September/24 Report
DEFORESTATION MONITORING TECHNICAL REPORT
Last updated
DEFORESTATION MONITORING TECHNICAL REPORT
Last updated
The study area comprises the farm "Fazenda Floresta Amazônica" located in the municipality of Apuí- AM (Figure 1). According to the vegetation classification by IBGE (2021), the farm area is situated within two types of vegetation: Open Ombrophilous Forest (OOF) and Dense Ombrophilous Forest (DOF).
This technical report uses NDVI vegetation cover monitoring to monitor possible changes in vegetation within the area called "Amazon Forest Farm", as well as detect possible degraded and deforested areas. Furthermore, based on images of deforestation alerts from the INPE and Imazon database, deforestation alerts were monitored in the period from September 1st to 30th, 2024, both in the farm area and in its surroundings.
In remote sensing, monitoring changes in vegetation cover at regional and global scales, the normalized difference vegetation index (NDVI) is commonly used (Shi et al., 2021, Wu et al., 2020). NDVI is an important indicator that reflects the state of vegetation, which is affected by precipitation, anthropogenic activities, temperature and soil water content.
For the elaboration of the NDVI map in the specified period between 09/01/2024 to 09/15/2024 and 09/16/2024 to 09/30/2024, sentinel L2A satellite images were utilized. On 09/12/2024, satisfactory conditions for the creation of the NDVI map were identified, with cloud coverage at 20%. Additionally, the image from 09/20/2024 was applied, featuring cloud coverage at 10%.
Following the standard procedure, NDVI was obtained by applying an algorithm based on the difference in near-infrared (NIR) and red reflectance divided by their sum of spectral bands (1), where NIR represents near-infrared electromagnetic spectrum and R the red electromagnetic spectrum, ranging between -1 and 1 (ROUSE et al., 1974), as follows:
Landsat 8
09/12/24
2024
B5-B4
Landsat 8
09/20/24
2024
B5-B4
Table 1. Specifications of the satellite image used for NDVI calculation
Additionally, a Soil Adjusted Vegetation Index (SAVI) map was developed to assess and confirm that thereis no deforestation occurring within the study area. The Soil Adjusted Vegetation Index (SAVI) is a variation of the Normalized Difference Vegetation Index (NDVI) that takes into account the influence of soil on the spectral response of plants. It is used to estimate vegetation density and vigor by correcting soil effects present in satellite images.
The same images from NDVI captured on August 09th were used. The formula for SAVI is as follows:
Where: NIR is the reflectance value in the near-infrared band; RED is the reflectance value in the red band; and L is the soil adjustment factor, which ranges from -1 to 1 (typically set to 0.5).
The inclusion of the soil adjustment factor (L) in the formula allows compensating the influence of soil reflectance, especially in areas with sparse vegetation or exposed soil. The value of L is typically set to 0.5, but it can vary depending on the characteristics of the vegetation and soil in the study area.
To provide a more comprehensive understanding of the indexes used in the study area, a Normalized Burn Ratio (NBR) map was also created in QGIS. This index is widely used to detect areas affected by fires (Key and Benson, 1999), deforestation, or damaged vegetation. It utilizes the near-infrared (NIR) and mid-infrared (MIR) bands from satellite images to map the extent and severity of burns. Landsat 8 satellite images, specifically bands 5 and 7 dated from September 20th, 2024, were used (available at https://earthexplorer.usgs.gov/).
The formula for NBR is as follows:
Where: NIR is the reflectance value in the near-infrared band, and MIR is the reflectance value in the midinfrared band.
The Normalized Burn Ratio (NBR) varies on a scale of values depending on the methodology and scale used. However, a common convention is for NBR to range from -1 to 1. Negative values of NBR (-1 to 0) typically indicate areas affected by recent fires, deforestation, or damaged vegetation. Values closer to -1 indicate higher severity of the burn or vegetation damage. Positive values of NBR (0 to 1) are often associated with areas of healthy vegetation. Values closer to 1 indicate greater vegetation vigor.
To assess deforestation expansion around the farm "Fazenda Floresta Amazônica" area, a radius of 50 km was determined. Databases from deforestation alerts provided by INPE and Imazon were utilized. The data obtained for February were in shapefile format, and all processing and filtering of alert points within the radius were conducted using QGIS software.
The Imazon Deforestation Alert System (SAD) currently employs NASA's Landsat 7 and 8 satellites, as well as ESA's Sentinel 1A, 1B, 2A, and 2B satellites. SAD detects forest degradation or deforestation events that occur in areas larger than 1 hectare.
INPE utilizes imagery from the WFI sensor on the CBERS-4 satellite and the AWiFS sensor on the Indian Remote Sensing Satellite (IRS), which have spatial resolutions of 64 and 56 meters, respectively.
The data is sent daily to the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA) without a minimum mapped area restriction. However, for the general public, polygons are made available with a minimum size of 6.25 hectares, allowing for comparison with data generated by the PRODES project.
The identification of forest cover change patterns is conducted through visual interpretation based on five main elements (color, tone, texture, shape, and context), using the Spectral Mixture Analysis (SMA) technique along with multispectral imagery in color composition to map the following classes:
Deforestation with exposed soil and deforestation with vegetation and mining;
Degradation, forest fire scar;
Selective Logging Type 1 (Disordered)
Selective Logging Type 2 (Geometric).
Analysis of the spatio-temporal distribution of the Normalized Difference Vegetation Index (NDVI), obtained from Landsat 8 images dated from September 12th, revealed a wide variety of values, covering the range from -0.01 to 0.52. These data offer a significant interpretation of vegetation cover in the study region. Lower NDVI values, between 0.1 and 0.0, generally correspond to bodies of water, such as rivers, and small areas devoid of vegetation along the banks of these bodies of water. This distinction occurs due to the presence of water, which reduces NDVI values, as well as the opening of the forest canopy in these regions. On the other hand, values above 0.4 are related to areas of dense and healthy forests. This higher range of NDVI indicates robust vegetation, reflecting the density and health of the forest canopy in these specific areas.
When examining the NDVI images dated September 20th (as illustrated in Figure 3), there was a continuity in values similar to the NDVI recorded on September 12 th, varying between 0,02 and 0.51. The consistency of these patterns over time strengthens the understanding of the stability and vitality of vegetation cover in the region under analysis.
When examining the Soil Adjustment Vegetation Index (SAVI), a variation in values between 0.03 and 0.72 was identified. These results reinforce the conclusion that there is no evidence of deforestation or significant human activities in the study area. Values in the range of 0.02 to 0.3 can be associated with water bodies, such as rivers, and exposed areas on river banks. On the other hand, values greater than 0.35 indicate areas covered by dense forests and in a good state of preservation.
During the analysis of the Normalized Burning Index (NBR) on September 20th, considerable variations were observed in the mapped values, ranging between 0.01 and 0.52. No negative values wereidentified; however, values close to zero were detected, indicating that the rivers in the Amazon are at low volume, increasing the exposure of sandbanks and rocks along the riverbanks.
Furthermore, the absence of negative values in the NBR indicates that there were no significant biomass losses due to fires, which is a positive sign for the conservation of local biodiversity. Positive index values suggest relatively healthy vegetation and soil that has not suffered degradation from fires. The analysis of this data is fundamental for planning environmental management strategies, allowing the implementation of preventive measures against forest fires and the promotion of sustainable land use practices.
During the month of September, 8 deforestation alert points were identified within a 50 km radius of the farm. As illustrated in Figure 6, six of these points are located at the limit of the radius, close to the city of Apuí; and two points are at the upper limit of the radius. It is also observed that the month of September recorded the same number of deforestation alert points as the month of August. In addition, the Amazon region is facing one of the worst droughts in recent years, with the highest number of fires since 2014.
As per Table 1, the UTM coordinates of each deforestation alert point were inserted, facilitating location for future inspections.
The consistency of NDVI values throughout September suggests stability in the region's vegetation cover, with no significant signs of deforestation or forest degradation. The SAVI complemented this analysis by indicating the absence of deforestation or significant human activities, while the NBR corroborated the absence of recent fires, confirming the integrity and resilience of the forest ecosystems during this period.
However, the detection of eight deforestation alert points within a 50 km radius of the farm during September highlights the need for continuous monitoring and mitigation actions to prevent the expansion of these activities. These alerts underscore the importance of monitoring and control to avoid environmental degradation. The information obtained is crucial to support conservation policies, sustainable land use management, and the implementation of protection measures, ensuring the preservation of natural ecosystems and the ecological balance of the region.
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Botucatu (SP), October 9th, 2024.
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