Louis Reymondin, Andy Jarvis, Karolina Argote, Norma Silva. Near-real Time Monitoring of Hábitat Change using a Neural Network and MODIS Data.

Transcrição

1 Terra-i Amazon Louis Reymondin, Andy Jarvis, Karolina Argote, Norma Silva Near-real Time Monitoring of Hábitat Change using a Neural Network and MODIS Data. International Center of Tropical Agriculture and The Nature Conservacy Decision and Policy Analysis Cali, Colombia October 2010

2 Table of Contents 1 Executive Summary Introduction Methodology The Data The Models Calibration Methodology Calibration Data Terra-i Calibration Validation Methodology Validation Data Terra-i Validation Results Detection by Country Detection within Protected Areas Detection in Areas of TNC interest Conclusion and Discussion Bibliography Annexes... i I II III IV V Tables by country... i Tables of detection in Protected Areas...ii Tables of detection in 5km Buffer of the protected areas... xii Tables of detection in 20km Buffer of the protected areas... xxii Tables of detection in Areas of TNC interest... xxxi

3 Figures Figure 1. Methodology Schematic Figure 2. Anomaly detection for a given píxel Figure 3. Diagram of the methodology used Figure 4. Calibration methodology Figure 5. Selected areas for calibration Figure 6. Deforestation detected by CLASlite Figure 7. Selection of threshold by cluster Figure 8. Selected threshold for each cluster Figure 9. Map of detection of PRODES digital in the Amazon Figure 10. Detection map of Terra-i and PRODES in the Amazon Figure 11. Validation results with PRODES Figure 12. Analyzed area in the Amazon Figure 13. Deforestation rate detected by Terra-I in the Amazon Figure 14. Change detected in Amazonian habitats Figure 15. Deforestation in the Amazon by country Figure 16. Deforestation detected in Amazon, excluding Brazil Figure 17. Terra-i detection in the Amazon-Colombia Figure 18. Terra-i detection in the Amazon-Venezuela Figure 19. Terra-i detection in the Guyanas Figure 20. Terra-i detection in the Amazon-Ecuador Figure 21. Terra-i detection in the Amazon-Peru Figure 22. Terra-i detection in the Amazon-Bolivia Figure 23. Terra-i detection in the Amazon-Brazil Figure 24. Detection map in protected areas of the Amazon Figure 25.Protected areas with the highest deforestation rate Figure 26. Protected areas with the highest deforestation rate Figure 27. Graph of the deforestation rate(%) in protected areas Figure 28. El Chore forest reserve with severe threat of deforestation Figure 29. Analysis of deforested areas in the Amazon Figure 30. Deforestation rate in Waiampi and Oiapoque Figure 31. Deforestation rate in Serradiv and Nasczingumt

4 Figure 32. Deforestation rate in Santarem and Paragimi Figure 33. Deforestation rate in Mtnorte and Mamobol Figure 34. Deforestation rate in Cofan and Rrcanaima Figure 35. Deforestation percentage in study areas Tables Table 1. Deforestation rate by state of PRODES Tabla 2. Deforestation rate detected by Terra-i Tabla 3. Deforestation rate in the Amazon, as detected by Terra-i Table 4. Deforestation rate in the most threatened protected areas Table 5. Deforestation rate with 5km buffer, in the Protected Areas Table 6. Deforestation rate to 5km buffer, in the most threatened protected areas Table 7. Deforestation rate in hectares per year in study areas Table 8. Deforestation rate in hectares per year in 50km of study areas Tabla 9. Deforestation percentage in the study areas

5 1 Executive Summary Terra-i is a model that detects natural habitat conversion events based on neural networks algorithms, vegetation index data from the Terra/MODIS satellite and precipitation data from TRMM. The model was implemented over the Amazon, and 99% of the territory was analysed. The single percentage omitted corresponds to areas with high cloud cover. The preliminaries results obtained from January 2004 to the end of May 2009 were calibrated using Landsat-7 images, chosen for their better resolution (30 m), compared to the MODIS images (250m). The Terra-i results were then validated using deforestation maps from 2005 to 2009 provided by the Brazilian Instituto Nacional de Pesquisas Espaciais (INPE) monitoring system the Proyecto de Estimativa de Deforestación de la Amazonia (PRODES). The Terra-i results were also compared to the deforestation maps provided for 2008 to 2009 by the Amazon institute. The final results shown an annual conversion rate of 3,062,000Ha/year in the Amazon as a whole, with the Brazilian Amazon exhibiting a conversion rate of 2,816,000Ha, the highest conversion percentage rate (3%) observed during six analysed years. In this report, we present the methodologies and results of various calibration and validation processes, analyzing the final data with statistics by country and by Amazonian protected area.

6 2 Introduction The intensity of the vegetation is a function that depends on climatic variables such as rainfall and temperature; site specific variables, such as vegetation type and soil characteristics; and alterations that disrupt the natural cycle, either natural phenomena or human activities. The Terra-i model uses Bayesian neural network, MODIS satellite data and TRMM precipitation data to predict the intensity of vegetation greenness at a given future date, based on measurements of past vegetation greenness and current climate. These predictions can then be compared to satellite data to detect significant changes in habitats. This report shows in detail the application, calibration and validation of the Terra-i model in the Amazon. We first select the optimal probabilities threshold for each cluster created by the model and then analyze the results at the country level and for protected areas. 6

7 3 Methodology The model uses a Multilayer Perception (MLP) neural network, combined with Bayesian theory and a robust confidence interval to identify abnormal behaviour in a time-series of vegetation change. The operationalization of the system for the whole of the Amazon (and with the ultimate aim of operationalizing globally) is a considerable challenge from a computer science perspective, as the resolution of the MODIS sensor (250m) means that the whole of the Amazon is made up of millions of individual values for each time-frame (16 days). This huge dataset implies the use of data mining technologies and distributed programming. Human activities create disturbances that alter the vegetation greenness cycle. Disturbances can be detected by the system as the NDVI of the landscape changes from its baseline. The general approach is to build a forecasting model capable of predicting vegetation greenness evolution based on previous greenness measurements and climatic measurements. Such a model is then used to predict future NDVI values (16 days ahead, given climatic conditions) and to identify anomalies or abrupt changes in vegetation. The model calculates an anomaly probability based on its predictions and the observed values. Our hypothesis is that vegetation evolution is influenced by recent rainfall and by more general seasonal trends, which can be captured by taking into account the preceding NDVI values at a given site. When major changes in the vegetation index are detected, we assume that they are due to human intervention on the land surface (fire, deforestation, or conversion). These events are thus flagged as occurrences that land managers, conservationists and policy makers should be aware of in a near real-time fashion The Data The inputs data are: NDVI data (Normalized Difference Vegetation Index) and quality assessment from the product MOD13Q1 from the MODIS sensor. These data are available with a frequency of 16 days and a spatial resolution of 250m. Precipitation data from the sensor TRMM (Tropical Rainfall Measuring Mission), with a measurement frequency of 3 hours and a spatial resolution of 28km. 7

8 3.2 The Models Figure 1. Methodology Schematic. After this learning step, we use the model to predict the greenness of the pixels for subsequent dates. By using Bayesian Neural Networks, we extract a probability that the observed value is an anomaly, based on the predicted value and some properties of the dataset found during model training. Whenever this probability exceeds a defined threshold, we flag the event as a potential anomaly. If the anomaly flag lasts for three consecutive dates, the system reports an anomaly at the given pixel. Figure 2. Anomaly detection for a given píxel. 8

9 While we report anomalies at the pixel level for every 16 days, this information can be synthesised to provide summary statistics for administrative units (municipalities, departments, and countries), critical ecosystems or protected areas. Our methodology can be split in four main steps: cleaning, clustering, modelling and anomaly detection. Figure 3 shows a graphical representation of the various steps involved. Figure 3. Diagram of the methodology used. 9

10 4 Calibration Methodology To calibrate the models obtained, we made a multitemporal analysis of satellite images Landsat-7 ETM + for the years 2004 and 2009 in areas where it was possible to obtain images of the same season and with a cloud cover less than 10% in both years. Landsat images were used mainly because of their resolution (30 meters), which exceeds that of MODIS (250m) which provides greater certainty in the calibration of the results. With these pairs of images and using the System Carnegie Landsat Analysis (CLASlite) we determined the deforestation in the areas selected for the calibration dataset. The CLASlite methodology consists of a spectral library created from biophysical data obtained from remote sensing and field work. Together with the sub-model "Automated Monte Carlo", the software identifies the most important components of tropical forest structure the fractional coverage canopy of vegetation, dead vegetation and exposed surfaces and then identifies deforestation and disturbance. 1 Figure 4. Calibration methodology. 1 Asner, G.P., et. al., Automated mapping of tropical deforestation and forest degradation: CLASlite. Journal of Applied Remote Sensing. 10

11 \ Figure 5. Selected areas for calibration. 11

12 4.1 Calibration Data The Carnegie Landsat Analysis - Lite Version (CLASlite) system is designed to identify deforestation and forest degradation using satellite imagery. The software was developed by Gregory Asner and his team at the Carnegie Institution expressly for nonprofit institutions and governments to use. Using the Landsat satellite images from 2004 and 2009 that were selected for model calibration, we obtain a map of deforestation in Claslite, demonstrating the change in forest cover between these two years. (Figure 6). These results have then been classified into two classes: 0 (no change) and 1 (change). These deforestation maps corresponds to the data set used for the calibration of Terra-i.. Figure 6. Deforestation detected by CLASlite. The images of detected change are then superimposed onto two sets of satellite source images, one from 2004 and the other from 2009, to verify that the results are real. When errors appear in the results (mainly caused by intensive agricultural areas, or bad cloud masking and highly sedimented bodies of water) it is necessary to mask and delete them manually through polygons, so that such errors do not interfere with the calibration process. In the end, CLASlite proved to be a very suitable tool for the calibration of the models of Terra-i models in areas with low altitude tropical forests, like the Amazonl. 12

13 4.2 Terra-i Calibration Our calibration approach is to compare the results of Terra-i with the calibration data set to find the optimal probability threshold for detection. This analysis is repeated for each class of the analysed area, allowing us to define the optimal threshold for each cluster. For each map of detections of the calibration data set and for each cluster of the analyzed area, three variables are defined: T, representing the total number of pixels detected by Terra-i, V, representing the total number of pixels found in the map validation, and M, representing the number of pixels detected by both Terra-i and the validation map. With these three variables, one can derive two indicators of model quality. The first is the model's ability to detect changes, which is calculated as follows: This corresponds to the proportion of pixels of validation also detected by Terra-i. This indicator varies from 0 to 1, where 0 represents a poor model calibration and 1 a perfectly calibrated model. The second indicator is the Terra-i ability to detect only changes in land cover. It is calculated as follows: This indicator is the proportion of pixels that Terra-i detects but that the calibration set does not. The indicator varies from 0 to 1, where 1 represents an uncalibrated model and 0 a perfectly calibrated model. Thus, the purpose of the calibration algorithm is to minimize I2, while maximizing I1. This is easily done by calculating I1 and I2 with different thresholds and then calculating a score P for each threshold corresponding to the Euclidean distance between the point T (I1,I2) and the optimal solution O (1,0). 13

14 The graph in Figure 7 is shows how the model score evolves for different thresholds and for each cluster. The lowest score means signifies the best threshold. Figure 7. Selection of threshold by cluster. Figure 8. Selected threshold for each cluster. 14

15 5 Validation Methodology 5.1 Validation Data To validate the results obtained by Terra-i, we used deforestation data for produced by the Brazilian National Institute for Space Research (INPE). Since 1983, the INPE has generated annual estimations of the rate of deforestation in the Brazilian Legal Amazon. From the year 2003 on, these estimation have been produced by a classification system known as Digital Image Project Estimation of Deforestation in the Amazon (PRODES). 2 Figure 9. Map of detection of PRODES digital in the Amazon. To create PRODES, the INPE developed a methodology to digitally process of TM / Landsat images, using the Linear Model of Spectral Mixture, which was implemented in the Information Processing System Georeferenciadas-SPRING/INPE. This model transforms the original bands TM3 (0.63 to 0.69 microns), TM4 (0.76 to 0.90 mm) and TM5 (1.55 to 1.75 microns) into image-fractions of "shadow", "vegetation" and "grounds." In the linear spectral mixing models, the response of each pixel of an image is considered as linear combination of spectral responses of each component in the mixture 3 2 (Instituto Nacional de Pesquisas Espaciais(INPE), 2004) 3 (Shimabukuro, Duarte, Mello, & Moreira, 2000) 15

16 To validate the results obtained by Terra-i in the Amazon, we compare the results obtained by our model and the PRODES estimation of deforestation in the Amazon between 2005 and Figure 10. Detection map of Terra-i and PRODES in the Amazon. 16

17 Deforestation Rate by State (Km 2 /year) State Acre Amazonas Amapá Maranhão Mato Grosso Pará Rondônia Roraima Tocantins Total Terra-i Validation Table 1. Deforestation rate by state of PRODES. Our validation approach is to compare the results of Terra-i with the validation data set. For each map of detections of the validation data set and for each cluster of the analyzed area, three variables are defined. T, representing to the total number of pixels detected by Terra-i, V, representing the total number of pixels found in the map validation, and M, representing the number of pixels that detected by both Terra-i and the validation map. With these three variables, one can derive two indicators of model quality. The first is the model's ability to detect changes and is calculated as follows: This corresponds to the proportion of pixels of validation also detected by Terra-i. This indicator varies from 0 to 1, where 0 represents a poor model calibration and 1 a perfectly calibrated model. The second indicator is the Terra-i ability to detect only changes in land cover. It is calculated as follows: This indicator is the proportion of pixels detected by terra-i that do not also belong to the validation set. This indicator varies from 0 to 1, where 1 represents an uncalibrated model and 0 a perfectly calibrated model. 17

18 Figure 11. Validation results with PRODES. As shown in the graph (Figure 11), when the results obtained by terra-i are validated with PRODES we obtain good results. We calculate, I1 as equal to 0.68, where 1 represents a perfectly calibrated model. This indicates that Terra-i has a good ability to detect changes that are also in the validation data set. We find that I2 is equal to 0.41, where 0 represent a perfectly calibrated model. this indicates that the level of noise in the detection is low. Con el fin de ampliar la validación de Terra-i con PRODES, se ha realizado un reporte mas detallado sobre el tema. In order to improve the validation of combining Terra-i with PRODES... In order to verify the Terra-i results, we are currently conducting more detailed studies with the PRODES model to compare and validate the results presented here. 18

19 6 Results By implementing the Terra-i model in the Amazon, we are then able to run it in 97.84% of the total area. The remaining 2.16% had too much cloud cover. Figure 12. Analyzed area in the Amazon. 19

20 Change Area (Ha) Near Real Time of Habitat Monitoring using a Neural Network and MODIS Data. By implementing the model Terra-i detected in the Amazon, the deforestation rate is 3,062,000 hectares per year between January 1, 2004 and May 25, Year Rate Ha/year Accumulative Rate ,722,931 3,722, ,790,425 6,513, ,815,494 9,328, ,108,956 12,437, ,424,637 15,862, ,509,938 18,372,381 Average 3,062,064 Tabla 2. Deforestation rate detected by Terra-i. Deforestation Rate detected by Terra-i in The Amazonas Year Figure 13. Deforestation rate detected by Terra-I in the Amazon. As shown in the graph (Figure 13), in 2004 Amazon had the highest rate of deforestation in the study phase, detecting a deforestation rate of 3,722,931 hectares. While noting some decrease in the rate of deforestation of the Amazon by year from 2004 to 2009, the data remains alarming and indicates that deforestation, should definitely be a conservation priority for environmental and agricultural authorities in every country that the Amazon covers. 20

21 6.1 Detection by Country By implementing the model Terra-i detected in the Amazon, the deforestation rate is 3,062,000 hectares per year. Figure 14. Change detected in Amazonian habitats. 21

22 Change Area (Ha) Near Real Time of Habitat Monitoring using a Neural Network and MODIS Data. Country Change in Protected Area (Ha) Accumulated Tabla 3. Deforestation rate in the Amazon, as detected by Terra-i. Rate (Ha/año) As shown in Table 3, the highest deforestation rate per unit area occurs in Brazil where there is an alarming rate of conversion of 2,816,225 ha / year. % Deforest Bolivia ,011 1% Brasil ,816,225 3% Colombia , % Ecuador , % Fre.Guyana , % Guyana , % Perú , % Suriname , % Venezuela , % TOTAL 3,722,931 2,790,425 2,815,494 3,108,956 3,424,637 2,509,938 18,372,381 3,062,064 2% In Brazil, forest loss is advancing mainly in the "arc of deforestation", which spans the states of Acre, Rondônia, south of Amazonas, northern Mato Grosso, southeast Pará, central and northern Tocantins, and Maranhão. However, it is important to note that deforestation in the Amazon, despite being higher in Brazil, is not just a problem in this country but throughout the whole basin. Deforestation Detection in Amazon Figure 15. Deforestation in the Amazon by country. 22

23 Change Area (Ha) Near Real Time of Habitat Monitoring using a Neural Network and MODIS Data. Deforestation Detection in Amazon without Brasil Figure 16. Deforestation detected in Amazon, excluding Brazil. Amazonian deforestation results in the loss of habitats and biodiversity, as well as the fragmentation of ecosystems. According to other studies in the period , 2,721,800 hectares were deforested per year, with loss of flora and fauna species. However, data limitations prevent good estimates of this loss. While there is local data on the status of biodiversity in the Amazon countries, there are neither overall statistics nor maps that illustrate biodiversity at the ecosystem level. This is why our detection system is an important advancement in monitoring deforestation and habitat loss in areas of high ecological importance such as the Amazon. 4 According to the latest GEO Amazon Report of the Amazonian countries, Brazil has the highest cumulative deforested area with km², which means that of the total area deforested by 2005, 79,5% occurred in Brazil, followed by Peru with 8.2% of the total deforestation of the period, and Bolivia and Colombia with 5.3 y 3.4%, respectively. The other countries contribute to below 2% of total. In 2004, the annual deforestation became the second highest in its history, with km², the first record in 1995, with km 2,according to de INPE/Programa de Cálculo do Desflorestamento da Amazônia (PRODES). 5 4 (Programa de las Naciones Unidas para El Medio Ambiente (PNUMA) y Organización del tratado de Cooperación Amazónica, 2009) 5 Idem 23

24 Below are maps of detection in each country of the Amazon: Figure 17. Terra-i detection in the Amazon-Colombia. Figure 18. Terra-i detection in the Amazon-Venezuela. 24

25 Figure 19. Terra-i detection in the Guyanas. Figure 20. Terra-i detection in the Amazon-Ecuador. 25

26 Figure 21. Terra-i detection in the Amazon-Peru. Figure 22. Terra-i detection in the Amazon-Bolivia. 26

27 Figure 23. Terra-i detection in the Amazon-Brazil. 27

28 6.2 Detection within Protected Areas For each protected area within the Amazon, we extract the deforested hectares for each analyzed year. We then repeat this process to include 5 km and 20 km buffer areas around the protected areas. The results are compiled in Excel tables (Annexes numbers II, II, IV). Figure 24. Detection map in protected areas of the Amazon. 28

29 Protected Areas with highest Deforestation Rate detected by Terra-i El Chore FE Rio Preto- Jacunda FE do Rio MequÚns FE do Rio Sao Domingos FE do Rio Vermelho FN Jamanxim FN do Bom Futuro Iñao Kaa-iya del Gran Chaco RB Nascentes da Serra do Cachimbo Figure 25.Protected areas with the highest deforestation rate. Thus it is possible to evaluate and compare the effectiveness of protected areas according to IUCN category and identify which of them are endangered due to habitat conversion activities on their outskirts. The Terra-i model detects that the protected areas with the highest rate of deforestation in the Amazon between 2004 and 2009 are: FE do Rio Sao Domingos in Brazil in the state of Rondonia where it has detected an average deforestation rate of ha / year and an alarming rate of deforestation of 4.54% and FN protected area in Brazil Bon Futuro in the state of Rondonia where the model detects an average deforestation rate of 5,434 ha/year and a deforestation percentage of 1.67%. (Figure 26) Figure 26. Protected areas with the highest deforestation rate. 29

30 AREANAME Change in Protected Area (Ha) Accum. Rate %Rate El Chore Z % FE Rio Preto-Jacunda VI % FE do Rio MequÚns Z % FE do Rio Sao Domingos Z % FE do Rio Vermelho VI % FN Jamanxim VI % FN do Bom Futuro VI % Iñao II % RB Nascentes da Serra do Cachimbo Ia % AREANAME IUCNCAT Table 4. Deforestation rate in the most threatened protected areas. Change between Protected Area and 5km buffer. (Ha) Accum. Rate %Rate El Chore % FE Rio Preto-Jacunda % FE do Rio MequÚns % FE do Rio Sao Domingos % FE do Rio Vermelho % FN Jamanxim % FN do Bom Futuro % Iñao % RB Nascentes da Serra do Cachimbo % AREANAME Table 5. Deforestation rate with 5km buffer, in the Protected Areas. Change between 5km buffer and 20km buffer. (Ha) Accum. Rate %Rate El Chore % FE Rio Preto-Jacunda % FE do Rio MequÚns % FE do Rio Sao Domingos % FE do Rio Vermelho % FN Jamanxim % FN do Bom Futuro % Iñao % RB Nascentes da Serra do Cachimbo % Table 6. Deforestation rate to 5km buffer, in the most threatened protected areas. 30

31 Deforestation R ate Percentage in Areas with greatest Threat 5.00% 4.00% 3.00% 2.00% 1.00% 0.00% El Chore FE Rio Preto- Jacunda FE do Rio MequÚns FE do Rio Sao Domingos FE do Rio Vermelho FN Jamanxim FN do Bom Futuro Iñao Kaa-iya del Gran Chaco RB Nascentes da Serra do Cachimbo Area Protegida Buffer 5km Buffer 20km Figure 27. Graph of the deforestation rate(%) in protected areas. As shown in Figure 27, according to Terra-i detections, the protected areas with the highest deforestation rates per unit area in the Amazon are under severe threat of deforestation. Such is the case of El Chore Forest Reserve located in Bolivia in the department of Santa Cruz, where the deforestation rate equals 0.72% within the boundaries of the reserve, but rises to 2.2% in the 5km buffer zone and 2.28% in 20km buffer zone. Figure 28. El Chore forest reserve with severe threat of deforestation. 31

32 6.3 Detection in Areas of TNC interest In these sections, we are present the results for a list of areas of TNC interest. Each area has been analyzed first by extracting the Terra-i's detections within the area and then by comparing them with the detections observed in the 50 km buffer area. Figure 29. Analysis of deforested areas in the Amazon. 32

33 Deforestation (Ha) Deforestation (Ha) Near Real Time of Habitat Monitoring using a Neural Network and MODIS Data. Figure 29 shows the deforestation rate per year, as detected by Terra-i between 2004 and 2009 in 10 areas of high ecological importance. The shapefiles were supplied by the TNC, with the goal of analyzing the degree of threat to which each is subject. Area name Change in Area (Ha) Area NoData Accum Rate Waiampi Serradiv Santarem Rrcanaima Paragomi Oiapoque Nascxingumt Mtnorte Mamobol Cofan Table 7. Deforestation rate in hectares per year in study areas. Area name Change in buffer. (Ha) Area NoData Accum Rate Waiampi Serradiv Santarem Rrcanaima Paragomi Oiapoque Nascxingumt Mtnorte Mamobol Cofan Table 8. Deforestation rate in hectares per year in 50km of study areas. The following graphs show the comparison of the hectares converted within the areas and in the buffer zones Waiampi 2800 Oiapoque km 50km 0 0km 50km Figure 30. Deforestation rate in Waiampi and Oiapoque. 33

34 Deforestation (Ha) Deforestation (Ha) Deforestation (Ha) Deforestation (Ha) Deforestation (Ha) Deforestation (Ha) Deforestation (Ha) Deforestation (Ha) Near Real Time of Habitat Monitoring using a Neural Network and MODIS Data Serradiv Nasczingumt km 50km 0 0km 50km Figure 31. Deforestation rate in Serradiv and Nasczingumt Santarem Paragomi km 50km km 50km Figure 32. Deforestation rate in Santarem and Paragimi Mtnorte 1600 Mamobol km 50km 0 0km 50km Figure 33. Deforestation rate in Mtnorte and Mamobol Cofan Rrcanaima km 50km 0 0km 50km Figure 34. Deforestation rate in Cofan and Rrcanaima. 34

35 The following graphs show the comparison of the conversion rates, in percentage of the total surface area, for each year within the protected areas and in the buffer zones. 1.60% Porcentaje de Deforestación en las Areas de estudio y en un Area de 50km alrededor de estas. 1.20% 0.80% 0.40% 0.00% Waiampi Serradiv Santarem Rrcanaima Paragomi Oiapoque Nascxingumt Mtnorte Mamobol Cofan Area Protegida Buffer 50km Figure 35. Deforestation percentage in study areas. As can be seen in the Figure 35, among the areas we studied in the Amazon, we found that Nascxingumt (in Mato Grosso), Paragonia (in Pará) and The Mtnorte (in Mato Grosso) are those with the highest deforestation rates per unit area, at with 1.53%, 1.29% and 0.66% respectively. (Table 9) We also observe that in the case of these three areas, as well as having the highest rates of deforestation within the protected areas, the surrounding 50km buffer zones are also under severe threat. AreaName % Rate Area % Rate Buffer Waiampi 0.00% 0.01% Serradiv 0.03% 0.06% Santarem 0.26% 0.18% Rrcanaima 0.02% 0.04% Paragomi 1.29% 1.48% Oiapoque 0.03% 0.02% Nascxingumt 1.53% 1.39% Mtnorte 0.66% 0.63% Mamobol 0.00% 0.01% Cofan 0.01% 0.02% Tabla 9. Deforestation percentage in the study areas. 35