Land degradation assessment in the Argentinean Puna: Comparing expert knowledge with satellite-derived information

García, César Luis; Teich, Ingrid; González‐Roglich, Mariano; Kindgard, Adolfo Federico; Ravelo, Andrés C.; Liniger, Hanspeter

doi:10.1016/j.envsci.2018.10.018

Cited by 24 publications

(25 citation statements)

References 27 publications

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“…The medium spatial resolution of Landsat (30 m) and the significant amount of cloud-free data make this dataset particularly useful to study the high Andean peatlands, especially considering that most peatlands are small (9 ha on average) and elongated (Figure 9 and Appendix A). For this reason, most remote sensing studies on high Andean peatlands have used Landsat data (i.e., [18,20,21,24]). Coarser data, such as the 250 m MODIS vegetation indices product, has been widely used for peatland mapping in the Northern Hemisphere where peatlands are larger in size [4].…”

Section: Discussionmentioning

confidence: 99%

“…This is especially true in semiarid regions such as the Central Andes Altiplano, where peatlands play important hydrological and ecosystemic roles [15,16] and are highly vulnerable to the projected changes in the regional hydroclimate [17]. The demonstrated capability to map these peatlands in detail [18], and to use satellite-based vegetation indices as indicators of their primary productivity [19][20][21], allows us to develop regional-scale studies of the Altiplano peatlands utilizing a fine-scale spatiotemporal remote sensing approach.…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies at different spatial and temporal scales have used satellite-derived normalized difference vegetation index (NDVI) data, along with other vegetation indices, to study different aspects of the high Andean peatlands. These studies include, but are not limited to, evaluations of spatiotemporal changes in primary productivity [19][20][21][22], classifications of migratory and endemic bird habitats [23], and analyses of spatiotemporal eco-hydrological patterns [19,24,25]. For instance, Moreau et al [19] assessed the temporal biomass dynamics of the Andean peatlands using NOAA/AVHRR NDVI data and found a close relationship between NDVI and humid (green) total peatland biomass measured at four Northern Bolivian Altiplano sampling sites.…”

Section: Introductionmentioning

confidence: 99%

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A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery

et al. 2019

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The high Andean peatlands, locally known as “bofedales”, are a unique type of wetland distributed across the high-elevation South American Altiplano plateau. This extensive peatland network stores significant amounts of carbon, regulates local and regional hydrological cycles, supports habitats for a variety of plant and animal species, and has provided critical water and forage resources for the livestock of the indigenous Aymara communities for thousands of years. Nevertheless, little is known about the productivity dynamics of the high Andean peatlands, particularly in the drier western Altiplano region bordering the Atacama desert. Here, we provide the first digital peatland inventory and multiscale productivity assessment for the entire western Altiplano (63,705 km2) using 31 years of Landsat data (about 9000 scenes) and a non-parametric approach for estimating phenological metrics. We identified 5665 peatland units, covering an area of 510 km2, and evaluated the spatiotemporal productivity patterns at the regional, peatland polygon, and individual pixel scales. The regional assessment shows that the peatland areas and peatlands with higher productivity are concentrated towards the northern part of our study region, which is consistent with the Altiplano north–south aridity gradient. Regional patterns further reveal that the last seven years (2011–2017) have been the most productive period over the past three decades. While individual pixels show contrasting patterns of reductions and gains in local productivity during the most recent time period, most of the study area has experienced increases in annual productivity, supporting the regional results. Our novel database can be used not only to explore future research questions related to the social, biological, and hydrological influences on peatland productivity patterns, but also to provide technical support for the sustainable development of livestock practices and conservation and water management policy in the Altiplano region.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery

et al. 2019

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“…Many studies employ complex assessments of land use and land cover change (LUCC) and its impacts on water, biodiversity, land processes, or climate due to the important role of LUCC on socio-ecologic and socio-economic systems and associated tradeoffs with sustainability, food security, biodiversity, and human and environmental vulnerability to global change [3,14]. Vegetation productivity indices, such as the Normalized Difference Vegetation Index (NDVI), remain some of the most commonly employed remote sensing proxies that pertain to ecosystem health and land productivity in HEI studies [15,16].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions

2019

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The role of remote sensing and human–environment interactions (HEI) research in social and environmental decision-making has steadily increased along with numerous technological and methodological advances in the global environmental change field. Given the growing inter- and trans-disciplinary nature of studies focused on understanding the human dimensions of global change (HDGC), the need for a synchronization of agendas is evident. We conduct a bibliometric assessment and review of the last two decades of peer-reviewed literature to ascertain what the trends and current directions of integrating remote sensing into HEI research have been and discuss emerging themes, challenges, and opportunities. Despite advances in applying remote sensing to understanding ever more complex HEI fields such as land use/land cover change and landscape degradation, agricultural dynamics, urban geography and ecology, natural hazards, water resources, epidemiology, or paleo HEIs, challenges remain in acquiring and leveraging accurately georeferenced social data and establishing transferable protocols for data integration. However, recent advances in micro-satellite, unmanned aerial systems (UASs), and sensor technology are opening new avenues of integration of remotely sensed data into HEI research at scales relevant for decision-making purposes that simultaneously catalyze developments in HDGC research. Emerging or underutilized methodologies and technologies such as thermal sensing, digital soil mapping, citizen science, UASs, cloud computing, mobile mapping, or the use of “humans as sensors” will continue to enhance the relevance of HEI research in achieving sustainable development goals and driving the science of HDGC further.

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“…Since just three indicators cannot fully capture the complexity of land degradation (i.e., its degree and drivers), countries are strongly encouraged to use other relevant national or sub-national indicators, data, and information to strengthen their interpretation, as well as participatory processes to validate results based on EO data. Integration of EO data into participatory processes that include local knowledge is crucial in the process of LDN [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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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Land degradation assessment in the Argentinean Puna: Comparing expert knowledge with satellite-derived information

Cited by 24 publications

References 27 publications

A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery

A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery

Remote Sensing of Human–Environment Interactions in Global Change Research: A Review of Advances, Challenges and Future Directions

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

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