Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
A. Markos, N. Sims, G. Giuliani
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引用次数: 5

Abstract

ABSTRACT Monitoring changes in Annual Net Primary Productivity (ANPP) is required for reporting on UN Sustainable Development Goal (SDG) Indicator 15.3.1: the proportion of land that is degraded over the total land area. Calibrating time-series observations of ANPP to derive Water Use Efficiency (WUE; a measure of ANPP per unit of evapotranspiration) can minimize the influence of climate factors on ANPP observations and highlight the influence of non-climatic drivers of degradation such as land use changes. Comparing the ANPP and WUE time series may be useful for identifying the primary drivers of land degradation, which could be used to support the Land Degradation Neutrality objectives of the UN Convention to Combat Desertification (UNCCD). This paper presents an algorithm for the Google Earth Engine (freely and openly available upon request – http://doi.org/10.5281/zenodo.4429773) to calculate and compare ANPP and WUE time series for Santa Cruz, Bolivia, which has recently experienced an intensification in its land use. This code builds on the Good Practice Guidance document (version 1) for monitoring SDG Indicator 15.3.1. We use the MODIS 16-day average, 250 m resolution to demonstrate that the Enhanced Vegetation Index (EVI) responds faster to changes in water availability than the Normalized Difference Vegetation Index (NDVI). We also consider the relationships between ANPP and WUE. Significant and concordant trends may highlight good agricultural practices or increased resilience in ecosystem structure and productivity when they are positive or reducing resilience and functional integrity if negative. The sign and significance of the correlation between ANPP and WUE may also diverge over time. With further analysis, it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.
超越可持续发展目标15.3.1良好实践指南1.0,使用谷歌地球引擎平台:开发一种自我调整算法,以检测水利用效率和净初级产量的显著变化
监测年度净初级生产力(ANPP)的变化是报告联合国可持续发展目标(SDG)指标15.3.1(退化土地占总土地面积的比例)的必要条件。用水效率(WUE)对ANPP时序观测数据的校正(单位蒸散发的ANPP测量)可以最大限度地减少气候因子对ANPP观测的影响,并突出土地利用变化等退化的非气候驱动因素的影响。比较ANPP和WUE时间序列可能有助于确定土地退化的主要驱动因素,这可用于支持《联合国防治荒漠化公约》(UNCCD)的土地退化中性目标。本文介绍了谷歌地球引擎的一种算法(应要求免费和公开提供- http://doi.org/10.5281/zenodo.4429773),用于计算和比较玻利维亚圣克鲁斯的ANPP和WUE时间序列,该地区最近经历了土地利用的加剧。本代码以监测可持续发展目标指标15.3.1的良好做法指导文件(第1版)为基础。我们使用MODIS的16天平均值,250 m分辨率来证明增强植被指数(EVI)比归一化植被指数(NDVI)对水分有效性变化的响应更快。我们还考虑了ANPP和WUE之间的关系。显著和一致的趋势可能会突出良好农业做法或生态系统结构和生产力的复原力增强,如果它们是积极的,则会降低复原力和功能完整性。ANPP与WUE之间相关性的符号和意义也可能随时间而分化。通过进一步的分析,有可能从植物生产力和生态系统恢复力变化的驱动因素方面解释这种关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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