Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China)

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during the growing season, as well as their driving forces in China from 2001 to 2012. Our results showed that the growing season NDVI increased continuously during 2001–2012, with a linear trend of 1.4%/10 years (p < 0.01). The NDVI in north China mainly exhibited an increasing spatial trend, but this trend was generally decreasing in south China. The vegetation dynamics were mainly at a moderate intensity level in both the increasing and decreasing areas. The significantly increasing trend in the NDVI for arid and semi-arid areas of northwest China was attributed mainly to an increasing trend in the NDVI during the spring, whereas that for the north and northeast of China was due to an increasing trend in the NDVI during the summer and autumn. Different vegetation types exhibited great variation in their trends, where the grass-forb community had the highest linear trend of 2%/10 years (p < 0.05), followed by meadow, and needle-leaf forest with the lowest increasing trend, i.e., a linear trend of 0.3%/10 years. Our results also suggested that the cumulative precipitation during the growing season had a dominant effect on the vegetation dynamics compared with temperature for all six vegetation types. In addition, the response of different vegetation types to climate variability exhibited considerable differences. In terms of anthropological activity, our statistical analyses showed that there was a strong correlation between the cumulative afforestation area and NDVI during the study period, especially in a pilot region for ecological restoration, thereby suggesting the important role of ecological restoration programs in ecological recovery throughout China in the last decade

Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

In recent decades, the monitoring of vegetation dynamics has become crucial because of its important role in terrestrial ecosystems. In this study, a satellite-derived normalized difference vegetation index (NDVI) was combined with climate factors to explore the spatiotemporal patterns of vegetation change during the growing season, as well as their driving forces in China from 2001 to 2012. Our results showed that the growing season NDVI increased continuously during 2001–2012, with a linear trend of 1.4%/10 years (p < 0.01). The NDVI in north China mainly exhibited an increasing spatial trend, but this trend was generally decreasing in south China. The vegetation dynamics were mainly at a moderate intensity level in both the increasing and decreasing areas. The significantly increasing trend in the NDVI for arid and semi-arid areas of northwest China was attributed mainly to an increasing trend in the NDVI during the spring, whereas that for the north and northeast of China was due to an increasing trend in the NDVI during the summer and autumn. Different vegetation types exhibited great variation in their trends, where the grass-forb community had the highest linear trend of 2%/10 years (p < 0.05), followed by meadow, and needle-leaf forest with the lowest increasing trend, i.e., a linear trend of 0.3%/10 years. Our results also suggested that the cumulative precipitation during the growing season had a dominant effect on the vegetation dynamics compared with temperature for all six vegetation types. In addition, the response of different vegetation types to climate variability exhibited considerable differences. In terms of anthropological activity, our statistical analyses showed that there was a strong correlation between the cumulative afforestation area and NDVI during the study period, especially in a pilot region for ecological restoration, thereby suggesting the important role of ecological restoration programs in ecological recovery throughout China in the last decade

Distribution of hunter groups and environmental effects on moose harvest in Interior Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2018Moose (Alces alces) is one of the most valuable wild game resources in Interior Alaska. In recent years, residents of rural indigenous communities have expressed concern that climate change and competition from non-local hunters are challenging local moose harvest opportunities. I collaborated with wildlife agencies and village tribal councils to co-design two studies to address rural community hunter concerns. The first study assessed the spatial and temporal distribution of local and non-local hunter groups to examine areas of potential competition. The second study addressed changing environmental factors and their impacts on moose harvest. Although competition among local hunters or among non-local hunters certainly occurs, competition between local and non-local hunters, or between resident and non-resident hunters is a more common and reoccurring issue. Local hunters are those who hunt in the area in which they reside whereas non-local hunters travel away from the area they reside to hunt. I assessed hunting patterns by local and non-local hunters in a remote hunting region near the interior villages of Koyukuk and Nulato to quantify moose harvest overlap between these two user groups to assess potential competition. I used Alaska Department of Fish and Game (ADFG) moose harvest records to develop a relative competition index that identified locations and time periods within the hunting season where the greatest overlap occurred from 2000-2016. I determined that the highest competition occurred between 16-20 September (i.e., peak harvest period) and was concentrated predominantly along major rivers. To decrease overlap and mitigate potential competition between hunter groups we recommend providing information on competition hotspots to hunters, or lifting the no-fly regulation in the Koyukuk Controlled Use Area with the caveat that hunting with the use of aircraft must occur 1.6 km from the Koyukuk River corridor. These actions may provide hunters information on how to re-distribute themselves across the landscape and allow hunters to use areas away from rivers, where most harvest currently occurs. Additionally, climate change and seasonal variability have anecdotally been documented to impact moose hunting opportunities. Specifically, warm temperatures, delayed leaf drop, and fluctuating water levels are concerns expressed by some local hunters. I quantified changes in temperature, leaf drop, and water level near Koyukuk and Nulato and the subsequent relationships between these environmental variables and the total number of moose harvested using linear regression models. I used temperature data, gauging station data (i.e., water level), remote sensing data (i.e., leaf drop analysis), and ADFG moose harvest records and explored previously untested hypotheses and to quantify relationships from 2000-2016. I concluded that non-local hunter harvest success was more dependent than local harvest success on environmental conditions. Non-local harvest significantly increased with higher water levels from 6-10 Sept (p=0.02), 11-15 Sept (p=0.02), and 16-20 Sept (p<0.01), and decreased with warmer temperatures in the same three time periods (p<0.01, p=0.02, p<0.01, respectively). Local harvest increased with higher water levels from 16-20 Sept (p<0.01). These results quantitatively show that environmental factors do impact hunter success. I speculate that local hunter harvest success is less dependent on environmental variability because they have the ability to harvest opportunistically, rely more heavily on the resource, and reside near the hunting area. This ability to opportunistically hunt and adapt may give them an advantage over non-local hunters as environmental conditions shift with climate change.Chapter 1: Introduction -- Chapter 2: Assessing moose harvest patterns to address hunter competition -- Chapter 3: Quantifying effects of environmental factors on moose hunting success -- Chapter 4: Conclusion

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