MAPPING FIRE SEVERITY FROM RECENT CALIFORNIA WILDFIRES USING SATELLITE IMAGERY

Urban sprawl has become a huge concern for cities like Los Angeles, New York, and Chicago in recent years. As urban sprawl pushes urbanization into city suburbs and outskirts, forest fragmentation becomes evidently prevalent and exposes forests to high temperatures, pollution, pests, and fires that threaten forest health. A 2021 report titled Rebuilding for a Resilient Recovery affirmed that the frequency and damage potential of wildfires have been exacerbated by climate change and urban sprawl especially in California. Globally, these fires can be attributed to both natural and anthropogenic drivers such as deforestation, agriculture, mining, and industrialization. Future projections predict that these incidences of fires will only worsen as the planet continues to warm further, with emphasis on the spread and intensities of the annual California wildfires over the decade. Quantifying the consequences of these fires on global climate change has become crucial and with the emergence of advanced GIS mapping tools, focus, visualization, and interpretation of fire and burn severity has become easier. However, knowledge and understanding of wildfire dynamics is limited especially in terms of fuel load, impacts on vegetation health, aerosol release and associated movement in the atmosphere. It is therefore important to address these gaps to make better and informed actions towards forest use, protection, management, and policies and broadly towards ambitious climate goals such as the UN’s Carbon Neutral goal by 2050. This study uses Sentinel 2A data from the Copernicus fleet between 2018 and 2022 to identify and assess the burn severity of affected areas in Sonoma County, California. The aim of the study is to understand the impacts of fires of fire on vegetation health and the post-fire recovery process. The Normalized Burn Ration Index (NBRI) was used to identify and measure the extent of the burnt areas within the county and their severity and Normalized Difference Vegetation Index (NDVI) was used as a measure of forest heath. The results show that Sonoma County has become a high burn severity area with a major decrease in unburned areas between 2018 and 2022. NDVI values recorded all decrease from January to December for all the years because of pre-fire season drought. The wildfire season begins in May and before then there are seasonal droughts that occur hence accounting for the initial decline in NDVI. The least values recorded were between 0.5 and 0.57 for September, indicating sparse and unhealthy vegetation because of sharp declines during the fire season

ANALYZING THE IMPACT OF SEA LEVEL RISE ON COASTAL FLOODING AND SHORELINE CHANGES ALONG THE COAST OF LOUISIANA USING REMOTE SENSORY IMAGERY

Sea level rise poses risks to coastal areas which is increasingly rendering such areas susceptible to flood and shoreline retreat. Notably, coastal areas like Southern Louisiana located along the Gulf of Mexico has experienced endangering events of land subsidence due to flood inundations resulting from incessant distribution of hurricanes and tropical storms. This research therefore employed remote sensing data to analyze the impacts of sea level rise on coastal flooding and shoreline retreat along the coast of Louisiana. That is, by assessing Sentinel-2 imagery data to evaluate flood prone and flood extent areas particularly during the Louisiana floods and Hurricane Harvey. Based on this, the results show most of the inland parishes in coastal Louisiana such as Assumption, St. James, Livingston, Lafourche and Terrebonne were within high flood risk zones of about 9.3. These parishes also suffered severe damage in terms of affected croplands, potentially flooded areas and affected urban areas. On the other hand, most of the parishes in close proximity to the waterbodies such as the Gulf of Mexico were interestingly within low flood risk zones of about 6.1 suggesting proximity to waterbodies not being the only indicating factor of a flood prone area. This research also highlights that Louisiana's shorelines are rapidly receding at a rate that could result in the loss of one million acres of the state’s land in the next four decades. Hence, the results from this research are anticipated to contribute to sustainable shoreline setback plans and mitigative strategies to protect Louisiana's coast

Genetic studies of extra-early provitamin-A maize inbred lines and their hybrids in multiple environments

Open Access Article; Published online: 25 Sept 2019Vitamin A deficiency, drought, low soil nitrogen (low N) and Striga hermonthica parasitism of maize (Zea mays L.) cause malnutrition and food insecurity in sub-Saharan Africa. The objectives of this study were to determine combining abilities of extra-early provitamin A (PVA) lines, classify them into heterotic groups (HGs), identify testers, and determine yield stability of hybrids under contrasting environments in two trials. In trial 1, 20 extra-early PVA lines were inter-mated in a diallel mating scheme to obtain 190 F1 hybrids. The 190 F1 hybrids plus six checks were tested under Striga infestation, drought, and stress-free environments in Nigeria from 2015 to 2017. In trial 2, 35 extra-early yellow hybrids were evaluated under low-N, Striga-infested and stress-free environments in 2018. Provitamin A concentrations of 23.98 and 22.56 μg g-1 were obtained for TZEEIOR 202 and TZEEIOR 205. TZEEIOR 197 × TZEEIOR 205 (20.1 μg g-1) and TZEEIOR 202 × TZEEIOR 205 (22.7 μg g-1) contained about double the PVA level of the commercial check, TZEEI 58 × TZEE-Y Pop STR C5 (11.4 μgg-1). Both general (GCA) and specific (SCA) combining ability variances were statistically significant for most agronomic traits, although GCA was much larger than SCA effects, indicating that additive genetic effects primarily controlled the inheritance of those traits. TZEEIOR 97 and TZEEIOR 197 were identified as inbred testers. TZEEIOR 197 × TZEEIOR205 (20.1 μg g-1) was identified as a single-cross tester as well as the most stable and highest-yielding hybrid across environments. TZEEIOR 202 and TZEEIOR 205 should be invaluable resources for breeding for high PVA. PVA level was independent of hybrid yield potential, indicating that selection of superior hybrids with elevated PVA levels should be feasible

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

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