Skip to main content

Advertisement

SpringerLink
Log in

Identification of Areas Highly Vulnerable to Land Conversion: A Case Study From Southern Thailand

  • Published:
Environmental Management Aims and scope Submit manuscript

Abstract

Land conversion is having major impacts on wildlife globally, and thus understanding and predicting patterns of land conversion is an important component of conservation planning. Southeast Asia is undergoing rapid habitat conversion; however, most countries in the region have very limited human resources devoted to planning, and typically land-cover trend assessments are often challenging. Here we demonstrate a rapid method for land-cover change quantification for areas of terrestrial, mangrove and peat swamp forests at high risk from land conversion that can be quickly and simply predicted using southern Thailand as an example. Land-cover maps from two time periods (1995/1996 and 2015/2016) were produced and compared to determine changes between the two time periods. Five land-cover categories (terrestrial forest, mangrove forest, peat swamp forest, human settlement, agriculture) were estimated along with land-cover changes. Hot spots of high percentage change for human settlement and agriculture were identified, and vulnerable habitats were mapped including terrestrial forest, mangrove forest and peat swamp forest. Between 1996 and 2016, 22.1% of terrestrial forests, 26.2% of mangrove forests and 55% of peat swamp forests were lost. The losses of these natural habitats were clearly associated with agricultural expansion. Approximately 10.6%, 14.3% and 33% of terrestrial, mangrove and peat swamp forest remaining were identified as highly vulnerable, of which the majority were at the boundaries between natural and human-dominated areas. The technique offers promise for rapidly identifying high priority areas for more detailed analysis and potential conservation interventions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aburas MM, Ho YM, Ramli MF, Ash’aari ZH (2016) The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: a review. Int J Appl Earth Observation Geoinf 52:380–389

    Article  Google Scholar 

  • Bank of Thailand. (n.d.). Economic Structure of Southern Thailand. https://www.bot.or.th/Thai/MonetaryPolicy/Southern/EconomicReport/DocLib_Structure/EconomicStructure_south.pdf.

  • Bank of Thailand. (2021, April 2). Regional Economic Finance. Retrieved April 25, 2021, from https://www.bot.or.th/Thai/Statistics/RegionalEconFinance/Pages/default.aspx.

  • Beresford AE, Eshiamwata GW, Donald PF, Balmford A, Bertzky B, Brink AB, Fishpool LD, Mayaux P, Phalan B, Simonetti D, Buchanan GM (2013) Protection reduces loss of natural land-cover at sites of conservation importance across Africa. PLoS ONE 8(5):e65370

    Article  CAS  Google Scholar 

  • Bhattarai BR, Wright W, Poudel BS, Aryal, Adav BP, Wagle R (2017) Shifting paradigms for Nepal’s protected areas: history, challenges and relationships. J Mt Sci 14(5):964–979

    Article  Google Scholar 

  • BirdLife International and Handbook of the Birds of the World. (2020). Bird species distribution maps of the world. Version 2020.1 [Data set].Species distribution data. http://datazone.birdlife.org/species/requestdis.

  • Bong IW, Felker ME, Maryudi A (2016) How are local people driving and affected by forest cover change? Opportunities for local participation in REDD+ measurement, reporting and verification. PLoS ONE 11(11):e0145330

    Article  Google Scholar 

  • Broberg L (2003) Conserving ecosystems locally: a role for ecologists in land-use planning. BioScience 53(7):670–673

    Article  Google Scholar 

  • Ceballos G, Ehrlich PR (2002) Mammal population losses and the extinction crisis. Science 296(5569):904–907

    Article  CAS  Google Scholar 

  • Cheng LL, Liu M, Zhan JQ (2020) Land use scenario simulation of mountainous districts based on Dinamica EGO model. J Mt Sci 17(2):289–303

    Article  Google Scholar 

  • Chowdhury MSH, Gudmundsson C, Izumiyama S, Koike M, Nazia N, Rana MP, Mukul SA, Muhammed N, Redowan M (2014) Community attitudes toward forest conservation programs through collaborative protected area management in Bangladesh. Environ Dev Sustain 16(6):1235–1252

    Article  Google Scholar 

  • Chutipong W, Kamjing A, Klinsawat W, Ngoprasert D, Phosri K, Sukumal N, Wongtung P, Tantipisanuh N (2019) An update on the status of Fishing Cat Prionailurus viverrinus Bennett. 1833 (Carnivora: Felidae) Thail J Threatened Taxa 11(4):13459–13469

    Google Scholar 

  • Cooper HV, Evers S, Aplin P, Crout N, Dahalan MPB, Sjogersten S (2020) Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation. Nat Commun 11(1):1–8

    Google Scholar 

  • Corner RJ, Dewan AM, Chakma, S (2014) Monitoring and prediction of land-use and land-cover (LULC) change. In Dhaka megacity: Geospatial Perspectives on Urbanisation, Environment and Health. Springer, Dordrecht, p 75–97

  • Croxall JP, Butchart SH, Lascelles BEN, Stattersfield AJ, Sullivan BEN, Symes A, Taylor PHIL (2012) Seabird conservation status, threats and priority actions: a global assessment. Bird Conserv Int 22(1):1–34

    Article  Google Scholar 

  • De Koning F, Aguiñaga M, Bravo M, Chiu M, Lascano M, Lozada T, Suarez L (2011) Bridging the gap between forest conservation and poverty alleviation: the Ecuadorian Socio Bosque program. Environ Sci Policy 14(5):531–542

    Article  Google Scholar 

  • DOPA. (2021). Download Statistic: Population Size. https://stat.bora.dopa.go.th/new_stat/webPage/statByYear.php.

  • Francis CA, Hansen TE, Fox AA, Hesje PJ, Nelson HE, Lawseth AE, English A (2012) Farmland conversion to non-agricultural uses in the US and Canada: current impacts and concerns for the future. Int J Agric Sustain 10(1):8–24

    Article  Google Scholar 

  • Gale GA, Thongaree S (2006) Density estimates of nine hornbill species in a lowland forest site in southern Thailand. Bird Conserv Int 16(1):57–69

    Article  Google Scholar 

  • Gordon A, Simondson D, White M, Moilanen A, Bekessy SA (2009) Integrating conservation planning and landuse planning in urban landscapes. Landsc Urban Plan 91(4):183–194

    Article  Google Scholar 

  • Grainger A (2008) Difficulties in tracking the long-term global trend in tropical forest area. Proc Natl Acad Sci 105(2):818–823

    Article  CAS  Google Scholar 

  • Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr 63:101–112

    Article  Google Scholar 

  • Han H, Yang C, Song J (2015) Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability 7(4):4260–4279

    Article  Google Scholar 

  • Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853

    Article  CAS  Google Scholar 

  • Hughes JB, Round PD, Woodruff DS (2003) The Indochinese–Sundaic faunal transition at the Isthmus of Kra: an analysis of resident forest bird species distributions. J Biogeogr 30(4):569–580

    Article  Google Scholar 

  • Imai N, Furukawa T, Tsujino R, Kitamura S, Yumoto T (2018) Factors affecting forest area change in Southeast Asia during 1980-2010. PLoS ONE 13(5):e0197391

    Article  Google Scholar 

  • IUCN. (2017). The IUCN Red List of Threatened Species. Version 2017 [Data set]. IUCN Red List. https://www.iucnredlist.org.

  • Jat MK, Choudhary M, Saxena A (2017) Urban growth assessment and prediction using RS, GIS and SLEUTH model for a heterogeneous urban fringe. Egypt J Remote Sens Space Sci 10(3):1–19

    Google Scholar 

  • Jiang L, Deng X, Seto KC (2013) The impact of urban expansion on agricultural land use intensity in China. Land Use Policy 35:33–39

    Article  CAS  Google Scholar 

  • Knorn J, Kuemmerle T, Radeloff VC, Szabo A, Mindrescu M, Keeton WS, Abrudan I, Griffiths P, Gancz V, Hostert P (2012) Forest restitution and protected area effectiveness in post-socialist Romania. Biol Conserv 146(1):204–212

    Article  Google Scholar 

  • Laurance WF, Sayer J, Cassman KG (2014) Agricultural expansion and its impacts on tropical nature. Trends Ecol Evol 29(2):107–116

    Article  Google Scholar 

  • Megahed Y, Cabral P, Silva J, Caetano M (2015) Land cover mapping analysis and urban growth modelling using remote sensing techniques in Greater Cairo Region—Egypt. ISPRS Int J Geo-Inf 4(3):1750–1769

    Article  Google Scholar 

  • Miettinen J, Shi C, Liew SC (2012) Two decades of destruction in Southeast Asia’s peat swamp forests. Front Ecol Environ 10(3):124–128

    Article  Google Scholar 

  • Namkhan M, Gale GA, Savini T, Tantipisanuh N (2021) Loss and vulnerability of lowland forests in mainland Southeast Asia. Conserv Biol 35(1):206–215

    Article  Google Scholar 

  • Newbold T, Hudson LN, Hill SL, Contu S, Lysenko I, Senior RA, Borger L, Bennett DJ, Choimes A, Collen B, Day J, Palma AD, Diaz S, Echeverria-Londono S, Edgar MJ, Feldman A, Garon M, Harrison MLK, Alhusseini T, Purvis A (2015) Global effects of land use on local terrestrial biodiversity. Nature 520(7545):45–50

    Article  CAS  Google Scholar 

  • NSO. (2021). Demography Population and Housing Branch. http://statbbi.nso.go.th/staticreport/page/sector/en/01.aspx.

  • Office of the Permanent Secretary Ministry of Tourism and Sports. (2020). Tourism Statistics 2019: Tourism Receipts from International Tourist Arrival. https://www.mots.go.th/more_news_new.php?cid=521.

  • Ord JK, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geographical Anal 27(4):286–306

    Article  Google Scholar 

  • Ota T, Lonn P, Mizoue N (2020) A country scale analysis revealed effective forest policy affecting forest cover changes in Cambodia. Land Use Policy 95:104597

    Article  Google Scholar 

  • Pensuk A, Shrestha RP (2007) Effect of land use change on rural livelihoods: A case study of Phatthalung Watershed, Southern Thailand. GMSARN In International Conference on Sustainable Development: Challenges and Opportunities for GMS, 12-14 December 2007, 12p.

  • Phommexay P, Satasook C, Bates P, Pearch M, Bumrungsri S (2011) The impact of rubber plantations on the diversity and activity of understorey insectivorous bats in southern Thailand. Biodivers Conserv 20(7):1441–1456

    Article  Google Scholar 

  • Pontius GR, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geographical Inf Sci 19(2):243–265

    Article  Google Scholar 

  • Posa MRC, Wijedasa LS, Corlett RT (2011) Biodiversity and conservation of tropical peat swamp forests. BioScience 61(1):49–57

    Article  Google Scholar 

  • Potapov P, Hansen MC, Laestadius L, Turubanova S, Yaroshenko A, Thies C, Smith W, Zhuravleva I, Komarova A, Minnemeyer S, Esipova E (2017) The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci Adv 3(1):e1600821

    Article  Google Scholar 

  • Pribadi DO, Pauleit S (2015) The dynamics of peri-urban agriculture during rapid urbanization of Jabodetabek Metropolitan Area. Land Use Policy 48:13–24

    Article  Google Scholar 

  • Richards DR, Friess DA (2016) Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc Natl Acad Sci 113(2):344–349

    Article  CAS  Google Scholar 

  • Robalino JA, Pfaff A (2012) Contagious development: neighbor interactions in deforestation. J Dev Econ 97(2):427–436

    Article  Google Scholar 

  • Santika T, Meijaard E, Budiharta S, Law EA, Kusworo A, Hutabarat JA, Indrawan TP, Struebig M, Raharjo S, Huda I, Ekaputri AD (2017) Community forest management in Indonesia: avoided deforestation in the context of anthropogenic and climate complexities. Glob Environ Change 46:60–71

    Article  Google Scholar 

  • Sathirathai S, Barbier EB (2001) Valuing mangrove conservation in southern Thailand. Contemp Econ Policy 19(2):109–122

    Article  Google Scholar 

  • Shwe NM, Sukumal N, Grindley M, Savini T (2020) Is Gurney’s pitta Hydrornis gurneyi on the brink of extinction? Oryx 54(1):16–22

    Article  Google Scholar 

  • Spracklen BD, Kalamandeen M, Galbraith D, Gloor E, Spracklen DV (2015) A global analysis of deforestation in moist tropical forest protected areas. PLoS ONE 10(12):e0143886

    Article  CAS  Google Scholar 

  • Sremongkontip S, Hussin YA, Groenindijk L (2000) Detecting changes in the mangrove forests of southern Thailand using remotely sensed data and GIS. Int Arch Photogramm Remote Sens 33(1):567–574

    Google Scholar 

  • Stibig HJ, Achard F, Carboni S, Rasi R, Miettinen J (2014) Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences 11(2):247

    Article  Google Scholar 

  • Suzuki K, Hara K (1996) Destruction of the tropical swamp forest ecosystem and its possibility for rehabilitation: An ecological case study in Thailand Wildlife Conservation Japan 2(1):37–46

    Google Scholar 

  • Tantipisanuh N, Chutipong W, Ngoprasert D, Lynam AJ, Steinmetz R, Sukmasuang R, Jenks KE, Grassman Jr. LI, Cutter P, Kitamura S, Baker MC, McShea W, Bhumpakphan N, Gale GA, Reed DH (2014) Recent distribution records, threats and conservation priorities of small cats in Thailand. Cat N Spec Issue 8:31–35

    Google Scholar 

  • Tantipisanuh N, Gale GA (2018) Identification of biodiversity hotspot in national level–Importance of unpublished data. Glob Ecol Conserv 13:e00377

    Article  Google Scholar 

  • Tantipisanuh N, Gale GA, Pollino C (2014) Bayesian networks for habitat suitability modeling: a potential tool for conservation planning with scarce resources. Ecol Appl 24(7):1705–1718

    Article  Google Scholar 

  • Tantipisanuh N, Ngoprasert D, Chutipong W, Kamjing A, Phosri K, Dachyosdee U (2019) Distribution status of otters and other small carnivore species in mangrove forest and wetland areas in Southern Thailand (2nd year) (Report No. P-17-50771). National Science and Technology Development Agency, 78p

  • Thai Meteorological Department (2014) Climate Chart: Mean Annual Rainfall in Thailand (mm) 30-year period: 1981–2010. https://www.tmd.go.th/en/climate.php?FileID=7

  • Thai Meteorological Department (2021, February 5). Thailand Annual Weather Summary, 2020. Retrieved April 25, 2021, from https://www.tmd.go.th/climate/climate.php?FileID=5

  • Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M (2017) Distribution and drivers of global mangrove forest change, 1996–2010. PLoS ONE 12(6):e0179302

    Article  Google Scholar 

  • Trisurat Y, Eawpanich P, Kalliola R (2016) Integrating land use and climate change scenarios and models into assessment of forested watershed services in Southern Thailand. Environ Res 147:611–620

    Article  CAS  Google Scholar 

  • Trisurat Y, Shrestha RP, Kjelgren R (2011) Plant species vulnerability to climate change in Peninsular Thailand. Appl Geogr 31(3):1106–1114

    Article  Google Scholar 

  • Ulya NA, Waluyo EA, Lestari S, Premonoi BT (2018) Peat Swamp Forest Degradation: Impacts, Affected Communities and Losses. In the 1st Sriwijaya International Conference On Environmental Issues 2018 (Vol. 68, p. 03007). EDP Sciences.

  • Umuziranenge G, Muhurwa F (2017) Ecotourism as potential conservation incentive and its impact on community development around Nyungwe National Park (NNP): Rwanda Imp J Interdiscip Res 3(10):2454–1362

    Google Scholar 

  • Valiela I, Bowen JL, York JK (2001) Mangrove Forests: One of the World’s Threatened Major Tropical Environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 51(10):807–815

    Article  Google Scholar 

  • Wijitkosum S (2016) The impact of land use and spatial changes on desertification risk in degraded areas in Thailand. Sustain Environ Res 26(2):84–92

    Article  CAS  Google Scholar 

  • Wyborn C, Bixler RP (2013) Collaboration and nested environmental governance: scale dependency, scale framing, and cross-scale interactions in collaborative conservation. J Environ manage 123:58–67

    Article  Google Scholar 

  • Zhang M, Zhao J, Yuan L (2013) Simulation of Land-Use Policies on Spatial Layout with the CLUE-S Model. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial. Inf Sci 1:185–190

    Google Scholar 

Download references

Acknowledgements

Project was supported by a King Mongkut’s University of Technology Research Grant (Grant no. 2017/9). Also, we thank Pongnapa Wongtung and Maliwan Namkhan for their help during the map production process as well as Matthew Grainger for valuable comments during paper preparation.

Author information

Authors and Affiliations

  1. Conservation Ecology Program, Pilot Plant Development and Training Institute, King Mongkut’s University of Technology Thonburi, Bangkok, 10150, Thailand

    Naruemon Tantipisanuh

  2. Conservation Ecology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, 10150, Thailand

    Naruemon Tantipisanuh & George A. Gale

Authors
  1. Naruemon Tantipisanuh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George A. Gale
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naruemon Tantipisanuh.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix I

Appendix I

Confusion matrix of classified land-cover maps for years 1995/1996 and 2015/2016

  

Reference (1995/1996)

  

Terrestrial forest

Mangrove forest

Water sources

Agriculture

Human settlement

Peat swamp forest

Totals

User’s accuracy

Map

Terrestrial forest

148

2

0

1

0

0

151

98.0%

 

Mangrove forest

0

146

0

1

0

0

147

99.3%

 

Water sources

0

0

147

0

3

0

150

98.0%

 

Agriculture

1

1

2

147

20

2

173

85.0%

 

Human settlement

0

0

0

1

127

0

128

99.2%

 

Peat swamp forest

0

0

0

0

0

48

48

100.0%

 

Totals

150

150

150

150

150

50

800

 
 

Producer’s Accuracy

98.7%

97.3%

98.0%

98.0%

84.7%

96.0%

 

95.4%

  

Reference (2015/2016)

  

Terrestrial forest

Mangrove forest

Water sources

Agriculture

Human settlement

Peat swamp forest

Total

User’s accuracy

Map

Terrestrial forest

149

1

0

0

0

0

150

99.3%

 

Mangrove forest

0

145

0

0

0

0

145

100.0%

 

Water sources

0

0

146

0

0

0

146

100.0%

 

Agriculture

1

4

1

150

12

5

173

86.7%

 

Human settlement

0

0

2

0

138

1

141

97.9%

 

Peat swamp forest

0

0

0

0

0

44

44

100.0%

 

Total

150

150

150

150

150

50

800

 
 

Producer’s accuracy

99.3%

96.7%

97.3%

100.0%

92.0%

88.0%

 

96.5%

  1. Overall accuracies were 95.4% and 96.5%, respectively

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tantipisanuh, N., Gale, G.A. Identification of Areas Highly Vulnerable to Land Conversion: A Case Study From Southern Thailand. Environmental Management 69, 323–332 (2022). https://doi.org/10.1007/s00267-021-01576-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00267-021-01576-6

Keywords

Search

Navigation