Mina Nada’s Post

🛰️I am happy to share my new blog contribution "How to optimize input data for Species Distribution Models" related to the EarthBridge project! Go to our website and read the full text https://lnkd.in/geAh4Bec. #EarthObservation #EarthBridgeEU #HorizonEU

🌍🔍 Understanding Species Distribution Modeling (SDM) with Google Earth Engine! Species Distribution Modeling (SDM) is a game-changing tool in conservation biology. It helps us map where species like Cedrela fissilis 🌳 can potentially thrive, based on environmental conditions such as climate, land cover, and soil attributes. By linking species presence data to environmental variables, we can estimate the current distribution of a species and even predict future shifts due to climate change or habitat loss. 🌡️🌿 One of the most exciting aspects of SDM is how platforms like Google Earth Engine (GEE) make large-scale modeling more accessible than ever. With GEE, conservationists can access vast environmental datasets, apply machine learning algorithms, and process models rapidly — all from a cloud-based platform. 🚀🌐 In the research led by Dr. Ramiro D. Crego (Crego et al., 2022), SDM was implemented using GEE's JavaScript Code Editor, showcasing how powerful and efficient this tool is for species conservation! 🖥️✨ Using SDM, we can visualize the distribution of Cedrela fissilis, a species crucial to many ecosystems, and assess the areas where conservation efforts should be prioritized. 🗺️🌱 This model not only helps us understand where this species currently exists but also where it could exist if environmental conditions change. This is key for shaping strategies to protect biodiversity in the face of global challenges. 🌎🛡️ Check out this distribution map of Cedrela fissilis below! 👇 #SpeciesDistributionModeling #SDM #Conservation #GoogleEarthEngine #GEE #CedrelaFissilis #Biodiversity #MachineLearning #Ecology #ClimateChange #ConservationTech #DrRamiroCrego #SaveOurSpecies

📊 Over the past year, data management firm EcoSpatial Solutions has been working with the Canal & River Trust to gather baseline data and measure improvements in biodiversity net gain (BNG) accurately in England and Wales. 🗣️ With the new BNG framework in mind, the trust has an opportunity to discuss enhancements on its land with partners that want to make habitat improvements off site. 🗺️ It was therefore decided to build a GIS tool that would map the whole network using geospatial information to establish a baseline and then continuously track biodiversity. 💻 If you want to find out more about more about this project and GIS tool, join us for our upcoming webinar: Geography in practice: using GIS to survey habitats and measure improvements for biodiversity net gain. Our webinar is free to attend and is hosted alongside RICS, Canal & River Trust and EcoSpatial. 🗓️ Tuesday, 3 December ⏰ 12.00pm -1.00pm Sign up here 👉 https://lnkd.in/dNfNEGXt

New research paper: #EarthRanger’s capabilities and real-world impact on conservation   With its foundations as the Elephant Real-time Monitoring System, developed by Save the Elephants, read how EarthRanger has grown into a comprehensive platform adopted at over 600 sites and projects across 74 countries.    The study, published in Methods in Ecology and Evolution, highlights EarthRanger’s advanced capabilities, integrating data from wildlife collars, AI-powered camera traps, satellite imagery, and on-the-ground observations. It also explores real-world applications, showing how EarthRanger empowers conservationists, from Liwonde National Park to Washington state’s Olympic Peninsula, to combat poaching, monitor wildlife, and make data-driven decisions in real time.    👉 Read the open-access study here to learn more about EarthRanger’s impact and real-world applications. #Tech4Wildlife

Exciting news! A new study shines a spotlight on how #EarthRanger, first developed as the Elephant Real-time Monitoring System by Save the Elephants, is transforming conservation worldwide!

🌍 Exciting Milestone: Completed Course on Using Spatial Data for Biodiversity! I’m thrilled to share that I have recently completed a comprehensive course on Using Spatial Data for Biodiversity! In a world where conservation efforts and biodiversity management are more crucial than ever, understanding how to leverage spatial data is key to making informed decisions and driving impactful change. This course has been an enlightening journey, equipping me with valuable skills and insights to enhance our approach to preserving the natural world. Here are a few highlights from my learning experience: Advanced Spatial Analysis: Gained hands-on experience with UN Biodiversity Lab, GIS tools and techniques to analyze and visualize spatial data, which is vital for understanding habitat distributions and species movement patterns. Biodiversity Mapping: Learned how to create detailed maps that illustrate biodiversity hotspots and track changes in species diversity over time, providing crucial information for conservation planning. Data Integration: Explored methods for integrating diverse datasets to build comprehensive models that aid in predicting ecological changes and assessing the impacts of environmental policies. Real-World Applications: Delved into case studies and real-world scenarios where spatial data has been used to tackle pressing environmental issues, reinforcing the practical applications of the skills acquired. This course has not only deepened my understanding of spatial data but also strengthened my commitment to contributing to biodiversity conservation efforts. I am excited to apply these new skills to future projects and collaborate with like-minded professionals in the field. A big thank you to UNITED NATION DEVELOPMENT PROGRAMME(LEARNING FOR NATURE) for delivering such an informative and inspiring course. I look forward to the opportunities that lie ahead and to continuing the journey of learning and growth in this vital field. #Biodiversity #SpatialData #UNBiodiversityLab #GIS #Conservation #UNDP #Learningfornature

The ArcGIS Suitability Modeler is a powerful tool for identifying optimal areas for conservation. This model can integrate various layers, such as biodiversity importance (species richness and rarity), ecosystem services (proximity to water and land cover type), and degradation (vulnerability to change). These layers are rescaled and summed to pinpoint areas with high cumulative suitability scores. The "locate regions" feature of the model then identifies contiguous regions of a specified size, which can be prioritized for conservation efforts. https://lnkd.in/dAkzRWcb https://lnkd.in/dJ4whtQU #esri #esrisaudiarabia #deeplearning #imagesegmentation #machinelearning #imageclassification #computervision #imageprocessing #spatialdata #algorithms #objectdetection #neuralnetworks #datascience #geoai #ai #arcgispro #saudiarabia #riyadh #greenriyadh #green_riyadh #algorithm #imagerecognition #gis #spatialdata #mapping #maps #egypt #cairo #cairoegypt #remotesensing #urbandevelopment #GeoAI #deeplearning #vegetation #desertification #trees #buildinfootprint #segmentation #imagesegmentation #conservation #reservations #naturalresources #wildlifeconservation #wildlifeprotection #wildlife

🌍 Mapping Vegetation in Ghana: Leveraging NDVI with Google Earth Engine 🌿 Excited to share my recent work on classifying vegetation across Ghana using Sentinel-2 satellite imagery and Google Earth Engine! 🚀 🌱 Objective The aim was to assess vegetation health and distribution by classifying land cover into three vegetation categories based on the Normalized Difference Vegetation Index (NDVI): Dense Vegetation (NDVI ≥ 0.6): Areas with high vegetation cover, such as forests and dense agricultural fields. Sparse Vegetation (0.2 ≤ NDVI < 0.6): Regions with moderate vegetation cover, including grasslands or degraded areas. No Vegetation (NDVI < 0.2): Areas with minimal or no vegetation, such as urban centers, water bodies, or barren lands. 🛰️ Methodology 1️⃣ Data Collection: Imagery: Sentinel-2 Surface Reflectance imagery for January–December 2024. Filters: Applied a cloud cover threshold (<10%) for clear imagery. 2️⃣ Analysis: NDVI Calculation: Derived from Sentinel-2 bands B8 (near-infrared) and B4 (red). Classification: Dense vegetation: NDVI ≥ 0.6. Sparse vegetation: 0.2 ≤ NDVI < 0.6. No vegetation: NDVI < 0.2. Visualization: Applied a clear color palette—dark green for dense vegetation, light green for sparse vegetation, and white for no vegetation. 📊 Insights: This NDVI-based classification provides a snapshot of vegetation health across Ghana, helping identify areas that require conservation, sustainable land management, or reforestation. 💡 Applications Agriculture: Pinpointing fertile lands and monitoring crop health. Conservation: Supporting biodiversity protection efforts in dense vegetation areas. Urban Planning: Identifying low-vegetation areas for reforestation and urban greening. Climate Action: Providing baseline data for carbon sequestration studies. 💻 Tools Used: Google Earth Engine: For cloud-based geospatial processing. Sentinel-2 Imagery: High-resolution multispectral data for vegetation analysis. 🔗 Let’s Connect I’d love to hear your thoughts, discuss similar work, or explore collaborations in GIS, remote sensing, or sustainability. Let’s use geospatial technology to build a greener future! #GIS #RemoteSensing #EnvironmentalMonitoring #Sustainability #GoogleEarthEngine #VegetationMapping #ClimateAction #GeospatialInnovation

Thanks to our friends at TERN Australia, a great opportunity with University of Tasmania and University of Adelaide. The NatureScan project aims to advance biodiversity monitoring in Australia by leveraging low-cost drone technology, cutting-edge artificial intelligence (AI), and expertise in field ecology. Working under general direction, the Spatial Field Ecologist will have a general focus on developing the tools and techniques for accurate and robust biodiversity mapping, including experts in field ecology, remote sensing, data processing, artificial intelligence, and biodiversity.

🌟 Exciting News for Ecology Enthusiasts! 🌿🔍 We’re thrilled to unveil #idopNetwork, a transformative tool tailored for the complex world of spatial community ecology. This innovative network tool empowers researchers, conservationists, and environmental planners to uncover and analyze the intricate ecological relationships that define our natural environments. 🚀 What makes idopNetwork unique? Visual Mapping See the unseen! Our advanced mapping reveals the hidden connections within ecosystems. Dynamic Data Analysis Dive into the dynamics of ecological interactions across different scales and time periods. User-Friendly Whether you're a seasoned ecologist or a budding environmental scientist, idopNetwork is built for you. 👥 We believe in the power of community and collaboration. How do you handle data in your ecological research or conservation efforts? What tools do you find indispensable? 💬 Let’s start a conversation! Share your experiences, challenges, or a quick tip on ecological data analysis in the comments below. We’re here to learn from each other and push the boundaries of what our communities can achieve together. 🔗 Want to know more about how idopNetwork can transform your projects? Check out in ink https://lnkd.in/e8ta5h6e #Ecology #Conservation #CommunityEcology #SpatialAnalysis #Biodiversity #EnvironmentalScience #Innovation #Networking #EcologicalConservation