Bridging the Gap Between Remote Sensing and Plant Phenotyping—Challenges and Opportunities for the Next Generation of Sustainable Agriculture

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

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Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

Applied Sciences ◽

10.3390/app11135911 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5911

Author(s):

Vanesa Martos ◽

Ali Ahmad ◽

Pedro Cartujo ◽

Javier Ordoñez

Keyword(s):

Remote Sensing ◽

Sustainable Agriculture ◽

Information Acquisition ◽

Industrial Revolution ◽

Structural Parameters ◽

Special Focus ◽

World Population ◽

Food And Agriculture ◽

Food And Agriculture Organization ◽

Zero Hunger

Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

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