Output data from: Land Use and Land Cover Map of Mount Namuli and surroundings in Mozambique

This repository includes the land use and land cover map of the Gurué region and the Mount Namuli in Mozambique, using remote sensing and Sentinel 2 images.

Gurué is located in the northwest of the Zambezia province and 150 km from the Malawi border. The Mount Namuli Massif, located in the north of the city, covers an area of about 200 km² at an altitude above 1200 m (Timberlake et al., 2009). The highest point of the massif, Mount Namuli, reaches 2,419 m. It is the second highest peak in Mozambique after Mount Binga (2,436 m) located in the Chimanimani National Reserve (Manica Province). The region surrounding Mount Namuli is inhabited by local communities who rely on it heavily for ecosystem services. Although, the area’s biodiversity is greatly threatened by conversion of forests and grasslands by these communities for subsistence and local market agriculture. There is minimal local government involvement in the area for conservation activities or social services, and thus there has been no effective management of natural resources. Mount Namuli is relatively small in extent but incredibly diverse and a part of the unique mountain island chain of inselbergs in northern Mozambique. Mount Namuli’s slopes are covered by a mosaic of forests, grasslands, and agricultural land. Rates of habitat loss, particularly across high conservation value areas above 1,200 meters, are increasing, driven primarily by the introduction of crops, such as the Irish potato, which exhaust the soils. The high rates of forest conversion underway on the mountain’s upper slopes must be halted immediately and long-term plans for natural resource management must be implemented if Mount Namuli’s remaining biodiversity is to be retained.

The methodology used in this study is based on a classical approach of remote sensing: satellite image collection (cloud-free and shadow free Sentinel 2, 10 m resolution, two season), identification of land use typology (based on field campains), delineation of training plots, supervised classification of land use using a statistical model (Random Forest) and finally, calculation of land occupation statistics.

The methodology and statistics are presented in the report included in the repository.