Concept to Practice of Geospatial-Information Tools to Assist Forest Management and Planning under Precision Forestry Framework: a review

Precision forestry is a new direction for better forest management. Precision forestry employs information technology and analytical tools to support economic, environmental and sustainable decision; the use of geospatial information tools enables highly repeatable measurements, actions and processes to manage and harvest forest stands, simultaneously allowing information linkages between production and wood supply chain, including resource managers and environmental community. In this report, we reviewed the most recent advances in the use of geospatial information technologies in forestry, and discussed their potential opportunities and challenges towards forest management and planning in the framework of precision forestry

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Terrestrial laser scanning for crop monitoring. Capturing 3D data of plant height for estimating biomass at field scale

Terrestrial laser scanning (TLS) is a young remote sensing method, but the trustworthiness of such measurements offers great potential for accurate surveying. TLS allows non-experts to rapidly acquire 3D data of high density. Generally, this acquisition of accurate geoinformation is increasingly desired in various fields, however this study focuses on the application of TLS for crop monitoring. The increasing cost and efficiency pressure on agriculture induced the emergence of site specific crop management, which requires a comprehensive knowledge about the plant development. An important parameter to evaluate this development or rather the actual plant status is the amount of plant biomass, which is however directly only determinable with destructive sampling. With the aim of avoiding destructive measurements, interest is increasingly directed towards non-contact remote sensing surveys. Nowadays, different approaches address biomass estimations based on other parameters, such as vegetation indices (VIs) from spectral data or plant height. Since the plants are not taken it is feasible to perform several measurements across a field and across the growing season. Hence, the change of spatial and temporal patterns can be monitored. This study applies TLS for objectively measuring and monitoring plant height as estimator for biomass at field scale. Overall 35 TLS campaigns were carried out at three sites over four growing seasons. In each campaign a 3D point cloud, covering the surface of the field, was obtained and interpolated to a crop surface model (CSM). A CSM represents the crop canopy in a very high spatial resolution on a specific date. By subtracting a digital terrain model (DTM) of the bare ground from each CSM, plant heights were calculated pixel-wise. Manual measurements aligned well with the TLS data and demonstrated the main benefit of CSMs: the highly detailed acquisition of the entire crop surface. The plant height data were used to estimate biomass with empirically developed biomass regression models (BRMs). Validation analyses against destructive measurements were carried out to confirm the results. The spatial and temporal transferability of crop-specific BRMs was shown. In one case study, the estimations from plant height and six VIs were compared and the benefit of fusing both parameters was investigated. The analyses were based on the TLS-derived CSMs and spectral data measured with a field spectrometer. The important role of plant height as a robust estimator was shown in contrast to a varying performance of BRMs based on the VIs. A major benefit through the fusion of both parameters in multivariate BRMs could not be concluded in this study. Nevertheless, further research should address this fusion, with regard to the capability of VIs to assess information about the vegetation cover or biochemical and biophysical parameters

The acquisition of Hyperspectral Digital Surface Models of crops from UAV snapshot cameras

This thesis develops a new approach to capture information about agricultural crops by utilizing advances in the field of robotics, sensor technology, computer vision and photogrammetry: Hyperspectral digital surface models (HS DSMs) generated with UAV snapshot cameras are a representation of a surface in 3D space linked with hyperspectral information emitted and reflected by the objects covered by that surface. The overall research aim of this thesis is to evaluate if HS DSMs are suited for supporting a site-specific crop management. Based on six research studies, three research objectives are discussed for this evaluation. Firstly the influences of environmental effects, the sensing system and data processing of the spectral data within HS DSMs are discussed. Secondly, the comparability of HS DSMs to data from other remote sensing methods is investigated and thirdly their potential to support site-specific crop management is evaluated. Most data within this thesis was acquired at a plant experimental-plot experiment in Klein-Altendorf, Germany, with six different barley varieties and two different fertilizer treatments in the growing seasons of 2013 and 2014. In total, 22 measurement campaigns were carried out in the context of this thesis. HS DSMs acquired with the hyperspectral snapshot cameras Cubert UHD 185-Firefly show great potential for practical applications. The combination of UAVs and the UHD allowed data to be captured at a high spatial, spectral and temporal resolution. The spatial resolution allowed detection of small-scale heterogeneities within the plant population. Additionally, with the spectral and 3D information contained in HS DSMs, plant parameters such as chlorophyll, biomass and plant height could be estimated within individual, and across different growing stages. The techniques developed in this thesis therefore offer a significant contribution towards increasing cropping efficiency through the support of site-specific management