Remote sensing is a powerful tool for mapping and monitoring conifer species in GIS. Conifers are evergreen trees that have needle-like leaves and produce cones. They are important for biodiversity, carbon storage, timber production, and recreation. In this article, you will learn how to use remote sensing data and methods to identify, classify, and map conifer species in GIS.
To map conifer species in GIS, you need remote sensing data that can capture the spectral, structural, and temporal characteristics of the trees. There are different types of remote sensing data sources, such as aerial photography, satellite imagery, lidar, and radar. Each data source has its own advantages and limitations, depending on the spatial resolution, spectral resolution, temporal resolution, and cost. For example, aerial photography can provide high spatial and spectral resolution, but it is expensive and may not cover large areas. Satellite imagery can cover large areas and have frequent updates, but it may have low spatial and spectral resolution. Lidar can measure the height and structure of the trees, but it is also expensive and may not be available for all areas. Radar can penetrate clouds and vegetation, but it may be complex and noisy.
I think that the question should be rephrased as "How can we map conifer species using remote sensing?" instead of "How can you use remote sensing to map conifer species in GIS?". GIS is a system for managing spatial data, not a tool that directly maps conifer species. The mapping results from a process that involves processing and analyzing remote sensing data to identify and classify conifer species. Several techniques can be used to map conifer species; these techniques rely on the unique spectral signatures of conifer trees, which can be captured by various remote sensing data sources. The choice of technique depends on the specific application and the availability of data. The process and result of the analysis can be stored in a GIS.
In my experience, mapping conifer species in GIS requires a comprehensive understanding of remote sensing data. For instance, in a forestry project, we utilized lidar data to accurately assess the vertical structure of conifer forests, enabling precise species mapping and habitat analysis. I agree that each remote sensing data source offers distinct advantages and limitations. Aerial photography provides detailed spatial and spectral information but can be costly. Satellite imagery covers large areas but may lack resolution. Lidar offers valuable tree structure data but comes with a price tag. Radar's ability to penetrate vegetation is offset by complexity and noise. Understanding these factors crucial in selecting the most suitable RS.
In my GIS and RS research experiences, mapping conifer species involves acquiring high-resolution satellite or aerial imagery, followed by crucial pre-processing steps like radiometric and geometric correction. Image interpretation, using supervised or unsupervised classification, is validated through ground truthing from field surveys. Integration with GIS layers allows for spatial analysis, and accuracy is assessed using confusion matrices. Leveraging species-specific spectral signatures and data from sources like forest inventories enhances classification precision. This integrated approach enables the creation of accurate conifer species distribution maps for effective forest management and ecological analysis.
Mapping species usually needs hyperspectral data due to more spectral data can be acquired to differentiate between species. However hyperspectral data is expensive and rare. So the other solution might be to use the common public multispectral data such as Landsat. To help differentiate between species, you can use multiple masks where the only images left if the Conifer cover. You can mask the vegetation, then mask only the dense forest/conifer. To map species you still need field data to know where species are located then using this data as a sample you can predict using a machine learning model to classify which conifer belongs to a species.
To map conifer species in GIS using remote sensing, analyze satellite or aerial images for unique coniferous vegetation colors. Use classification techniques to distinguish conifers from other land cover types. Field surveys can help validate the results. Consider LiDAR data for three-dimensional details. By integrating these data into GIS, you can create precise maps showing where conifer species are located.
To classify and map conifer species in GIS, you need remote sensing methods that can extract and analyze the relevant information from the data. There are different types of remote sensing methods, such as supervised, unsupervised, object-based, and machine learning. Each method has its own steps, inputs, outputs, and accuracy. For example, supervised methods require training samples of known conifer species to create a classification model. Unsupervised methods do not require training samples, but they group the data into clusters based on similarity. Object-based methods segment the data into meaningful units, such as individual trees or stands. Machine learning methods use algorithms that can learn from the data and improve their performance.
To classify the conifer species, it is common to use supervised methods such as machine learning models like random forests. You can use field data as a sample input. To enhance the different spectral values between species you can use PCA (principal component analysis).
Remote sensing methods for conifer species mapping in GIS involve high-resolution imagery, pre-processing corrections, and supervised or unsupervised classification. Ground truthing validates results, and GIS integration enables spatial analysis. Accuracy is assessed using confusion matrices, while species-specific spectral signatures and data from sources like forest inventories enhance mapping precision, supporting effective forest management.
I would try all the methods mentioned and select the one which gives the best result. There are differences, like: - Supervised Classification needs training samples and remote sensing data, often resulting in High Accuracy, but the usability of the results depends on the training data (local focus). - Unsupervised Classification uses only remote sensing data to create, for example, clusters of species. The accuracy is lower than with supervised methods because the method is purely data-driven and depends on the internal feature relationships (broader scope). - Machine learning (Deep Learning) is also possible and uses remote sensing data, with optional training samples. This method can yield high-accuracy results but is expensive to run.
- J'ai utilisé une classification orientée objet avec des données Lidar et hyperspectrales pour cartographier plusieurs espèces de mangrove dans le delta du fleuve Ouémé au Bénin. La haute résolution et l'information 3D du Lidar couplées à la richesse spectrale de l'hyperspectral ont permis une extraction très fine des espèces. - Les méthodes de segmentation d'images puis de classification sont de plus en plus performantes avec l'amélioration des résolutions. La combinaison de données de types variés (optique, radar, lidar...) augmente encore les résultats pour identifier avec précision différentes essences forestières.
To perform remote sensing tasks and create maps of conifer species in GIS, you need GIS tools that can handle and process the data. There are different types of GIS tools, such as desktop, web, or mobile applications. Each tool has its own features, functions, and interfaces. For example, desktop tools can provide more advanced and customized options, but they may require more computing power and storage space. Web tools can provide more accessibility and collaboration, but they may depend on internet connection and data security. Mobile tools can provide more mobility and convenience, but they may have limited functionality and compatibility.
In QGIS, tools such as the Semi-Automatic Classification Plugin (SCP) and Orfeo ToolBox provide capabilities for image classification, including supervised and unsupervised methods. QGIS also integrates with various plugins for data visualization and analysis. In ArcGIS, the Image Classification toolbar facilitates supervised and unsupervised classification processes. The Spatial Analyst extension in ArcGIS is commonly used for raster analysis, allowing for sophisticated spatial operations. Additionally, the ModelBuilder tool in ArcGIS enables the creation of custom workflows for processing and analyzing remote sensing data.
A good resource here is the Google Earth Engine, to prototype the solution, it is true that this option requires a stable internet connection, but it removes the need for a user architecture in the early phases of the project. When the classification is successful, the project can be converted to a Web App, which is then accessible to a larger user group, before setting up a client-server-side to run all the operations in-house. Using a local desktop for processing large remote sensing datasets is generally not recommended because of the computational and hardware limitations it imposes. However, for smaller datasets or for specific tasks that require more control over the processing workflow, ArcGIS and QGIS can be effective tools.
GIS tools work by integrating five key components: hardware, software, data, methods, and people. GIS tools automate tasks that were historically done manually, such as compiling new maps by overlaying one on top of the other or physically cutting maps into pieces representing specific study areas, then changing their projection.
J'utilise régulièrement QGIS pour tous les traitements d'images satellite et la réalisation de cartographies d'occupation du sol. Sa riche bibliothèque d'algorithmes, son interface intuitive et son co?t très abordable en font un outil de choix. Les solutions cloud comme ArcGIS Online permettent de partager facilement les cartes et les données SIG avec de multiples partenaires. Cette accessibilité accrue et cette collaboration sont très utiles pour les projets multi-sites impliquant des acteurs variés.
Before you can use remote sensing data and methods to map conifer species in GIS, you need to prepare the data for analysis. Data preparation involves steps such as data acquisition, data quality assessment, data preprocessing, and data integration. Each step has its own challenges and solutions, depending on the data source and method. For example, data acquisition may involve searching, downloading, or requesting the data from different providers or platforms. Data quality assessment may involve checking the data for errors, gaps, or distortions. Data preprocessing may involve correcting, enhancing, or transforming the data to make it suitable for analysis. Data integration may involve combining or overlaying different data sources or layers to create a composite data set.
See my previous comment about using Google Earth Engine, handling remote sensing data can be complex and performing all steps in a local (client) environment can slow down the prototyping. GEE can be used for all the steps mentioned, and then after fine-tuning of the process(es) be transferred to a client-server location. The different Data Preparation steps also depend on the selected method, process, and source data. For example, locally flown drone data will need more data preparation than the analyse-ready datasets from Copernicus and NASA.
Lors d'un projet de cartographie d'espèces de mangroves, la collecte de relevés de terrain et l'échantillonnage d'images pour l'entra?nement du classifieur m'ont pris beaucoup de temps. Une planification méticuleuse amont est essentielle. Une évaluation rigoureuse de la qualité et une standardisation des données de sources multiples sont indispensables avant l'analyse. Toute erreur ou approximation à ce stade affecte les traitements en aval. L'automatisation au maximum de la cha?ne de traitements (masques nuages, corrections atmosphériques, mosa?ques...) via des toolboxes Python ou GEE minimise les manipulations et accélère l'analyse.
After you have prepared the data, you can use remote sensing methods to analyze the data and map conifer species in GIS. Data analysis involves steps such as data exploration, data classification, data validation, and data visualization. Each step has its own techniques and outputs, depending on the data source and method. For example, data exploration may involve inspecting, plotting, or summarizing the data to understand its characteristics and patterns. Data classification may involve applying, testing, or comparing different methods to assign labels or categories to the data. Data validation may involve assessing, measuring, or improving the accuracy or reliability of the results. Data visualization may involve creating, displaying, or exporting maps or charts to communicate the results.
The Geographical Information System (GIS) will be instrumental in this step of the process, because it can be used for: - Data Exploration: visually inspect the raw and processed data - Data Validation: create different maps of the results and scenarios to evaluate the accuracy of the model and improve the performance by modifying the input data (feature engineering) - Data Visualization: creating maps is an excellent method for Communicating findings and highlighting spatial patterns The classification process can be managed in the GIS as well, but how this will be implemented depends too much on the individual project needs, so it's difficult to give an exact example of how the data classification step is embedded in the process.
Lors d'un projet de cartographie d'occupation des sols, j'ai d? tester plusieurs algorithmes de classification (SVM, Random Forests) et jeux de bandes spectrales avant de trouver la combinaison optimale pour distinguer efficacement différents types de cultures. Une phase exploratoire approfondie des données, avec des visualisations et statistiques descriptives, est indispensable pour bien comprendre le signal contenu dans les images avant de se lancer dans la classification. L'analyse de séries temporelles pour caractériser les variations saisonnières de réflectance m'a souvent aidée à mieux séparer des classes spectrales initialement confusionnées sur une image unique.
The final step of using remote sensing to map conifer species in GIS is data interpretation. Data interpretation involves steps such as data synthesis, data evaluation, and data application. Each step has its own goals and implications, depending on the data source and method. For example, data synthesis may involve integrating, summarizing, or explaining the results in relation to the objectives and questions. Data evaluation may involve critiquing, comparing, or discussing the results in relation to the literature and context. Data application may involve applying, sharing, or updating the results for decision making or action taking.