. By quantifying th carbon storag potntial of forsts and analyzing th impact of tr covr loss on rgional and national carbon budgts, policymakrs can mak informd dcisions to combat climat chang ffctivly.

6. CONCLUSION

   In conclusion, analyzing tr covr loss data in undrstanding xtnt, causs, impacts from dforstation and dgradation Timlin usd rmot snsing data, satllit imagry, machin larning tchniqus and socioconomic indicators, rsarchrs wr abl to gain valuabl insights. Th rviwd rsarch highlights th importanc of applying data and analysis to mapping and tr covr loss at multipl scals, from rgion to rgion. Thy mphasiz th importanc of accurat and up-to-dat data to idntify dforstation hotspots, monitor changs ovr tim, and valuat th ffctivnss of intrvntion programs. In addition, th litratur mphasizs th importanc of intgrating data analyzs of socio-conomic factors and land-us changs to bttr undrstand th drivrs of tr covr dgradation in India. This intrdisciplinary approach allows rsarchrs to idntify root causs and prioritiz consrvation fforts accordingly. Ovrall, data analysis on tr covr loss in India is critical for vidnc-basd dcision-making, policy formulation and consrvation fforts. By lvraging th powr of advancd data and analytical tchniqus, comprhnsiv stratgis can b dvlopd to rduc dforstation, promot sustainabl land managmnt practics and crat Indias srving forsts valu covr protction.

7. FUTURE SCOPE

Th futur scop of data analysis of tr covr loss in India involvs svral potntial aras of xploration and application. Hr ar som ky aras to considr:

• Prdiction and Early Warning Systms: Data analysis can b usd to dvlop prdictiv modls and arly warning systms for tr covr loss in India. By analyzing historical data, satllit imagry, climat data, and othr rlvant factors.

• Impact Assssmnt of Policy Intrvntions: Data analysis can b utilizd to valuat th ffctivnss of policy intrvntions aimd at addrssing tr covr loss. By analyzing th data bfor and aftr th implmntation of spcific policis or intrvntions, it is possibl to assss thir impact on tr covr and idntify th most ffctiv approachs. This can inform futur policymaking and hlp dsign mor fficint stratgis for tr covr consrvation.

REFERENCES

[1] Global Forst Watch (https://www.globalforstwatch.org/)

Global Forst Watch provids data and tools for monitoring forsts globally, including tr covr loss in India. Th platform allows usrs to xplor intractiv maps, analyz trnds, and download data.

[2] Forst Survy of India (http://fsi.nic.in/)

Th Forst Survy of India (FSI) is an organization undr th Ministry of Environmnt, Forst, and Climat Chang, Govrnmnt of India. FSI conducts rgular assssmnts of th country's forst rsourcs and publishs rports, such as th India Stat of Forst Rport (ISFR), which provids comprhnsiv information on forst covr, tr covr, and forst covr loss.

[3] World Rsourcs Institut (https://www.wri.org/)

Th World Rsourcs Institut (WRI) is a global rsarch organization that focuss on sustainabl dvlopmnt. WRI producs rsarch, data, and tools rlatd to forst covr loss and natural rsourc managmnt, including country profils for India.

[4] Th Food and Agricultur Organization of th Unitd Nations (FAO) – Global Frst Rsourcs Assssmnt (http://www.fao.org/forst-rsourcs- assssmnt/n/)

FAO's Global Forst Rsourcs Assssmnt (FRA) provids comprhnsiv data on forst rsourcs, including tr covr loss, for countris lik India. Th FRA rports ar publishd vry fiv yars and provid insights into th status and trnds of th world's forst rsourcs.

[5] Ravindranath, N. H., t al. (2008). "Tr covr loss and forst dgradation in India: implications for REDD+." Currnt Scinc 95(11): 1529-1536. (https://www.jstor.org/stabl/24098293)

[6] Pandy, R., t al. (2017). "Drivrs of tr covr loss and forst dgradation in India." Environmntal Consrvation 44(3):226-234. (https://www.cambridg.org/cor/journals/nvironmntal-

/D6A20C0A3C9E6C1FF6E2E8E1FFC3A4C0)