Estimating global ecosystem service values and its response to land surface dynamics during 1995–2015

Sannigrahi, Srikanta; Bhatt, Sandeep; Rahmat, Shahid; Paul, Saikat Kumar; Sen, Shashwati

doi:10.1016/j.jenvman.2018.05.091

Cited by 118 publications

(56 citation statements)

References 47 publications

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“…Land Use and Land Cover (LULC) data are important inputs for countries to monitor how their soil and land use are being modified over time [1,2]. It is also possible to identify the impacts of increasing urban environments in different ecosystems [3,4,5,6], monitoring protected areas, and the expansion of deforested areas in tropical forests [1,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…For the latter purpose, the LULC mapping has been used to enhance the results found for Costanza et al [15] that provided global ES values. In that research, the values have been rectified since its first publication [16,17]; the LULC approach provides land classes which allow to estimate ES by unit area, making it possible to extrapolate ES estimates and values for greater areas and biomes around the world by using the benefit transfer method [1,17,18]. Nonetheless, Song [19] highlights the limitation of these values estimations, since LULC satellite-based products have uncertainties related to the data.…”

Section: Introductionmentioning

confidence: 99%

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Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Tavares

Beltrão

Guimarães

et al. 2019

Sensors

132

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In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Tavares

Beltrão

Guimarães

et al. 2019

Sensors

132

View full text Add to dashboard Cite

show abstract

“…A new global, spatially explicit ES assessment was required for this study because previous studies have not released the supporting spatial data, calculations, and resulting maps (e.g. Costanza et al 1997, Ghermandi and Nunes, 2013, Costanza et al 2014, Li and Fang 2014, Kubiszewski et al 2017, Sannigrahi et al 2018, Song 2018 The global ES assessment workflow consists of the following steps: (1) inventory the ecosystem subservices and services in ES assessments; (2) homogenize the inventory; (3) reduce the inventory to those ecosystem subservices that are mappable and calculable; (4) preprocess the data; (5) apply spatial disaggregation; and (6) compute total ES values. Table 1 provides detailed information for the ES assessment, including the category, service, and subservices used.…”

Section: Global Es Assessmentmentioning

confidence: 99%

“…Since the review in 2011, six other studies mapped ES values globally (Ghermandi and Nunes 2013, Costanza et al 2014, Li and Fang, 2014, Kubiszewski et al 2017, Sannigrahi et al 2018, Song 2018, but the outputs were not published preventing incorporation in future or other analyses by the broader scientific community.…”

Section: Introductionmentioning

confidence: 99%

Global ecosystem service values in climate class transitions

Watson

Wanders

et al. 2020

Environ. Res. Lett.

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Ecosystem service assessments facilitate the valuation of nature and support decision-making. Ecosystem services are connected to climate; however, ecosystem service values affected by climate change remain unclear. We mapped global ecosystem service values totaling ∼1.3 trillion international dollars for 2005. Transitions in Köppen-Geiger climate classes projected with General Circulation Models under the four IPCC Representative Concentration Pathways (RCP) were modeled providing 20 climate scenarios. The mapped global ecosystem service values were combined with the 20 climate scenarios in order to identify where and how much of the global ecosystem service value is within a climate class transition. By 2050, 252-375 billion international dollars of ecosystem service value (20%-30% of total value) are in a Köppen-Geiger climate transition for both RCP 2.6 and 8.5 scenarios. In RCP 2.6, the 2015 Paris Agreement carbon emission scenario target, Köppen-Geiger climate transitions stabilize after 2050. However, in the RCP 8.5 scenario, ecosystem service values amounting to 467-632 billion international dollars (37%-50% of total value) are in a Köppen-Geiger climate transition by 2085. These results provide an inclusive global overview of climate change impact on evaluated ecosystem services that affect populations and economies.

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“…To date, with some exceptions (Chen & Zhang, 2000;Costanza et al, 1997;de Groot et al, 2012;Deloitte Access Economics, 2017;Kubiszewski, Costanza, Anderson, & Sutton, 2017;Sannigrahi, Bhatt, Rahmat, Paul, & Sen, 2018), most ESV focusses on marginal changes in the value of a narrow suite of ES flows, commonly from a single, locally-important habitat (Baskara, Hendarto, & Susilowati, 2017;Matthew et al, 2018;Pascal et al, 2016). In application, these values are typically used in social benefit cost analysis of specific proposals (Georgiou et al, 1997;van der Ploeg & de Groot, 2010).…”

Section: Introductionmentioning

confidence: 99%

Challenges and Sensitivities in Assessing Total Ecosystem Service Values: Lessons From Vanuatu for the Pacific

Buckwell

Fleming

Smart

et al. 2020

The Journal of Environment & Development

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Ecosystem service valuation (ESV) can inform land-use change policy and adaptation responses to climate change in Pacific Small Island Developing States. Despite Small Island Developing States communities relying acutely and directly on ecosystem service (ES) flows, methodologies must contend with limited valuation data and challenges. We undertake ESV to generate coefficients we then apply to mapped habitat extents for Vanuatu. We find the contribution of ESs to the people of Vanuatu is considerable and significantly larger than its gross domestic product. Therefore, policies that support ecologically sustainable exploitation of ESs are paramount in promoting community well-being. We also identify and discuss context-specific methodological challenges, which, if not addressed, risk distorting valuations, supporting perverse policy responses, and eroding confidence in ESV. We make recommendations to address the challenges of accounting for ecosystem condition, data gaps, and consideration of customary benefits, provide context to the interpretation of our results, and suggest where further research can ameliorate risks.

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Estimating global ecosystem service values and its response to land surface dynamics during 1995–2015

Cited by 118 publications

References 47 publications

Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Global ecosystem service values in climate class transitions

Challenges and Sensitivities in Assessing Total Ecosystem Service Values: Lessons From Vanuatu for the Pacific

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