Evaluation of Appropriate Identification of Deforestation Agents and Drivers for Designing REDD+ Readiness Activities through an Examination of the Area around Gunung Palung National Park, Indonesia

For effective REDD+ implementation with multiple readiness activities, agents and drivers of deforestation and forest degradation needs to be identified appropriately. This study examined how such identification can be utilized for instituting REDD+ activities design. We examined this question by using satellite imagery analysis and socioeconomic surveying around Gunung Palung National Park in Indonesia. After recognizing the deforestation rate in the area, the characteristics of agents and drivers of deforestation were explored by using statistical analysis. Several canonical discriminant analyses revealed that the agents and drivers could be classified effectively by using socioeconomic type rather than ethnic groups or geographical location. A principal component analysis and the associated scatter diagrams showed that various agents and drivers exist in a given area within the study region. Finally, these efforts led to the suggestion of options for REDD+ readiness activities based on the diverse features and underlying causes.

Has global deforestation accelerated due to the COVID-19 pandemic?

As the COVID-19 pandemic unfolded,questions arose as to whether the pandemic would amplify or pacify tropical deforestation.Early reports warned of increased deforestation rates;however,these studies were limited to a few months in 2020 or to selected regions.To better understand how the pandemic infl uenced tropical deforestation globally,this study used historical deforestation data(2004–2019)from the Terra-i pantropical land cover change monitoring system to project expected deforestation trends for 2020,which were used to determine whether observed deforestation deviated from expected trajectories after the fi rst COVID-19 cases were reported.Time series analyses were conducted at the regional level for the Americas,Africa and Asia and at the country level for Brazil,Colombia,Peru,the Democratic Republic of Congo and Indonesia.Our results suggest that the pandemic did not alter the course of deforestation trends in some countries(e.g.,Brazil,Indonesia),while it did in others(e.g.,Peru).We posit the importance of monitoring the long-term eff ects of the pandemic on deforestation trends as countries prioritize economic recovery in the aftermath of the pandemic.

Identifying the Rates and Drivers of Spatiotemporal Patterns of Land Use and Land Cover Changes in the Hurungwe District, Zimbabwe: A GIS and Remote Sensing Approach

Identifying spatiotemporal patterns of land use and land cover changes (LULCC) and their impacts on the natural environment is essential in policy decisions for effective, sustainable natural resource management solutions. This study employed supervised image classification in Google Earth Engine (GEE) cloud-based platform to assess the land cover land use changes for the past 30 years (1989-2020), as well as predict the land cover states and the risk of future forest loss in the next ten years, using TerrSet 20 software in Hurungwe district, Zimbabwe. The study findings revealed a net forest area and shrub loss of 32% and 10%, while croplands, water bodies, and bare lands have increased by about 171%, 7%, and 119% between 1989 and 2020, respectively. Croplands are the major contributor to the net change in forests, particularly tobacco farming. The predictive model estimated that by 2030 the district would lose approximately 7% of the current forest cover area, most likely converted into croplands, shrubs, and settlements. The results reinforce the importance of bridging the gap between socioeconomic activities and institutional policies to ensure proper natural resource management. Integrating institutional policy and socioeconomic goals is indispensable to ensure sustainable development.

Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

The rate of forest degradation and deforestation in Nigeria has been increasing over the years and is prominent in the southwestern parts. Despite the significant change and degradation observed in a lowland rainforest in the region—Ogbese Forest Reserve, there is a great dearth of information about the level of forest cover change. Therefore, this study determined the cover dynamics of the rainforest reserve over the epoch of 20 years using Geographic Information System and remote sensing techniques. Coordinates of the boundary and some other benchmark places within the forest reserve were obtained. Secondary data collection included: Landsat imageries of 1998, 2002 and 2018. An interview guide was used to obtain information from forest officials and locals of the surrounding communities to complement the spatial data obtained. Image classification was done using the maximum likelihood algorithm. The rate of change across the epochs was determined using the area of the land cover classes. The level of vegetation disturbance in the reserve was determined through Normalized Difference Vegetation Index. Five different forest cover classes were identified in the study area: forest, plantation, farmland, grassland, and bare land. The natural forest reduced significantly from 34.43 km2 (48%) in 1998 to 8.73 km2 (12%) in 2002 and was depleted further by 2018, while other cover classes increased. NDVI value also reduced from 0.25 to 0.13. Agriculture, among others, was observed as the main driver of forest degradation and deforestation in Ogbese Forest Reserve. The study concluded that the remaining forest (i.e. plantation) could also be depleted by 2025, as it decreases by −0.94 km2 per year if proper reforestation and management practices are not introduced.