Long-Term Monitoring of Desert Land and Natural Resources and Application of Remote Sensing Technologies

The practice of coppicing is debated in the literature for the risk factors associated with soil erosion. Although erosion experiments provide useful data for estimating the susceptibility to soil erosion, there are many open questions that cannot be solved in isolated experiments, but which can be assessed by activating a long-term monitoring process. In this way, it is possible to correctly frame the spatial and temporal scale of soil erosion in coppice forests.&#160;The aim of the work is to evaluate the effectiveness of the use of remote sensing data in combination with field data, for monitoring the evolution of forest stands interested by coppicing in relation to soil erosion.&#160;We have installed a long-term monitoring network for erosion estimation, while Sentinel-2C satellite data were used for the period 2016-2018. Starting from this dataset, a selection of vegetation indices was calculated and compared to the morphological and topographical parameters of the study area, as well as the above-ground data collected during field activities. Using the Canonical Correspondences Analysis (CCA) the relationships between the matrix of vegetation indices, topographic and vegetational parameters and the respective performances of this protocol have been explored in order to describe the evolution of the forest stands in the study area associated to soil losses.

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Long-term monitoring of sea ice conditions in the Kerch Strait by remote sensing data

Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2017 ◽

10.1117/12.2277829 ◽

2017 ◽

Author(s):

Olga Y. Lavrova ◽

Marina I. Mityagina ◽

Andrey G. Kostianoy ◽

Tatiana Yu. Bocharova

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Remote Sensing Data ◽

Sensing Data ◽

Kerch Strait ◽

Ice Conditions ◽

Long Term Monitoring ◽

Term Monitoring

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One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

Remote Sensing ◽

10.3390/rs13101892 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1892

Author(s):

Sébastien Rapinel ◽

Laurence Hubert-Moy

Keyword(s):

Remote Sensing ◽

Natural Vegetation ◽

Open Source Data ◽

Source Data ◽

Good Classification ◽

Long Term Monitoring ◽

One Class Classification ◽

Term Monitoring

Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.

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