The Mananara Nord National Park is located in the northeastern part of Madagascar. More precisely, this area is located in the district of Mananara Nord and the region of Analanjorofo. Its geographical coordinates are oriented between latitude 16˚10'00" and longitude 49˚46'00". This part presents tropical rainforests. We simulated a sample on a part of this zone which the commune of Antanambe (Figure 1).

A Sentinel-2 image is used that was acquired on November 07, 2016. The Sentinel-2 image comes directly from the European Space Agency (ESA) download site or more precisely in https://scihub.copernicus.eu.

The Sentinel-2 satellite is launched as part of the European Union’s Copernicus program which is equipped with the MSI sensor, it consists of two satellites, Sentinel-2A and Sentinel-2B: the first was launched in June 2015 and the second in March 2016. Each of these satellites provides all-land observation every 5 days, 10 m to 60 m resolution. The MSI sensor will provide 13 spectral bands from visible to mid-infrared illustrated in Table 1.

In 2014, inventory work was carried out in Madagascar by [7] on forest stratification. Landsat 5 images are used for the year 2005. A classification result from this work was used in the study.

SNAP (Sentinel Application Platform) is an ESA product specifically for processing Sentinel images. SNAP contains Sentinel toolkits with common architectures developed by Brockmann Consult, SkyWatch and CS. SNAP is an open-source solution with a number of toolboxes and plugins.

The Sentinel-2 image that we acquired is at pre-processing level 2a, i.e. it is no longer necessary to make the radiometric and geometric correction. We continue the pre-processing by combining the mono-spectral bands into a single multi-spectral band because when downloading Sentinel-2 images to the ESA site, the images are still separated into channels, so we need a transformation to obtain a multi-spectral image. Then, we cut the study area in order to minimize the processing time.

The NDVI thresholding technique is used for a pre-classification to discriminate vegetation to other land cover types [8]. NDVI values can range from −1 and 1. Higher values of the index correspond to the presence of dense and healthy vegetation cover. They are generally between 0.1 and 0.7 while clouds and snow result in NDVI values close to 0 [9].

The supervised classification [10] was used to determine the forest cover of the study area. We implemented the existing Random forest algorithm in the SNAP tool which is particularly effective in identifying relationships between a variable to be explained and explanatory variables [11].

To wait for the objective, we performed change detection between the 2016 classified Sentinel-2 image and the 2005 classification result from LandSat 5 [12] [13] [14]. The methodology is illustrated in Figure 2.

We have here a pre-classification result after the NDVI calculation. This index uses the near infrared band (PIR) and the red band (R) of the satellite image. In the case of Sentinel-2 images, the PIR is the eighth band and the R is the fourth band.

NDVI = ( P I R − R ) / ( P I R + R ) (1)

By applying Equation (1), we have the result presented in Figure 3 where the higher values of the index correspond to the presence of a dense and healthy vegetation cover.

Supervised classification is used using the Random forest algorithm. It is a classification algorithm based on parallel learning on multiple decision trees that improves prediction accuracy [11]. After integrating the field validation points, the SNAP tool gave the confusion matrix presented in the Table 2 below, which deduces an acceptable Kappa index of 94, 8863%. The result is presented in two classes (forest and non-forest) in Figure 4.

Change detection consists in comparing the classification in 2005 with Landsat 5 and the classification in 2016 with Sentinel 2 (Figure 5).

The transition matrix (Table 3) represents the percentage change in pixels for the two classes (Forest and No-Forest) between the two dates of 2005 and 2016.

According to the table, 70.044% of the forests classes and 75.652% of the no-forests classes remain stable the two dates of 2005 and 2016. During this 11-year period, the analysis shows a significant deforestation (Forest → Non-Forest) of 29.956% but also regeneration (Non-Forest → Forest) of 24.348%. The result is presented in Figure 6.

In terms of area, 16,687 ha of forest has been lost in 11 years. This means that there is a significant deforestation between two dates 2005 and 2016 (Table 4).

According to the Strategic Environmental and Social Assessment of Madagascar’s REDD+ program, the annual deforestation rate for the rainforest ecoregion

in Madagascar is 0.94% per year for the period 2010-2013. The Analanjorofo region is found among the regions with the highest annual deforestation rate. In terms of area, the Analanjorofo region lost 24,000 ha of forest for the period 2010-2013. Surveys conducted by ONE in 2016 were able to group four root causes of deforestation and forest degradation: The weakness of governance and institutions, the non-rational use of forest resources and forest areas, the weak coordination of the development of rural areas to the detriment of forests and the rural poverty and the lack of alternatives development alternatives.