Developing good practice guidance for estimating land degradation in the context of the United Nations Sustainable Development Goals

Sims, Neil; England, Jacqueline R.; Newnham, Glenn; Alexander, Sasha; Green, Carly; Minelli, Sara; Held, Alex

doi:10.1016/j.envsci.2018.10.014

Cited by 107 publications

(107 citation statements)

References 7 publications

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“…UNCCD is the custodian agency for SDG indicator 15.3.1 to monitor progress towards achieving SDG target 15.3. UNCCD through its national reporting and review process should regularly collect, on a four-year basis, and analyze data on the proportion of land that is degraded over the total land area (Sims et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…detection of changes in primary productivity as compared to a baseline period) and performance (i.e. local productivity relative to other areas that share a similar landcover type over the dedicated region) (Sims et al, 2019;UNCCD, 2017). The three measures are then aggregated resulting in a 3-class (improvement; stable; degradation) land productivity subindicator following UNCCD guidelines for reporting (UNCCD, 2017).…”

Section: Methodsmentioning

confidence: 99%

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Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube

et al. 2020

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Avoiding, reducing, and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth. To halt and reverse the current trends in land degradation, there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands, as required by the Sustainable Development Goals (SDGs), in particular, the SDG indicator 15.3.1 ("proportion of land that is degraded over total land area"). Earth Observations (EO) can play an important role both for generating this indicator as well as complementing or enhancing national official data sources. Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR. Consequently, there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data (e.g. Landsat or Sentinels). In response to this issue, this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube

et al. 2020

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“…Long-Term Trend (LTT): This method is based on the trajectory indicator as described by the UNCCD GPG [9]. The trend is obtained by computing a Mann-Kendall non-parametric significance test [29,30] and considering only as significant changes those that show a p-value ≤ 0.05.…”

Section: Trends In Land Productivitymentioning

confidence: 99%

“…NPP is usually estimated by means of EO from known correlations between the fraction of absorbed photosynthetically active radiation (fAPAR) and plant growth vigor and biomass [8]. Various vegetation indices can be calculated from satellite data to be used as a proxy for land productivity [9], with the Normalized Difference Vegetation Index (NDVI) being the most frequently used [10][11][12]. There are also currently numerous methods that can be used to analyze and extract insights from time series image datasets [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…There are also currently numerous methods that can be used to analyze and extract insights from time series image datasets [13][14][15][16][17][18][19]. For example, the UNCCD Good Practice Guidance (GPG) [9] suggests three metrics derived from the remotely sensed data to estimate changes in land productivity (trend, state, and performance), and tools, such as Trends.Earth, have been developed to support the calculation of such metrics globally [20]. Additionally, many annual metrics linked to key phenological events (i.e., onset of greenness, time of peak NDVI, maximum NDVI, rate of green-up, rate of senescence and integrated NDVI) can be derived from the NDVI time series.…”

Section: Introductionmentioning

confidence: 99%

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Combining Earth Observations, Cloud Computing, and Expert Knowledge to Inform National Level Degradation Assessments in Support of the 2030 Development Agenda

et al. 2019

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Monitoring progress towards the 2030 Development Agenda requires the combination of traditional and new data sources in innovative workflows to maximize the generation of relevant information. We present the results of a participatory and data-driven land degradation assessment process at a national scale, which includes use of earth observation (EO) data, cloud computing, and expert knowledge for Argentina. Six different primary productivity trend maps were produced from a time series of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset (2000–2018), including the most widely used trajectory approach and five alternative methods, which include information on the timing and magnitude of the changes. To identify the land productivity trend map which best represented ground conditions, an online application was developed, allowing 190 experts to choose the most representative result for their region of expertise nationwide. Additionally, the ability to detect decreases in land productivity of each method was assessed in 43,614 plots where deforestation had been recorded. The widely used trajectory indicator was the one selected by most experts as better reflecting changes in land condition. When comparing indicators’ performance to identify deforestation-driven reductions in productivity, the Step-Wise Approach Trend Index (SWATI), which integrates short- and long-term trends, was the one which performed the best. On average, decreases of land productivity indicate that 20% of the Argentine territory has experienced degradation processes between 2000 and 2018. The participatory data generation and verification workflow developed and tested here represents an innovative low cost, simple, and fast way to validate maps of vegetation trends and other EO-derived indicators, supporting the monitoring of progress towards land degradation neutrality by 2030.

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Identifying management‐driven dynamics in vegetation cover: Applying the Compere framework to Cooper Creek, Australia

et al. 2022

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The observed spatial and temporal dynamics in landscape and ecosystem resources are the net effects of natural processes and management activities. Monitoring the impact that humans have on these resources requires that these two sources of variability be partitioned, removing the natural variability to reveal variability due to management activities. Here, we present Compere, a relative benchmarking framework for monitoring the management‐driven impacts on ecosystem resources. The framework identifies locations in a region that share similar biophysical properties to a target location. Taking an attribute of interest, it then compares the value of the target location with those of all its biophysically equivalent locations, with any differences being attributed to variations in management. We provide an example application of Compere, using satellite‐derived vegetation cover data to examine the impact of hydrocarbon extraction activities on land condition (as described by vegetation cover) across the 490,000‐km2 Cooper Creek region in arid central Australia. Validation was performed by comparing land condition estimates to known disturbance patterns across the region. We found that the establishment of well sites was associated with between 12% and 41% decreases in land condition but that condition recovered to original levels by 6 years after establishment. We also found that fires had much larger and longer lasting effects on land condition across these landscapes, indicating that mining‐related activities that change fire patterns are likely to affect land condition most. Compere is a generic, flexible framework, providing a new capability for monitoring management impacts on multiple types of ecosystem resources.

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Developing good practice guidance for estimating land degradation in the context of the United Nations Sustainable Development Goals

Cited by 107 publications

References 7 publications

Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube

Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube

Combining Earth Observations, Cloud Computing, and Expert Knowledge to Inform National Level Degradation Assessments in Support of the 2030 Development Agenda

Identifying management‐driven dynamics in vegetation cover: Applying the Compere framework to Cooper Creek, Australia

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