Annotated datasets are essential for training and evaluating machine learning models in forest ecology. This dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species. Data were acquired using Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS), include field-measured attributes such as species identity and Diameter at Breast Height (DBH), and terrestrial and aerial RGB imagery. TLS point clouds were georeferenced and co-registered with centimetre-level accuracy, enabling precise integration with ALS data. The dataset includes segmented individual trees and wood–leaf classifications, suitable for applications such as tree morphology analysis, biomass estimation, and species classification. To support benchmarking, outputs from established classification algorithms (LeWoS, TLSeparation, CANUPO, and Random Forest) are included. As one of the first open-access LiDAR datasets from Indian tropical forests, it provides critical reference data for developing and validating forest structure models. It can also aid biomass mapping efforts in support of large-scale missions such as NASA-ISRO’s NISAR and ESA’s BIOMASS.

Green areas play an important role within urban agglomerations due to their impact on local climate and their recreation function. For detailed monitoring, frameworks like the flora fauna habitat (FFH) classification scheme of the European Union’s Habitat Directive are broadly used. By date, FFH classifications are mostly expert-based. Within this study, a data-driven approach for FFH classification is tested. For two test areas in the municipality of Vienna, ALS point cloud data are used to derive predictor variables like terrain features, vegetation structure and potential insulation as well as reflection properties from full waveform analysis on a 1 m grid. In addition, Sentinel-1 C-Band time series data are used to increase the temporal resolution of the predicting features and to add phenological characteristics. For two 1.3 × 1.3 km test tiles, random forest classifiers are trained using different combinations (ALS, SAR, ALS+SAR) of input features. For all model test runs, the combination of ALS and SAR input features lead to best prediction accuracies when applied on test data.

At the end of the 1980s the Municipal Department for Environmental Protection of Vienna - MA 22 initiated a detailed biotope mapping on the basis of the Viennese nature conservation law. Approximately 40 % of Vienna’s city area were covered, however only 2 % of the densely populated areas. This biotope mapping was the basis for the biotope types mapping (2005-2011) and of the green areas monitoring (2005). An update of these surveys has been planned in order to meet the various requirements of urban nature conservation and the national and international, respectively, legal monitoring and reporting obligations.Since the 1970s the municipality of Vienna has built up a comprehensive database and uses state-of-the-art methods for collecting geodata carrying out services for surveying, airborne imaging and laser-scanning. Currently systems for mobile mapping, oblique aerial photos and a surveying flight with a single photon LiDAR system are being implemented or prepared. Because of the numerous high-resolution data available within the municipality and limitations mainly in spatial resolution of satellite data, the City of Vienna saw no need or benefit in integrating satellite images until now.However, satellite data are now available within the European Copernicus program, which have considerable potential for monitoring green spaces and biotope types due to their high temporal resolution and the large number of spectral channels and SAR data. For the first time, the Sentinel-1 mission offers a combination of high spatial resolution in Interferometric Wide Swath (IW) recording mode and high temporal coverage of up to four shots every 12 days in cross-polarization in the C-band. The Sentinel-2 satellites deliver multispectral data in 10 channels every 5 days with spatial resolutions of 10 or 20 m.Within the SeMoNa22 project, various indicators are derived for the Vienna urban area (2015-2020) and used for object-oriented mapping and classification of biotope types and characterization of the green space: Sentinel-1 data (→ time series on the annual cycles in the backscattering properties of the vegetation, phenology), Sentinel-2 data (→ multispectral time series via parameters for habitat classification / vegetation indices), High-resolution earth observation data (airborne laser scanning (ALS), image matching, orthophoto → various parameter describing the horizontal and vertical vegetation structure). The main goals of SeMoNa22 is to explore efficient and effective ways of knowing if, how and to what extent the data collected can form the basis and become an integrative part of urban conservation monitoring. For this purpose, combinations of different earth observation data (satellite- and aircraft- supported or terrestrial sensors) and existing structured fieldwork data collections (species mapping, soil parameters, meteorology) are examined by means of pixel- and object-oriented methods of remote sensing and image processing. The study is done for several test sites in Vienna covering different ecosystems. In this contribution the ongoing SeMoNa22 project will be presented and first results will be shown and discussed.

Airborne laser scanning (ALS) point clouds are employed for the generation of country-wide digital terrain and surface models (DTMs and DSMs) and to derive further information about forested areas. This acquisition method has been established as a state-of-the-art of topographic data acquisition, especially in forested areas. However, as ALS data acquisitions are done on relatively low temporal resolution (e.g. for Austria every 6-10 years), forest parameter extraction with high temporal resolution based on ALS data is limited. In particular, the derivation of dynamic forest information such as biomass or canopy cover changes requires relatively high temporal resolution.Aerial images, along with their image-matching-based point clouds (IM), provide a further option for the creation of DSMs. Especially in areas with high vegetation such as forests, the ALS and IM data yield different elevation values.The aim of this study is to systematically quantify these differences and to investigate strategies to approximate IM-based DSMs to the ALS-based DSMs. For this research, a study site within the Vienna Woods Biosphere Reserve in the Eastern part of Austria was selected for the development and evaluation of an approach to minimise the height differences. For this area ALS and IM datasets from the same month are available.Initially, topographic models, such as the normalised DSM (nDSM), were derived from the available point clouds. Statistical parameters for different kernel sizes of the image matching nDSM were further calculated within a derived canopy mask. These parameters as predictors, along with the known differences of the nDSMS based on ALS and IM as target values, were used to train a random forest regression to further fit the IM to the ALS data.The validation, conducted on three different areas, showed an approximation of the elevation values to the ALS nDSM utilised as a reference within the canopy mask. This improvement demonstrates a promising approximation of the two models of about 77% in relation to the median of the deviations between the adjusted and the given model compared to the initial situation. The IM data shows its limitations in elongated gaps in the canopy, as the closing effects of small canopy gaps in forested areas pose challenges for the IM-based nDSM. In such instances, the regression function cannot make any improvements.

Context: Dry stone walls are a prominent landscape feature integrating archaeological, ecological, and landform process aspects, but inventories of these features are often missing. Airborne laser scanning (ALS) provides data for efficient area-wide detection of linear landscape features like dry stone walls. Objectives: We developed an automated approach to generate a comprehensive inventory of dry stone walls in terraced landscapes using statewide ALS data. Our objectives were to derive high-resolution digital terrain models (DTMs) and compare different methods to extract dry stone walls from DTMs and DTM-based features. Further, we produced a map of dry stone walls for the study site based on the best-performing method. Methods: The study site is the 213 km ² Wachau UNESCO World Heritage Site (Austria). We generated reference data for 206 ha for model training and testing. We investigated Random Forest classification of Canny edges and deep learning (DL) segmentation architectures on the DTM and DTM-based feature composites. Results: Model evaluation yields F1 scores of 0.72 for the Random Forest classifier and 0.84–0.86 for the DL models. Comparable performance is observed when training on RGB composites of DTM-based features and on the normalized DTM alone. Convolutional and transformer-based architectures show no clear performance differences, indicating that DL-based detection of linear topographic features is robust to both input feature design and model architecture when using high-resolution elevation data. The area-wide application results in a final dataset of 925 km of detected dry stone walls across the Wachau region. The derived density map shows dry stone wall densities up to 2740 m/ha. Conclusions: Our results show that publicly available ALS data allow reliable, automated mapping of dry stone walls across terraced landscapes. The Wachau-wide mapping provides a basis for heritage documentation, habitat assessments, and future landscape-scale monitoring of the state of dry stone walls. The study demonstrates how DL methods can enhance landform mapping for landscape management and monitoring.