Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece

Röder, Achim; Udelhoven, T.; Hill, Joachim; Barrio, Gabriel del; Tsiourlis, Georgios

doi:10.1016/j.rse.2008.01.018

Cited by 107 publications

(57 citation statements)

References 52 publications

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“…At present, some principal methods are as follows: (1) Extraction of rangeland degradation information based on remote sensing image classification; (2) Direct comparison. This method takes non-degraded rangeland as a reference through the comparison of characteristic parameters observed directly (such as biomass, vegetation coverage, edible forage, NDVI, NPP, soil physical and chemical properties indices) to analyze the degradation/restoration of rangeland (Numata et al, 2007;Liu and Zha, 2004;Röder et al, 2008); (3) Monitoring rangeland degradation based on time series analysis of remote sensing. In recent years, these methods have caught widespread attention, and mainly include rainfall use efficiency (RUE) (Wessels et al, 2006;Prince et al, 2004;Paruelo et al, 1999;Holm et al, 2003;Gao et al, 2005;Bai et al, 2008a) and residual trends (RESTREND) (Evans and Geerken, 2004;Wessels et al, 2007;Xu et al, 2010;Cao, 2006;Eckert et al, 2015); (4) Local NPP (the actual Net Primary Productivity) Scaling (Wessels et al, 2007;Wessels et al, 2008;Prince et al, 2009).…”

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confidence: 99%

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

Wang

Xia

et al. 2017

Quaternary International

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Abstract-the "Three-River Headwaters" region (TRHR) rangeland ecosystem is extremely sensitive and fragile, and in recent years has undergone continuous degradation. The vast area and severe nature conditions inhibit data acquisition and field experiments, resulting in different understandings about the spatial characteristics and dynamics of rangeland degradation in the region. Therefore, reliable monitoring method of rangeland degradation is urgently needed for the rangeland protection and management. In this paper, a novel rangeland degradation monitoring scheme based on Local NPP Scaling (LNS) was suggested;A suitable partition program of rangeland productivity unit was set up by using Analytical Hierarchy Process (

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confidence: 99%

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

Wang

Xia

et al. 2017

Quaternary International

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“…As a result, the assessment of post-fire vegetation regeneration is of crucial importance 83 for the understanding of the environmental impacts of fire and for supporting sustainable post-84 fire management (e.g. controlled grazing, Roder et al 2008b). In comparison with labor-85 intensive field work, the synoptic nature of remote sensing systems offers a time-and cost-86 effective means to fulfill this duty (Lentile et al 2006).…”

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confidence: 99%

Assessing post-fire vegetation recovery using red–near infrared vegetation indices: Accounting for background and vegetation variability

Veraverbeke

Gitas

Katagis

et al. 2012

ISPRS Journal of Photogrammetry and Remote Sensing

102

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“…The TM scenes were downloaded from the USGS EarthExplorer online catalogue as L1T product, consisting of at-sensor radiance single spectral channels orthorectified using a DEM and co-registered. Dense time series, or "stacks", of Landsat scenes have recently been used in temperate environments for detecting both long-term vegetation trends [36] and land cover transitions [37,38]. The main advantage of this approach is that the larger number of observations can allow long-term changes to be detected with greater sensitivity and reliability by comparison to conventional two-date change detection.…”

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confidence: 99%

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

2015

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Forest dynamics influence climate, biodiversity, and livelihoods at multiple scales, yet current resource policy addressing these dynamics is ineffective without reliable land use land cover change data. The collective impact of harvest decisions by many small forest owners can be substantial at the landscape scale, yet monitoring harvests and regrowth in these forests is challenging. Remote sensing is an obvious route to detect and monitor small-scale land use dynamics over large areas. Using an annual series of Landsat-5 Thematic Mapper (TM) images and a GIS shapefile of property boundaries, we identified units where harvests occurred from 2005 to 2011 using an Object-Based Change Detection (OBCD) approach. Percent of basal area harvested was verified using stand-level harvest data. Our method detected all harvests above 20% basal area removal in all forest types (northern hardwoods, mixed deciduous/coniferous, coniferous), on properties as small as 10 acres (0.4 ha; approximately four Landsat pixels). Our results had a resolution of about 10% basal area (that is, a selective harvest removal of 30% could be distinguished from one of OPEN ACCESS Remote Sens. 2015, 7 7810 40%). Our method can be automated and used to measure annual harvest rates and intensities for large areas of the United States, providing critical information on land use transition.

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Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece

Cited by 107 publications

References 52 publications

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

Assessing post-fire vegetation recovery using red–near infrared vegetation indices: Accounting for background and vegetation variability

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

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