Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Impact of roads on deforestation levels across Latin America

2,141 views

Published on

Presentation on the impact of road developments in Latin America.

Published in: Technology, Business
  • Be the first to comment

Impact of roads on deforestation levels across Latin America

  1. 1. ROAD IMPACT ASSESSMENT ON HABITAT LOSS IN LATIN AMERICAKarolina Argote, Louis Reymondin, Carolina Navarrete, Denny Grossman, Jerry Touval, Andy Jarvis Decision and Policy Analysis Research Area (DAPA) International Center for Tropical Agriculture (CIAT) Conservation Biology Institute (CBI) The Nature Conservancy (TNC)
  2. 2. Spot the road?
  3. 3. Project outcomesThis project presents a habitat change monitoring methodology that can be used to identify environmental impacts of road construction, and support improved design of future projects that would minimize negative environmental impacts. The project has also helped understand the nature ofenvironmental impacts of road infrastructure projects in distinctcontexts across Latin America, demonstrating the importance of policy and ecosystem specific safeguards.
  4. 4. Objectives of the project• Evaluate the environmental impacts of road infrastructure in the past through monitoring of natural habitat loss pre- and post- construction for 5 road projects across Latin America• Demonstrate how this can be integrated into a decision support tool – DATABASIN• Identify entry points by which ex ante assessment can provide improved safeguards
  5. 5. Content1. Methodologies 1.1. Terra-I – Habitat Change Monitoring2. Road Impact Results 2.1. The BR-364 highway in Brazil. 2.2. The IIRSA projects in Peru. 2.3. The Pan-American Highway in Panama. 2.4. The Santa Cruz – Puerto Suarez corridor in Bolivia. 2.5. The Trans-Chaco Highway in Paraguay.3. Methodology of the Future Deforestation Scenarios and Results4. Carbon-Conservation Interface5. Conclusions and Recommendations Photo by Alvaro Gaviria in Cartagena del Chaira Parques Nacionales Naturales de Colombia
  6. 6. 1.1. Methodologies: Terra-i Methodology Terra-i is a system that monitoring the habitat change in Latin America using Neural Network and satellite data We therefore try to learn how each pixel (site) responds to climate, and any anomoly corresponds to human impact. Neural-network, is a bio-inspired technology which emulates the basic mechanism of a brain. It allows … To find a pattern in noisy dataset 9000 To apply these patterns to new dataset 8500 8000 7500 7000 NDVI INPUTS: Past NDVI (MODIS MOD13Q1) 6500 Previous Rainfall (TRMM) Measurments 6000 Predictions 5500 Interval max OUTPUT: 16 day predicted NDVI Interval min 5000 4500 1 2 3 4 5 6 7 8 9 Time
  7. 7. 1.1. Methodologies: Terra-i The Bottom-Line • 250m resolution • Latin American coverage (currently) • Satellite data to monitorithe habitat every 16 days • Identification of habitat change events • Habitat loss data online to visualize and download. www.terra-i.org
  8. 8. 1.1. Methodologies: Interpreting the maps
  9. 9. 1.1. Methodologies: Interpreting the graphs When did the habitat loss happen within each buffer? Area of habitat lost Geographic footprint of Buffer distance the road from the road (km)
  10. 10. BR-364 Highway, Brazil Date Acre Segment: Construction 2002 to 2010 Rondonia Segment: Construction 1985 to 1997
  11. 11. 2.1. Road Impact Results: BR-364 Highway, Brazil Study Area Section 2: a corridor of 515km which connects the town of Rio Branco in the state of Acre and Porto Velho in the state of Rondônia.
  12. 12. 2.1. Road Impact Results: BR-364 Highway, Brazil Study Area The road Cruzeiro do Sul – Porto Velho was analyzed into two different sections. Section 1: a corridor of 623km Cruzeiro do Sul - Rio Branco in the state of Acre, Brazil. This section passes through a large biological corridor of the state of Acre which has been regulated by 39 protected areas connected to each other.
  13. 13. 2.1. Road Impact Results: BR-364 Highway, Brazil
  14. 14. 2.1. Road Impact Results: BR-364 Highway, Brazil
  15. 15. 2.1. Road Impact Results: BR-364 Highway, Brazil Road impact Comparing the two segments one can see a huge difference in the deforestation rates and in how it is the spatially distributed. Much higher deforestation rates, and much BIGGER footprint >50km due to secondary roads etc. More localised footprint, and and lower overall deforestation levels. Nevertheless, increase in last 2 years.
  16. 16. 2.1. Road Impact Results: BR-364 Highway, Brazil
  17. 17. 2.1. Road Impact Results: BR-364 Highway, Brazil Protected Areas Protected Area 2004 2005 2006 2007 2008 2009 2010 2011 Accum. Rate Bom Futuro 3,906 9,531 18,325 11,381 13,675 2,619 14,738 2,231 76,406 10,188 Rio Jaciparana 3,838 5,594 12,288 7,300 3,563 2,494 10,925 1,525 47,525 6,337 Uru-Eu-Wau-Wau 219 450 1,238 656 1,125 575 7,000 1,494 12,756 1,701 Rio Ouro Preto 263 744 1,613 550 206 100 3,006 131 6,613 882 Corumbiara 1,313 1,894 956 1,081 531 94 550 75 6,494 866 Pacaas Novas 0 75 275 2,488 194 225 2,306 663 6,225 830 Mutum 50 100 1,231 656 525 288 2,331 369 5,550 740 Bom Futuro and Jaciparaná are the two protected areas most affected by deforestation in Rondônia and are located next to the analyzed road, within a buffer area of 20km. Actually, the deforestation rate in Bom Futuro has been of 10,188 hectares per year (adding up to 76,406 hectares converted in 7.5 years) whereas it has been of 6,337 hectares per year in Rio Jaciparaná (adding up to 47525 hectares converted in 7.5 years).
  18. 18. 2.1. Road Impact Results: BR-364 Highway, Brazil Conclusions • Section 1: Acre. Habitat loss of 19,542 hectares was recorded per year in average in a buffer area of 50km of the Cruzeiro do Sul -Rio Branco Segment • Section 2: Rondonia. Habitat loss of 79,783 hectares per year within a same buffer size around the Rio Branco -Porto Velho Segment. • Much higher in the segment Rio Branco-Porto Velho (in Rondonia) than in Cruzeiro do Sul-Rio Branco (In Acre) likely due to the conservation policies implemented in Acre state. Note fewer secondary roads, and greater protection from National Parks in segment 1. Road: Rondonio Acre Project Period: 2002-2010 2002-2010 Average pre-road deforestation rate: 79,000 18,700 Average post-road deforestation rate: 113,000 (+43%) 32,400 (+72%) Year of peak deforestation: 2006 2008 Footprint (modal deforestation distance): 20-30km 20-30km
  19. 19. IIRSA Project, Peru Date Construction: 1998 to 2007
  20. 20. 2.2. Road Impact Results: IIRSA Projects, Peru Study Area The analyzed roads have a total length of 1584km and go through all Peru from the Pacific coast to the Acre state in Brazil. The road was split into three different sections for the analysis:  Section 1: 752km. Paita on the Pacific coastSection 1 (Piura) to Tarapoto.Andean  Section 2: 381km. Tarapoto - Huanuco (where it passes 2km away from Tingo Maria National Park).  Section 3: 451km. Section 2 Piedemonte Tingo Maria (Huanuco) - Cruzeiro do Sul (Acre, Brazil). Section 3 Amazon
  21. 21. 2.2. Road Impact Results: IIRSA Projects, Peru
  22. 22. 2.2. Road Impact Results: IIRSA Projects, Peru Road Impact Section 1 (Paita-Tarapoto) : accumulated loss of 40,794 hectares (5,439 Ha/yr). IIRSA Road Impact Significant Habitat loss Section 1: Patia-Tarapoto increase in 6,000 deforestation in 5,000 past 3-4 years 4,000 Hectares 3,000 Most habitat 2,000 loss in first 1,000 10km (45%) 0 Road to 10 10 to 20 20 to 30 30 to 40 40 to 50
  23. 23. 2.2. Road Impact Results: IIRSA Projects, Peru
  24. 24. 2.2. Road Impact Results: IIRSA Projects, Peru Road Impact Section 2 (Tarapoto-Tingo Maria) : accumulated loss of 30,763 Ha (4,102 Ha/yr). Most impacted areas are located in a buffer of 30km from the road. Significant IIRSA Road Impact increase in Habitat loss Section 2: Tarapoto-TingoMaria 3,000 deforestation in past 3-4 years 2,500 2,000 Hectares Most habitat 1,500 loss in first 1,000 30km (88%) 500 0 Road to 10 10 to 20 20 to 30 30 to 40 40 to 50
  25. 25. 2.2. Road Impact Results: IIRSA Projects, Peru
  26. 26. 2.2. Road Impact Results: IIRSA Projects, Peru Road Impact Section 3 (Tingo Maria-Cruzeiro): accumulated loss of 58,900 hectares (7,853 Ha/year). Most impacted areas are located in a buffer of 30km from the road. No apparent IIRSA Road Impact increase in Habitat loss Section 3: TingoMaria-Cruzeiro deforestation 7,000 during or after 6,000 road 5,000 construction Hectares 4,000 3,000 Most habitat 2,000 loss in first 1,000 30km (81%) 0 Road to 10 10 to 20 20 to 30 30 to 40 40 to 50
  27. 27. 2.2. Road Impact Results: IIRSA Projects, Peru
  28. 28. 2.2. Road Impact Results: IIRSA Projects, Peru Conclusions Section 1: Andes. Footprint more localised (<10km), 25% increase in habitat loss post- project versus pre-project. Section 2: Piedemonte. Larger footprint (10-20km), and > doubling of deforestation after road finalization. Section 3: Tingo Mario-Cruzeiro. High baseline levels of deforestation in the region, but no increase since road project (major sections of road still not complete). Road: IIRSA, Peru, Section 1 IIRSA, Peru, Section 2 IIRSA, Peru, Section 3 Project period: 1998-2007 1998-2007 1998-2007 Average pre-road deforestation rate: 4,900 2,300 7,600 Average post-road deforestation rate: 6,100 (+25%) 5,200 (+125%) 7,500 (-1%) Year of peak deforestation: 2010 2010 2005 Footprint (modal deforestation distance): 0-10km 10-20km 10-20km
  29. 29. Pan-American Highway, Panama Date Construction: 1985 to 1990
  30. 30. 2.3. Road Impact Results: Pan-American Highway, Panamá Study Area The Pan-American Highway is located in the Darien province in Panama at the eastern end of the country and its length is approximately 262km, in a 30km of buffer around the road are located more than 10 protected areas with important ecological functions.
  31. 31. 2.3. Road Impact Results: Pan-American Highway, Panamá Habitat Change Monitoring MAIN INPUTS  For generated deforestation maps before 2000: A dataset of land cover produced by the Forest Information System Project, the National Environmental Authority (ANAM) for 1992 and 2000.  For generated deforestation maps between 2004 and 2010: Terra-I dataset. Methodology i. Reclassify the Land Cover Maps of 1992 and 2000 using ArcGIS software in Vegetation and non vegetation maps. ii. Generated the deforestation map of 1992-2000. iii.Applied the Terra-I Methodology to monitoring the habitat change between 2004 to 2010. iv.Analyze the road impact in buffers areas in 10, 20, 30, 40 and 50km of the road.
  32. 32. 2.3. Road Impact Results: Pan-American Highway, Panamá
  33. 33. 2.3. Road Impact Results: Pan-American Highway, Panamá Road impact The habitat loss is greater the closest it’s to the road. Vast majority of habitat change occurred in the 1990’s directly Buffers Area 1992-2000 2004-2010 Total loss %Loss after road construction. Road to 10km 253,546 77,930 3,675 81,605 32% 10km to 20km 260,711 37,391 1,606 38,997 15% Deforestation 2004-2011 < 10% 20km to 30km 539,159 39,849 4,700 44,549 8% 30km to 40km 497,927 16,051 2,531 18,583 4% of 1990’s levels. 40km to 50km 380,294 7,466 1,844 9,310 2% Road to 50km 2,450,696 272,150 18,231 290,381 12%
  34. 34. 2.3. Road Impact Results: Pan-American Highway, Panamá Conclusions • Between 1992 and 2000 there was an alarming loss of 7% of the total national forest cover in Panama which is equivalent to 497,306 hectares. This deforestation is localized mostly in the provinces of Panama and Darien and close to the road. • The impact occurs mainly in the direct influence area of the road (0 to 10km). • The Darien province lost 24% of its forests, and Panama 23%. Most of this deforestation occurred in Mixed Cative forest in order to create new cropland areas.
  35. 35. Santa Cruz-Puerto Suarez, Bolivia Date Construction: 2000 to 2011
  36. 36. 2.4. Road Impact Results: Santa Cruz-Puerto Suarez Corridor, Bolivia Study Area The corridor Santa Cruz-Puerto Suarez is located in the South East of Bolivia. Its length is approximately 636km and connects the towns of Santa Cruz de la Sierra and Puerto Suarez located on the border with the state of Mato Grosso do Sul in Brazil. In the area one can see four easily distinguishable types of ecoregions: Pantanal, Dry Chaco, Chiquitano Dry Forest and Cerrado
  37. 37. 2.4. Road Impact Results: Santa Cruz-Puerto Suarez Corridor, Bolivia
  38. 38. 2.4. Road Impact Results: Santa Cruz-Puerto Suarez Corridor, Bolivia Road Impact • Road still under construction. Some direct impacts especially close to Santa Cruz. • Major indirect impacts of fires originating from “slash and burn” practices, especially in 2010. Santa Cruz Road Impact Habitat loss Section: Santa Cruz-Puerto Suarez 14,000 12,000 10,000 Hectares 8,000 6,000 4,000 2,000 0 Road to 10 10 to 20 20 to 30 30 to 40 40 to 50
  39. 39. 2.4. Road Impact Results: Santa Cruz-Puerto Suarez Corridor, Bolivia Conclusions • Too early to say what direct impacts are until road fully connects Bolivia with Brazil • Nevertheless, clear indirect impact through fires originating from slash and burn activity, especially in the region of Santa Cruz Road: Santa Cruz-Puerto Suarez Project period: 2000-2011 Average pre-road deforestation rate: 11,392 Average post-road deforestation rate: N/A Year of peak deforestation: 2010 Footprint (modal deforestation distance): 20-30km
  40. 40. Trans-Chaco Highway, Paraguay Date Construction: 2002 to 2006
  41. 41. 2.5. Road Impact Results: The Trans-Chaco Highway, Paraguay Study Area The trans-Chaco highway length approximately 736km, extending from the boundaries between Bolivia and Paraguay near the military post Mayor Infante Rivarola in the department of Boqueron until it intersects with the 9th Road which runs through the Dry Chaco in Boqueron continuing through the department of Presidente Hayes across the humid Chaco region up to the Asuncion metropolitan area in the Central Department.
  42. 42. 2.5. Road Impact Results: The Trans-Chaco Highway, Paraguay Habitat Change Monitoring MAIN INPUTS  For generated deforestation maps between 2000 and 2004: Dataset from the high spatial resolution satellite Landsat 4 Thematic Mapper in the Dry Chaco ecoregion.  For generated deforestation maps between 2004 and 2010: Terra-I dataset. Methodology i. Classify the Landsat-4 satellite images using the k-Means Algorithm . ii. Generated the deforestation map of 2000-2004. iii.Applied the Terra-I Methodology to monitoring the habitat change between 2004 to 2011. iv.Analyze the road impact in buffers areas in 10, 20, 30, 40 and 50km of the road.
  43. 43. 2.5. Road Impact Results: The Trans-Chaco Highway, Paraguay
  44. 44. 2.5. Road Impact Results: The Trans-Chaco Highway, Paraguay
  45. 45. 2.5. Road Impact Results: The Trans-Chaco Highway, Paraguay Road Impact • Total of 650,000 Hectares lost in 50km buffer since 2004 • Massive increase since 2007 (project completion) • Large footprint, covering >50km from road
  46. 46. 2.5. Road Impact Results: The Trans-Chaco Highway, Paraguay Conclusions • Very high levels of deforestation pre- and post- road construction • But > 300% increase in deforestation rates since road finished, with a footprint that likely goes beyond 50km buffer Road: Trans-Chaco Highway Project period: 2002-2006 Average pre-road deforestation rate: 23,000 Average post-road deforestation rate: 97,000 (+319%) Year of peak deforestation: 2010 Footprint (modal deforestation distance): 30-40km
  47. 47. Methodologies: Future Deforestation Scenarios INPUTS  The distance to the nearest road, to the nearest river, This methodology is still to the nearest city (> 1000 people) under development, nevertheless its implementation as proof of concept  The elevation in Brazil generated good results in  The base map (what was already deforested before the areas with clear patterns of analysis with terra-I (from human classification of the deforestation. terra-i clusters))  The detection from the terra-I model. Steps… 1) Train neural network to recognize pixels that are likely to be deforested 2) Create maps of potential deforestation 3) Select random pixels from the potential maps 4) Repeat 2 and 3 Currently, the tool only takes into account topographic variables, but the idea is in the future is to include others inputs such as administrative information (protected areas, country…), social information (small farmers, industrial exploitation, community managed forest…) and ecosystems in order to improve the estimation.
  48. 48. 3. Future Deforestation Scenarios: Applied in BR-364 Road, Brasil Applied INPUTS  The distance to the nearest road, to the nearest river,  The study area was the area around the road Rio to the nearest city (> 1000 people) Branco-Porto Velho, in Brazil from the 1st of  The elevation January 2004 to the 10th of June 2011.  The base map (what was already deforested before the Constant rate to 10’000 hectares per 16 days analysis with terra-I (from human classification of the period (the average rate recorded by Terra-i in this terra-i clusters)) area)  The detection from the terra-I model. Sampled 10’000 pixels to train the neural network. (7000 unchanged and 3000 deforested) This first implementation of this methodology gave encouraging results as by comparing this result with the actual detected deforestation one can see that the general patterns resulting from the simulation are convincing and quite similar to the real events. However, various improvements could be instigated.
  49. 49. 3. Future Deforestation Scenarios: Applied in BR-364 Road, Brasil PROOF OF CONCEPT Base map Potential deforestation at T=0 Potential deforestation at T=150 Predicted deforestation Actual deforestation (Terra-i)
  50. 50. A synthesis of findingsRoad: Rondonio Acre IIRSA, Peru, Sect. 1 IIRSA, Peru, Sect. 2 IIRSA, Peru, Sect. 3 Trans-Chaco HighwayProject Period: 2002-2010 2002-2010 1998-2007 1998-2007 1998-2007 2002-2006Average pre-road deforestation rate: 79,000 18,700 4,900 2,300 7,600 23,000Average post-road deforestation rate: 113,000 (+43%) 32,400 (+72%) 6,100 (+25%) 5,200 (+125%) 7,500 (-1%) 97,000 (+319%)Year of peak deforestation: 2006 2008 2010 2010 2005 2010Footprint (modal deforestation distance): 20-30km 20-30km 0-10km 10-20km 10-20km 30-40km • Roads clearly a significant driver of deforestation and land-use change, demonstrated in all 5 projects studies. Impacts are both direct and indirect. • A road makes a different “footprint” depending on the ecosystem (Andes <10km, Amazon ~50km, Chaco >50km).
  51. 51. Lessons learned• Other factors such as secondary roads and rivers are important drivers of habitat change and roads open access to them.• As a Monitoring Tool Terra-I is only useful to analyze projects after 2004. In the cases of Acre-Rondonia, Peru and Paraguay we had three cases where the full power of Terra-I could be shown.• Local, national and international policies are clearly important contexts that should be taken into account during and after road construction as they have a clear link to land-use change and can contribute to mitigation or exacerbation of the road project encironmental impact.
  52. 52. Policy Recommendations• Regional and national environmental policies in place can significantly reduce the number of hectares deforested during and after the road construction project. The most outstanding case can be found in Brazil where Rondonia has higher deforestation rates compared to Acre.• Most of the protected areas affected directly or indirectly by the road construction, were established after the roads where built. In cases were critical ecosystems are endangered, policy makers and development planners should consider for the future, well in advance, critical natural habitats for conservation and biodiversity hotspots.• It’s expected that infrastructure allows the expansion of economic activities. In this sense, national and regional policies and incentives to promote sustainable and environmentally friendly agricultural practices are also important. In the case of the slash and burn method in Bolivia and Peru causing multiple forest fires, more national policies and programs to promote more sustainable practices should be in place, such as the Slash and Mulch Agroforestry Systems. It’s also key to have increased productivity. Land policy and law enforcement are also relevant in terms of reducing the negative environmental impact of road infrastructure.
  53. 53. Project Components by Conservation Biology Institute- New Datasets in IDB-DSS - Carbon - Deforestation- New Analyses - Carbon CNH relationship - IIRSA road impact on CNH in Amazon and Gran Chaco - IDB road impact on CNH related to deforestation
  54. 54. New Data: Roads and Deforestation
  55. 55. Carbon and Biodiversity Analysis
  56. 56. Carbon-Conservation Interface: CNH Protected Areas
  57. 57. Carbon-Conservation Interface: CNH Species
  58. 58. Carbon-Conservation Interface: CNH Terrestrial Ecosystems
  59. 59. Carbon-Conservation Interface: All Critical Natural Habitat
  60. 60. Carbon-Conservation Interface: Above Ground Carbon
  61. 61. Interface between High Carbon and Critical Natural Habitat
  62. 62. Impact of IIRSA Roads on CNH
  63. 63. IIRSA Road Impacts to Biodiversity
  64. 64. IIRSA Road Impacts to Biodiversity
  65. 65. Cruziero do Sul – Rio Branco Road (BR364)An exploration of Road Development, Deforestation, AboveGround Carbon and Critical Natural Habitat
  66. 66. AcknowledgementsThis Consultancy Project was conducted by the International Center for Tropical Agriculture (CIAT), the NatureConservancy (TNC), and the Conservation Biology Institute (CBI) for the Environmental and Social Safeguards Unit ofthe Inter-American Development Bank. This project was supported with funds from the German FederalBundesministerium fuer wirtschaftliche Zusammenarbeit und Entwicklung (BMZ) in the framework of a cooperationprogram between the Inter-American Development Bank (IDB) and the Deutsche Gesellschaft fuer InternationaleZusammenarbeit (GIZ). Thank you!

×