Abstract. It is estimated that the globe’s forest has shrunk by 3% since 1990, an area equivalence to the geographical boundaries of South Africa. The Kisatchie National Forest of Louisiana replicates plentiful climatic, physiographic and edaphic differences in the country and this forest faces a serious problem of degradation and disturbance of different nature. Remote sensing from satellites offers the best way to observe these changes over time. This study will employ Landsat-8 satellite imagery to analyze forest cover change in Kisatchie National Forest from 2010 to 2020. The objectives of the study are to (i) identify the trend, nature, and the magnitude of forest cover change, (ii) prepare image maps delineating forest cover change for the duration of the study (iii) establish the trend of CO2 levels within Kisatchie environs. Results showed a gain of forest cover within the Kisatchie National Forest which correlated to the rate of CO2 sequestration by sinks. NDVI of 2010 was 0.65 compared to 0.86 for 2020 indicating a gain of 32% of forest cover since 2010. This showed how effective Protected areas are in conserving forest cover and restricting land uses that may disturb forest structure.

Abstract. Global climate change has affected the rate of rising sea level, the frequency, intensity, timing, and distribution of hurricanes and tropical storms which threatens coastal ecosystems such as Bayou Perot, Little Lake in New Orleans along the Gulf of Mexico. The impact of hurricanes could include wetland and coastal land loss. This paper compared the land cover changes around Bayou-Perot- Little Lake, New Orleans, USA following Hurricanes Ida (August 26, 2021 to August 28, 2021). Two high-resolution Sentinel 2 imagery dated before and after Hurricane Ida was compared to assess the impacts of the hurricane on the land cover around Bayou Perot. A Random Forest classification (RF) algorithm in Google Earth Engine was used to produce maps and identify areas that have experienced conversions in land use or land cover change after the hurricane. This method of classification has the advantages of high classification accuracy and the ability to measure variable importance in land-cover mapping. In addition to random classification algorithm, other analysis such as the Normalized Difference Vegetation Index (NDVI) was be used to gain a better perspective of the overall changes in vegetation across the landscape. Five main classes were considered after the classification which included water, vegetation, bare soil, built up and marsh area. The results of the land cover change showed exposed old coastal marsh, valuable dune habitat providing storm protection to estuaries, wetlands, and the coastal population destroyed.

Abstract. The coast of Louisiana is a major zone of the Gulf of Mexico and an ecologically critical area for both carbon sequestration and habitation of diverse ecosystems. The ten major marine sectors each have annual GDPs of tens of billions of dollars annually. In 2019 alone, these sectors provided 2.4 million high-paying jobs, 397 billion in goods and services and another estimated 667.5 billion in sales. Aside these obvious benefits that coastal wetlands provide, they also help to reduce inland flooding and coastal erosion. According to the National Oceanic and Atmospheric Administration (NOAA), about 32% of Louisiana alone is made up of wetlands. The U.S. Geological Survey estimates that Louisiana has been losing wetlands since the late 1930’s and that the current rate of loss will result in total wetland loss in another two hundred years. Satellite data were obtained from Landsat 8 satellite imaging. The data was trained and processed using QGIS free software to produce maps. The maps were then analyzed and interpreted. The results of this study affirmed a gradual decline in wetland area with a major increase in vegetation cover in Dulac, supporting some findings by the USGS in 2017 which classified Louisiana’s current rate of as low compared to the 1930’s and 1970’s. However, wetland dynamics is a complex series of events that occur over time and requires constant tracking and monitoring to provide evidence-based practical and applicable results that will suit the ever-emerging dynamics of management, policymaking, restoration, and management of wetlands themselves.

Abstract. As part of Earth’s nutrient cycle, a layer of air travels every summer from Africa across the Atlantic Ocean. In June 2020, the thickest and densest dust plume traveled over 5000 miles along with the Saharan Air Layer (SAL) from Africa towards the USA and the Caribbean. Due to its gravity and impact, it was nicknamed “Godzilla”. While the cause of this event remains unclear, the advantage of using remote sensing applications to monitor aerosol concentrations and movement provides future opportunities to leverage machine learning technologies to build predictive models with the goal of early forecasting and public health interventions. The Sentinel-5P satellite instrument measures the air quality, ozone, and Ultraviolet (UV) radiation, and can be used for climate monitoring, and forecasting. Available on this platform is the UV Aerosol Index (AI) product, a qualitative index that indicates the presence of elevated layers of aerosols in the atmosphere. In this paper, we used Google Earth Engine to monitor the transatlantic movement of this historic dust plume across the Sahara Desert and estimate the aerosol concentrations throughout June 2020. The flexibility of the platform enabled us to generate time series maps to visualize the movement of the Godzilla dust storm from the Sahara Desert across the ocean. The results obtained are relevant for effective planning and interventions to ameliorate the health threats associated with the movement of the dust plume. The outcome is useful for defining the relationship between aerosol concentrations, human health, and aquatic life.