Remote estimation of shelterbelt width from SPOT5 imagery

Deng, Rongxin; Li, Y.; Xu, Xiaowen; Wang, W. J.; Wei, Yichang

doi:10.1007/s10457-016-9915-1

Cited by 9 publications

(5 citation statements)

References 19 publications

(16 reference statements)

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“…To mitigate error sources, enhancing the quality of remote sensing data can prove effective in improving accuracy. When the crop harvesting period is at the end of September and the middle of October, notable spectral differences emerge in the spectrum of farmland shelterbelts [49]. Addressing the impact of temporal phases and leveraging it to estimate shelterbelt age requires in-depth investigation.…”

Section: Analysis Of Age Error Sourcesmentioning

confidence: 99%

Age Identification of Farmland Shelterbelt Using Growth Pattern Based on Landsat Time Series Images

Zhang,

Li,

et al. 2023

Remote Sensing

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Farmland shelterbelt, as a category of shelterbelt in forestry ecological engineering, has an important influence on agricultural sustainability in agricultural systems. Timely and accurate acquisition of farmland shelterbelt age is not only essential to understanding their shelter effects but also directly relates to the adjustment of subsequent shelterbelt projects. In this study, we developed an age identification method using growth pattern to extract the age of shelterbelt (i.e., years after planting) based on Landsat time series images. This method was applied to a typical area of shelterbelt construction in the north of Changchun, China. The results indicated that the accuracy of age identification reached a stable situation when the permissible age error exceeded 3 years, achieving an accuracy of approximately 90%. Moreover, the accuracy at different growth phases (1–3 years, 4–15 years, 16–30 years, and >30 years) decreased with increasing age, and the accuracy of each growth phase can reach more than 80% when the permissible age error is beyond 7 years. Compared to building the typically weak statistical relationship between the shelterbelt age and remote sensing characteristic information to derive age, this method presented a direct age identification method for fine-scale age extraction of the shelterbelt. It introduced a novel perspective for shelterbelt age identification and the assessment of shelterbelt project advancement on the regional scale.

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Section: Analysis Of Age Error Sourcesmentioning

confidence: 99%

Age Identification of Farmland Shelterbelt Using Growth Pattern Based on Landsat Time Series Images

Zhang,

Li,

et al. 2023

Remote Sensing

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“…The wind barriers' width is related to barrier porosity [161,165], but also has a specific effect on wind velocity [167]. Using 10 m SPOT5 images is applicable for windbreaks' width measurement; however, errors may arise when the windbreak is presented on both sides of the road, in the case of sparse or young windbreaks [168]. Aerial images were analyzed using object-oriented image analysis to map the windbreaks [169][170][171], but also for the automatic extraction of wind barriers' width [172].…”

Section: Living Wind Barriers Current State and Research Gapsmentioning

confidence: 99%

“…The disadvantage is that this dataset does not provide information about windbreak height or porosity and is available only for EEA contributing countries. Satellite images were tested for wind barriers' mapping [168], rough estimation of sheltered areas [147], and for estimation of the wind barriers width, height, and porosity [161] or gaps [184]. The 10 m resolution images are applicable, however better results would be obtained with more detailed images as was shown from aerial images [169][170][171].…”

Section: Future Directionsmentioning

confidence: 99%

Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions

et al. 2023

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Remote sensing (RS) has revolutionized field data collection processes and provided timely and spatially consistent acquisition of data on the terrestrial landscape properties. This research paper investigates the relationship between Wind Erosion (WE) and Remote Sensing (RS) techniques. By examining, analyzing, and reviewing recent studies utilizing RS, we underscore the importance of wind erosion research by exploring indicators that influence the detection, evaluation, and modeling of wind erosion. Furthermore, it identifies research gaps particularly in soil erodibility estimation, soil moisture monitoring, and surface roughness assessment using RS. Overall, this research enhances our understanding of WE and RS and offers insights into future research directions. To conduct this study, we employed a two-fold approach. First, we utilized a non-systematic review approach by accessing the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database. Subsequently, we conducted a systematic review of the relevant literature on wind erosion and remote sensing in the core collection of the Web of Science (WoS) database. Additionally, we employed the VOSviewer bibliometric software to generate a cooperative keyword network analysis, facilitating the advancements and identifying emerging areas of WE and RS research. With a non-systematic review, we focused on examining the current state and potential of remote sensing for mapping and analyzing following indicators of wind erosion modelling: (1) soil erodibility; (2) soil moisture; (3) surface roughness; (4) vegetation cover; (5) wind barriers; and (6) wind erosion mapping. Our study highlights the widespread utilization of freely available RS data, such as MODIS and Landsat, for WE modeling. However, we also acknowledge the limitations of high resolution sensors due to their high costs. RS techniques offer an efficient and cost-effective approach for mapping erosion at various scales and call for a more comprehensive and detailed assessment of soil erosion at regional scales. These findings provide valuable guidance for future research endeavors in this domain.

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“…The studies were mainly focused on their structure, function and management (Fan et al, 2002). As researches expanding from field scale to landscape scale, geographic information system (GIS) and remote sensing (RS) technologies were widely used in shelterbelt research (Wiseman et al, 2009;Czerepowicz et al, 2012;Deng et al, 2013Deng et al, , 2017Zheng et al, 2016). Using the principle of the landscape ecology and combining GIS and RS, landscape structure can be analyzed to accurately evaluate development and status at landscape scale.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Changes in Shelterbelt Landscape in Northeast China

Deng¹

2019

Appl. Ecol. Env. Res.

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Understanding the shelterbelt development in northeast China over the past 30 years will provide a scientific decision support for its management in future. In this paper, Landsat images were selected to extract shelterbelt information covering three points in time (1990, 2000 and 2010). Shelterbelt density and landscape indexes (ratio of belt to patch, connectivity, and dominance) were introduced to analyze changes in landscape. Farmland landscape indexes (ratio of farmland to zone and fragmentation) were used to analyze the effect of farmland on shelterbelt distribution and change. The result showed that: (1) shelterbelt density had considerable spatial variation. These changes were strong correlated with land-use type. Farmland fragmentation was a main index which significantly affected the connectivity and evenness of shelterbelt, especially on connectivity; (2) shelterbelt density increased about 10% over the 30 years. This modest increase included two contrasting trends, an increase by 13.2% from 1990 to 2000 followed by a 2.8% reduction by 2010. Even the quantity reduced over 2000-2010, the qualities including connectivity and evenness were getting better overall. The temporal changes of shelterbelt density had little relationship with farmland changes. This research can provide valuable information for decision makers to guide and develop future policies.

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Remote estimation of shelterbelt width from SPOT5 imagery

Cited by 9 publications

References 19 publications

Age Identification of Farmland Shelterbelt Using Growth Pattern Based on Landsat Time Series Images

Age Identification of Farmland Shelterbelt Using Growth Pattern Based on Landsat Time Series Images

Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions

Analysis of Changes in Shelterbelt Landscape in Northeast China

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