Evaluating the land cover dynamics in the protected areas using GIS and remote sensing techniques: the case of Nyerere National Park, Tanzania

Abstract

Understanding land cover dynamics of protected areas is one area of active research and several studies have been done in this direction. However, such studies are limited with few parameters and lack a long-range spatial-temporal analysis to effectively understand land cover dynamics and thereby helping countries manage their protected areas sustainably. This research used Nyerere National Park (NNP) to explore its land cover dynamics from 1991 to 2021 and projected to 2050, vegetation health from 2000 to 2021 and surrounding human population from 1988 to 2021. The park’s land cover of 1991 and 2021 was explored using a smileCart classifier after training 897 samples of water, bareland, grassland, bushland and forest from the Landsat imagery. Its 2050 land cover was simulated using CA-Markov model. The park’s vegetation health was studied using NDVI and EVI from the Landsat and MODIS imagery. Land cover classes with significant changes are forest and grassland. The forest areas showed a decreasing trend from 62%-to-52%-to-41% from 1991-to-2021-to-2050, while the grassland areas showed an increasing trend from 9%-to-17%-to-24%. The maximum NDVI values from the Landsat imagery showed a minimal decrease from 0.76 in 1991 to 0.75 in 2021. Many park’s areas have weak vegetation based on the overall NDVI and EVI results. The study also identified rapid increase in human population around the park, and agricultural activities taking place in some of its areas. The results of this study provide a new reference to NNP and other studies in all other protected areas.

Keywords:

• Land cover changes
• vegetation health
• protected areas
• Nyerere National Park
• GIS
• remote sensing

Acknowledgements

The authors extend their sincere thanks to Surv. Lukas Mbilinyi from TANAPA for his support and guidance led to the successful completion of this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

This work has no extra data besides with those presented in the main text of the document.

Additional information

Funding

This work is funded by the Natural Science Foundation of Chongqing, China (Grant Number: 2022NSCQ-MSX2069) and the Ministry of Education of China (Grant Number: 19JZD023).