What is remote sensing I How is remote sensing used to contribute to a sustainable bioeconomy? I What are the limitations? I Case studies
What is remote sensing?
Remote sensing is the science, technology and art of observing an object, scene, or phenomenon by instrument-based technique at a distance without physical contact [1]. In other words, remote sensing allows the collection of information about an object without making a physical contact with it [2]. In a similar way that humans use their vision to gather certain information about an object (e.g. its colour, shape or size) without touching it, the remote sensing cameras and scanners are used as sensors to obtain data about the objects on the earth’s surface (e.g., forests, crop fields, water bodies, infrastructure objects) without physical contact to them. Satellites, aeroplanes and drones are used as common platforms to carry those sensors. The remote sensors’ output is usually in a form of image or photograph, which can be further processed to extract information about the observed objects.
Remote sensing camera captures reflected or emitted electromagnetic energy from the observed object. According to objects' biochemical composition and surface characteristics, the amount of energy reflected by objects is different. For example, healthy green leaves reflect green light (that is why we see leaves as green), but most of the red and blue light is absorbed for photosynthesis. Therefore, the acquired remote sensing images can be used to differentiate objects (e.g., water, trees, buildings) and quantify the object's bio-physical parameters (e.g., biomass). Supervised and unsupervised modellings (both classification and regression) are employed to retrieve that information from remote sensing images. Machine and deep learning modelling techniques are usually utilised with remote sensing images for accurate information extraction. The retrieved information (e.g., land-use land-cover, amount of biomass) can be further employed for spatial analysis using geographic information system (GIS) to assess the relationship with other climatic (e.g., temperature, precipitation), socioeconomic (e.g., population, income) and ecologic (e.g., species abundance) parameters.
Initially developed for the military purposes, remote sensing today is widely used also for civil applications. A range of environmental variables can be measured with remote sensing techniques in the atmosphere, over the land and the water. Therefore, remote sensing is applied in a large number of disciplines, including, but not limited to agriculture and crop monitoring, archaeology, cartography, civil engineering, climatology, disaster monitoring and prediction, forestry, geology, meteorology, oceanography, pollution monitoring, soil characterisation, urban mapping, and water resource mapping and monitoring [2].
How is remote sensing used to contribute to a sustainable bioeconomy?
Considering its wide range of uses, remote sensing can be well applied to assist the transition to sustainable bioeconomy, and to monitor its impacts and results. For example, remote sensing is used to capture:
- Land cover and land use at different spatial scales and resolutions enabling representations on global, regional, national and/or local levels;
- Land cover and land use change to observe its temporal dynamics;
- Vegetation (e.g. of croplands, grasslands [3], forests, peatlands), including characterising the type of vegetation, yields and its state of health;
- Environmental impacts, for example, monitoring of illegal logging, forest fires, impacts of floods, erosion and droughts;
- State of biodiversity and other ecosystem services.
Therefore, remotely sensed data can be used, for example, to estimate biomass potential, for calculation of carbon sinks of forest biomass [4], for monitoring of land use and land use change impacts of bioeconomy activities (e.g. land use change associated to biofuel production, extent of deforestation, etc.). Furthermore, remote sensing provides important data for precision agriculture applications and thus may contribute to sustainable intensification of agriculture. To ensure the sustainability of bioeconomy activities, remote sensing is increasingly used for monitoring environmental impacts, for example, changes in biodiversity [5] [6] [7], state of protected areas [8] and pollution [9]. Some case studies of using remote sensing for bioeconomy monitoring can be found below.
What are the limitations?
Although remote sensing technologies have been continuously developing over the last 50 years, there are still several challenges to cope with. For example, technical limitations in terms of resolution, coverage and cloud cover – in particular in case of the satellite based remote sensing [10]. Obtaining the higher resolution images imposes additional costs as high resolution satellite images are mostly available at extra charge, or specialised equipment (drones, cameras, sensors, computing power) has to be purchased to gather high-resolution information on the local scale.
Further challenges are concerning validation and transferability of remote sensing data models. Validation requires field data. However, it is often complicated, expensive or even impossible to obtain the necessary information on the ground. For example, when validating data models based on satellite images from the past decades. Transferability of the models developed and validated in a particular location to other locations and scales is another significant challenge. The transferability challenge will be addressed by one of the case studies below, where a land use change monitoring model developed for North Hesse will be applied to the larger Weser-Ems region in Germany.
Case studies
Germany: Maize for biogas in North Hesse
Further case studies will be available in 2023/2024 and will demonstrate the use of remote sensing for monitoring the impacts on land use change in the Weser-Ems region in Germany
Brazil, Indonesia, Malaysia: Remote sensing for monitoring of the bioeconomy
Efficient and accurate monitoring of the bioeconomy must be extended to the beginning of the supply chain, including biomass production at the farm, plantation, and forestry levels [11]. This is where land use change and deforestation occur, and on-site monitoring via remote sensing can help to ensure that biomass production does not conflict locally with no-deforestation objectives and the protection of high carbon stock areas.
In a case study, remote sensing technology was implemented to identify how satellite-based land use change detection contributes to bioeconomy monitoring. As land use change caused by an extension of agricultural areas is the main driver for carbon stock and biodiversity losses [12], the use of satellite images are an indispensable element of bioeconomy monitoring. For this, relevant datasets and satellite images were gathered and a methodology to detect land use change and varying land management intensities based on Landsat images was developed, focusing on four pilot regions in Brazil, Indonesia, and Germany in the timeframe 2007 – 2016.
The results in the pilot area in Brazil showed that a total of 2,110,607 hectares of forest were converted to annual cropland and managed pasture between 2008 and 2017. The highest deforestation rate occurred in 2012, as shown in the figure below:
Deforestation in the pilot region in Brazil per year (Source: Based on data from the SYMOBIO project (ongoing work by GRAS) and Helka et al. (2020) [11]).
Likewise, in the pilot area in Kalimantan the forest that was converted to plantation was assessed annually between 2007 and 2017. A total of 7,199,459 ha of tree cover loss has been detected, including deforestation and replanting activities. Replanting activities of palm, rubber, etc. plantations have been detected and separated from the deforestation, resulting in 6,451,363 ha of actual deforestation.
This result shows how remote sensing can support the distinction of different land use change, and how accounting for replanting is important for proper deforestation reporting. The study also found that deforestation assessment can be conducted annually for state, municipality, or any other administrative level suitable for bioeconomy monitoring (see the figure below).
Selected states of Brazil showing the deforestation over time (Source: Based on data from the SYMOBIO project (ongoing work by GRAS) and Helka et al. (2020) [11]).
Based on these findings, GRAS is now developing an integrated, semi-automated system based on remote sensing to monitor the expansion of cropland and pasture into forests and natural grasslands at the country and regional levels.
Notes and references
- Tempfli et al. (2009). Principles of remote sensing : an introductory textbook. Available at: http://www.itc.nl/library/papers_2009/general/PrinciplesRemoteSensing.pdf
- Rees (2012). Physical Principles of Remote Sensing (3rd). doi: 10.1017/CBO9781139017411
- Wachendorf et al. (2018). Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands. Grass Forage Sci. doi: 10.1111/gfs.12312
- Abbas et al. (2020). Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales. Remote Sens. doi: 10.3390/rs12203351
- Wang et al. (2019). Remote sensing of terrestrial plant biodiversity. Remote Sens. doi: 10.1016/j.rse.2019.111218
- Randin et al. (2020). Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens.. doi: 10.1016/j.rse.2019.111626.
- Reddy (2021). Remote sensing of biodiversity: what to measure and monitor from space to species? Biodivers Conserv. doi: 10.1007/s10531-021-02216-5
- Mao et al. (2020). Remote Sensing Applications for Monitoring Terrestrial Protected Areas: Progress in the Last Decade. doi: 10.3390/su12125016
- Stebel et al. (2021). SAMIRA-Satellite Based Monitoring Initiative for Regional Air Quality. Remote Sens. doi: 10.3390/rs13112219
- Dubovik et al. (2021). Grand Challenges in Satellite Remote Sensing. Remote Sens. doi: 10.3389/frsen.2021.619818
- Helka et al. (2020). Combining Environmental Footprint Models, Remote Sensing Data, and Certification Data towards an Integrated Sustainability Risk Analysis for Certification in the Case of Palm Oil. Sustainability . doi: 10.3390/su12198273
- the IPBES website for data on biodiversity loss and measures to halt it, available at: https://ipbes.net