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LPDynR: A new tool to calculate the land productivity dynamics indicator

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The United Nations Sustainable Development Goal 15 (Life on Land), adopted the indicator 15.3.1 to measure the Land Degradation Neutrality. This indicator is based on three sub-indicators: (1) Trends in Land Cover, (2) Land Productivity and (3) Carbon Stocks. The Land Productivity sub-indicator refers to the total above-ground Net Primary Production and reflects changes in health and productive capacity of the land. It can be calculated using the Land Productivity Dynamics approach, which performs a combined assessment of the long term tendency of change of land productivity and its current level relative to homogeneous land areas. Here, we present the R-based tool LPDynR, which implements the Land Productivity Dynamics approach for the calculation of the Land Productivity sub-indicator. LPDynR ingests vegetation-related indices derived from time series of remote sensed imagery. The final indicator is a 5-class map, ranging from declining to increasing land productivity. As an example of LPDynR functionalities and applicability, we present a case study for Europe. First, we show the general way to calculate the indicator for the entire time series (2000–2019), explained in a step by step process. Secondly, we show how to alternatively calculate the indicator based only on the long term tendency of change, but we evidence the added value of including the current level of productivity to refine the final indicator. Finally, we present some code for the calculation of “partial indicators” in terms of time scale along the observation period, which may help the user to understand the land productivity dynamics within the time series, as well as to assess the stability of the final product. While the indicator shows a general positive dynamics across Europe during the period 2000–2019, some of the partial maps show more negative trends, demonstrating the highly fluctuating character of vegetation.
2021-11-25
ELSEVIER
JRC124797
1470-160X (online),   
https://www.sciencedirect.com/science/article/pii/S1470160X21010517,    https://publications.jrc.ec.europa.eu/repository/handle/JRC124797,   
10.1016/j.ecolind.2021.108386 (online),   
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