A STUDY ON THE SHORELINE CHANGES AND LAND USE/LAND COVER ALONG THE KETA COASTAL ZONE

The Keta Municipality has undergone a rapid increase in population due to economic and commercial activities. This led to the municipal’s coastal and shoreline zone being faced with severe environmental challenges throughout the years. The goal of the study was to evaluate Keta’s shoreline changes and the coastal Land Use Land Cover (LULC) using multi-temporal remote sensing datasets. These datasets were subjected to the following image processing techniques such as image enhancement, image classification and, shoreline extraction. The Digital Shoreline Analysis System (DSAS), a plugin tool in ArcGIS was utilized to assess the rate of shoreline changes (i.e., erosion or accretion) from 2000 to 2020. These were achieved based on the following statistical methods used; Linear Regression Rate (LRR), Net Shoreline Movement (NSM), and End Point Rate (EPR). The LULC analysis indicated that built-up areas and water bodies have increased rapidly from 14.71–18.43%, and 47.68–50.46% respectively from 2000 to 2021. In terms of the shoreline changes, LRR showed a mean of −0.95m/year with 68.22% faced with erosion and accretion of 31.78%. The EPR and NSM revealed a mean shoreline change of −1.19m/year and −26.3/period respectively from 2000 to 2021. The EPR and NSM results both revealed that 69.24% experienced erosion and 30.76% accretion, indicating the prevalence of erosion at the shoreline. This research is to contribute to both the development of Keta’s shoreline protection and management measures as well as sustainable land use planning. Also, aids in achieving most of the Sustainable Development Goals in the municipal

TRACKING THE GODZILLA DUST PLUME USING GOOGLE EARTH ENGINE PLATFORM

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

ASSESSING LAND COVER CHANGE AROUND BAYOU PEROT-LITTLE LAKE, NEW ORLEANS USING SENTINEL 2 SATELLITE IMAGERY

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

APPLICATION OF REMOTE SENSING IN MONITORING FOREST COVER CHANGE AND CARBON DIOXIDE LEVELS AT KISATCHIE NATIONAL FOREST OF LOUISIANA

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

USING REMOTE SENSING TO DETECT FOREST COVER CHANGE IN SAM HOUSTON NATIONAL FOREST, TEXAS

The Sam Houston National Forest is a large, forested area in Texas that has experienced significant land-use changes over the past few decades. The study area replicates plentiful climatic, physiographic, and edaphic differences in the country and this forest faces a serious problem of degradation and disturbance of different nature. In this study, we utilized remote sensing technology specifically Landsat 4 ETM and Landsat 8 from USGS Earth Explorer with spatial resolution 30 m, to analyze forest cover change in Sam Houston National Forest from 2001 to 2020. We also employed the Hansen Global Forest Cover Data from the Google Earth Engine Catalogue to assess the forest cover loss and gain within the study period. Also, the i-Tree software was used to estimate carbon sequestration in the forest and assess the potential benefits of forest management practices. Results of the study showed that the Sam Houston National Forest has experienced a net loss of forest cover over the past few decades, primarily due to agricultural expansion and urbanization. However, the forest has also shown signs of regrowth and recovery in certain areas, highlighting the potential for effective forest management practices to promote carbon sequestration and conservation. Overall, our study highlights the importance of remote sensing technology for understanding forest cover change and its implications for carbon sequestration and climate change mitigation

ASSESSMENT OF WETLAND DYNAMICS AND LOSS IN TERREBONNE PARISH USING REMOTE SENSING

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

Urbanization and urban forest loss: a spatial analysis of five metropolitan districts in Ghana

ABSTRACTThere have been many concerns raised on the impacts of increasing urbanization on sensitive urban ecosystems such as soils, water bodies and forests. The lack of governmental programs and policies further hinders the protection and conservation of these ecosystems especially in developing countries such as Ghana. This research seeks to understand how rapid urbanization has changed urban forest cover within five metropolitan districts, Accra, Cape Coast, Kumasi, Sekondi-Takoradi and Tamale over the years. In pursuit of this objective, Landsat 7ETM+ and 8OLI images were acquired and analyzed for the years 1990, 2000, 2010 and 2020 using Geographic Information Science (GIS) and Remote Sensing techniques. Results showed substantial loss of urban forests in these districts over the 30 year period and increased in built and cultivated land areas. Data also showed increasing population in these districts over the study period. The study concludes with a recommendation for growth management in the country and the urgent need for policy reforms that integrate urban forests and ecosystems protection into mainstream planning and development in the country