Integrated Analyses of PALSAR and Landsat Imagery Reveal More Agroforests in a Typical Agricultural Production Region, North China Plain

Abstract

Abstract:As the largest among terrestrial ecosystems, forests are vital to maintaining ecosystem services and regulating regional climate. The area and spatial distribution of trees in densely forested areas have been focused on in the past few decades, while sparse forests in agricultural zones, so-called agroforests or trees outside forests (TOF), have usually been ignored or missed in existing forest mapping efforts, despite their important role in regulating agricultural ecosystems. We combined Landsat and PALSAR data to map forests in a typical agricultural zone in the North China Plain. The resultant map, based on PALSAR and Landsat (PL) data, was also compared with five existing medium resolution (30-100 m) forest maps from PALSAR (JAXA forest map) and Landsat: NLCD-China, GlobeLand30, ChinaCover, and FROM-GLC. The results show that the PL-based forest map has the highest accuracy (overall accuracy of 95 ± 1% with a 95% confidence interval, and Kappa coefficient of 0.86) compared to those forest maps based on single Landsat or PALSAR data in the North China Plain (overall accuracy ranging from 85 ± 2% to 92 ± 1%). All forest maps revealed higher accuracy in densely forested mountainous areas, while the PL-based and JAXA forest maps showed higher accuracy in the plain, as the higher omission errors existed in only the Landsat-based forest maps. Moreover, we found that the PL-based forest map can capture more patched forest information in low forest density areas. This means that the radar data have advantages in capturing forests in the typical agricultural zones, which tend to be missing in published Landsat-based only forest maps. Given the significance of agroforests in regulating ecosystem services of the agricultural ecosystem and improving carbon stock estimation, this study implies that the integration of PALSAR and Landsat data can provide promising agroforest estimates in future forest inventory efforts, targeting a comprehensive understanding of ecosystem services of agroforests and a more accurate carbon budget inventory.

Tracking Reforestation in the Loess Plateau, China after the “Grain for Green” Project through Integrating PALSAR and Landsat Imagery

“…A detailed workflow was developed to map the forest and multi-source forest dataset comparison in Figure 2. We used the L-band PALSAR data which can penetrate into forests with substantial volume scattering through the incident energy interaction with trunks and branch According to the United Nations Food and Agriculture Organization (UN FAO), the forest definition includes both forest structure and canopy information using a unit of land (>0.5 ha) with tree cover > 10% and the minimum height > 5 m. The previous studies found that the threshold-based approach for forest mapping is robust and extendable in different regions, such as the monsoon region in Asia, Oklahoma in USA [27,28], and the North China Plain, in China [29].…”

“…Qin et al [28] utilized the PALSAR data with 25 m spatial resolution and optical remote sensing data (Landsat) with 30 m spatial resolution to extract the forest information of Sub-Humid and Semi-Arid Regions from Oklahoma, in 2010, which has the highest overall accuracy compared with other forest products, with an overall accuracy of 88.2%. Yang et al [29] utilized the same method to produce a forest product with 30 m spatial resolution to analyze the agroforests in North China Plain. Considering the above successful cases and complementary advantages between optical and SAR remote sensing data, the integration of SAR and optical remote sensing images may enable improvement of forest mapping [30].…”

Integrating Participatory Methods and Remote Sensing to Enhance Understanding of Ecosystem Service Dynamics Across Scales

“…The novel approach we have developed and tested here for integrating methods to study the dynamics of ecosystem services ensured that local context was central to the process, addressing the limitations of many RS-GIS mapping studies [20][21][22]. Furthermore, our findings are not limited to the local scale and can be scaled up to the landscape level.…”

“…For these proxy indicators to be locally relevant, stakeholder input is necessary to ensure indicators accurately address local context, which can be challenging when the RS-GIS data for mapping ES rarely exists in the form that is easily understood by the average ES user. Finally, whilst RS-GIS may detect changes in ES, 'ground truth' information is often required to understand causation which requires engaging with local stakeholders [20] and is missing in many RS-GIS mapping studies [21,22]. In this sense, mapping of ES only fulfills the objective of testing the scientific hypothesis, and ES beneficiaries are often not deeply considered.…”

Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification

“…Google Earth imagery provides no-cost imagery with ultra-high definition in the red, green, and blue spectral bands, which could provide crucial information that could be used to identify and extract the shapes of surface objects. Radar data is believed to have potential usefulness in LULC classification, because of its ability to penetrate trees, shrubs, and herbaceous vegetation [55,56]. Therefore, multivariate remote sensing data could be used to improve the accuracy of training samples and thus the accuracy of classification results.…”