In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

Summary: Seventeen ferrets were examined because of progressive bilaterally symmetric alopecia that was nonpruritic. Dermatologic and endocrinologic testing were used to determine the cause of the alopecia. Resting cortisol, testosterone, and thyroxin concentrations and results of acth stimulation tests were found to be within reference range limits established for this species. High serum estradiol concentrations were found to be a reliable indicator of adrenal cortical neoplasia in these ferrets.

The desire to obtain a better understanding of ecosystems and process dynamics in nature accentuates the need for observing these processes in higher temporal and spatial resolutions. Linked to this, the measurement of changes in the external structure and phytomorphology of plants is of particular interest. In the fields of environmental research and agriculture, an inexpensive and field-applicable on-site imaging technique to derive three-dimensional information about plants and vegetation would represent a considerable improvement upon existing monitoring strategies. This is particularly true for the monitoring of plant growth dynamics, due to the often cited lack of morphological information. To this end, an innovative low-cost light-field camera, the Lytro LF (Light-Field), was evaluated in a long-term field experiment. The experiment showed that the camera is suitable for monitoring plant growth dynamics and plant traits while being immune to ambient conditions. This represents a decisive contribution for a variety of monitoring and modeling applications, as well as for the validation of remote sensing data. This strongly confirms and endorses the assumption that the light-field camera presented in this study has the potential to be a light-weight and easy to use measurement tool for on-site environmental monitoring and remote sensing purposes.

Environmental monitoring programs carried out by expeditions or autonomous stations are expensive and only allow measurements for discrete times and locations. After data acquisition most of the data needs hand-operated validation and evaluation before being stored in databases.For a higher local and temporal resolution on parameters of marine ecosystems, it is planned to extend monitoring programs by attaching a small-sized module, which combines a microcontroller with multiple sensors, to ships of opportunity or any other suitable platform. The modules design focuses on the usability, reliability and interoperability of the derived data by using metadata information and assessing in-situ which data is relevant to be measured and stored.Using an ESP32, a popular microcontroller, to collect data from OEM sensors of different manufacturers enables a high flexibility in parameters and sensor types. The use of different OEM sensors also allows to experiment with unconventional hydrological sensors. The proposed open source module attempts to collect data as reliable as with conventional monitoring sensor systems.This approach allows an event based data acquisition, e.g. by adjusting the sampling rate so that only as much data as necessary is measured. In order to provide precise spatio-temporal referencing, the system contains a real time clock and GPS positioning. Moreover, storing the raw data of the sensors alongside their calibration coefficients enables post-processing of the data. The ESP32 transmits the stored data to a server via WiFi or an external LTE module. From this point on, a machine-based validation, flagging of relevant data and basic visualization can assist the evaluation.With such a module integrating multiple sensors and focusing on the interpretation and use of data starting at the measurement, reliable and pre-evaluated data from hard to access areas can be obtained and contribute to the assessment of dynamic and heterogeneous ecosystems.

Urban public spaces are highly dynamic systems where traffic patterns, pedestrian flows, and human activities vary strongly across temporal scales. Capturing these dynamics at high temporal resolution remains challenging, particularly using low-cost and reproducible observation methods. In this study, we present an automated workflow for continuous urban activity monitoring based on publicly available webcam imagery and deep learning–based object detection.A public webcam overlooking Augustusplatz, a central urban square in Leipzig (Germany), is continuously accessed, and still frames are extracted from the video stream at one-minute intervals. Each frame is processed using the YOLO11 object detection model to identify and count relevant object classes, including passenger vehicles and pedestrians. The detection results are converted into structured JSON records and enriched with metadata such as timestamp and geographic location. All data are stored in an InfluxDB time-series database and visualized and statistically analyzed using Grafana.This setup enables near-real-time and long-term analysis of urban activity patterns across multiple temporal scales. Distinct signatures of recurring and episodic events can be identified, including daily commuting cycles, evening rush hours, road closures, public celebrations, and large seasonal events such as Christmas markets. The minute-scale resolution allows for detailed investigation of short-term dynamics, while continuous operation over longer periods enables comparative and trend analyses.The presented approach demonstrates how publicly available visual data and open-source tools can be combined into a scalable and transferable framework for urban monitoring. Potential applications include event detection, urban mobility analysis, validation of traffic models, assessment of public space usage, and integration with other environmental or socio-economic datasets. The method provides a cost-efficient complement to traditional urban sensing infrastructures and offers new opportunities for data-driven urban and environmental research.