Beyond Forest Conservation: Exploring the Impact of REDD+ on Livelihood and Detection of Forest Cover Change in Cross River State, Nigeria

To address the issue of climate change, the United Nations Framework Convention on Climate Change introduced REDD+ “Reducing Emissions from Deforestation and Forest Degradation”. Nigeria has lost 90% of its natural forest. The Cross River State has the largest proportion of the remaining tropical forests. In 2010, Nigeria joined the UN-REDD scheme to contribute to global climate change mitigation. Accordingly, the CRS became Nigeria's first REDD+ pilot state. Logging was therefore prohibited. A mixed-methods approach was used in this study to assess the impact of REDD+ in CRS. It involved key informant interviews, questionnaires, and remote sensing data. Sampling was done using a purposive and snowball approach. Autoregressive integrated moving average analysis was used to develop a model to predict the post-intervention period dependent on time. A simple linear regression of the residual values of the Normalized Difference Vegetation Index was used to determine the impact of the REDD+ program on the forest cover. The results indicate a slight positive impact. Time accounted for a 3.5% variation in vegetation cover of Akamkpa and Boki Local Government Areas after ten years of REDD+. However, more variables could be added to improve the model and identify the major drivers explaining variations in vegetation gain. A parametric t-test was also conducted, and the result was significant at (p<0.05) when compared to the ordinary least squares regression. Agriculture was the main economic activity in the study area. Furthermore, many respondents preferred agricultural skills\training and 67% desired more land for farming. This can have a detrimental effect on the CRS forest resources. The study proposes that future conservation efforts should consider forest community capacity-building preference before project commencement. Moreover, smallholder farmers should be empowered and trained to maximize yields on existing agricultural lands. Information, education, and communication materials should be made in local languages to raise awareness about REDD+, climate change, and forest conservation in Nigeria

MUSES Near-Infrared Reflectance of Vegetation (NIRV) 16-Day 30m Geographic Grid over Beijing Since 1984

&lt;p&gt;The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRV), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (&lt;a href="https://muses.bnu.edu.cn/"&gt;https://muses.bnu.edu.cn/&lt;/a&gt;).&lt;/p&gt; &lt;p&gt;This dataset is the MUSES NIRV product at 30 m spatial resolution and 16-day temporal resolution over Beijing. The MUSES NIRV product is provided on Geographic grid and spans from 1984 to 2022 (continuously updated). It was generated from the Landsat collection 2 surface reflectance data using a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method (Xiao &lt;em&gt;et al&lt;/em&gt;., 2015; Xiao&nbsp;&lt;em&gt;et al&lt;/em&gt;., 2017). The MUSES NIRV product is spatially complete and temporally continuous.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Dataset Characteristics:&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Spatial Coverage: 115.416599&ordm; E &ndash; 117.508219&ordm; E, 39.441929&ordm; N &ndash; 41.059283&ordm; N&lt;/li&gt; &lt;li&gt;Temporal Coverage: 1984 &ndash; 2022&lt;/li&gt; &lt;li&gt;Spatial Resolution: 0.000269469&ordm; (approximately 30 m)&lt;/li&gt; &lt;li&gt;Temporal Resolution: 16 days&lt;/li&gt; &lt;li&gt;Projection: Geographic&lt;/li&gt; &lt;li&gt;Data Format: HDF&lt;/li&gt; &lt;li&gt;Scale: 0.0001&lt;/li&gt; &lt;li&gt;Valid Range: 0 &ndash; 10000&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Citation&nbsp;&lt;/strong&gt;(Please cite this paper whenever these data are used)&lt;strong&gt;:&lt;/strong&gt;&lt;/p&gt; &lt;ol&gt; &lt;li&gt;Xiao Zhiqiang,&nbsp;&lt;em&gt;et al&lt;/em&gt;. (2015). Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices.&nbsp;&lt;em&gt;Remote Sensing&lt;/em&gt;, 7, 9844-9864&lt;/li&gt; &lt;li&gt;Xiao Zhiqiang,&nbsp;&lt;em&gt;et al&lt;/em&gt;. (2017). Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data.&nbsp;&lt;em&gt;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing&lt;/em&gt;, 10, 5551-5568&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;If you have any questions, please contact Prof. Zhiqiang Xiao ([email protected]).&lt;/p&gt; &lt;p&gt;&nbsp;&lt;/p&gt