Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales

Michael, Cherlet; Kutnjak, Hrvoje; Šmíd, Marek; Stefan, Sommer; Ivits, Éva

doi:10.1007/978-3-319-24112-8

Cited by 156 publications

(143 citation statements)

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“…Compared to conventional PhenoCam (only with blue, green, and red channels), NIR-enabled PhenoCam-based VIs take advantage of the fact that green vegetation reflects more NIR than visible light, which makes them more relevant to monitor variation in biomass and seasonal variability in photosynthetic capacity [84]. More studies investigating other PhenoCam vegetation indices are needed, for instance NIRv and 2-band enhanced vegetation index (EVI2; EVI computed without blue band) could be complementary indexes to be applied to track GPP in future, as both indexes are reported to have a good relationship with GPP [41,85,86] and could be computed with two bands (Red and NIR).Our results confirmed that it is promising to utilize the NIR-enabled PhenoCam as a complementary and cost-effective way to characterize GPP, biomass, and phenology.…”

Section: Combing Different Phenocam-based Vis To Represent Physiologimentioning

confidence: 99%

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Luo

El-Madany

Filippa³

et al. 2018

Remote Sensing

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Tree-grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity-GPP) at four tree-grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural

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Section: Combing Different Phenocam-based Vis To Represent Physiologimentioning

confidence: 99%

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Luo

El-Madany

Filippa³

et al. 2018

Remote Sensing

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“…Remote sensing allows for the reconstruction of historical trends as well, using satellite image time series: for example, the reconstruction of the hydroperiod in Doñana marsh from 1974-2014 [14], or the assessment of rangeland conditions in semiarid regions [15]. The most widely used methods to monitor vegetation are based on the use of vegetation indexes, such as the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), as proxies of aboveground biomass [7,16,17]. However, the use of these indexes is also subjected to limitations and criticism; for example, they have been shown to saturate asymptotically at high biomass values [12,18].…”

Section: Introductionmentioning

confidence: 99%

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

et al. 2017

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Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources-particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park's marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001-2015 used to calibrate the models (R 2 = 0.65). Estimators based on a single MODIS NDVI image performed worse (R 2 ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems.

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“…Thus, the negative trends in the RUE were widely used to detect land degradation around the world [26,[30][31][32]36,63,94]. Human influence was considered in order to identify land degradation, using the residual trend method by calculating the difference between observed NDVI and NDVI as predicted from precipitation [26,33,94,95].…”

Section: Eco-hydrological Changes Of the Bjd And Adjacent Regionmentioning

confidence: 99%

“…The Remote Sensing technique can now provide the potential to retrospectively monitor the meteorological and ecological elements over these drylands. From the early 1980s, researchers began to use satellite derived vegetation index data (e.g., NDVI) to track ecological dynamics [21][22][23][24], and through the years, the remote sensing technique for monitoring ecohydrological variations has been demonstrated to be useful at regional to global spatial scales [25][26][27][28][29][30][31][32][33]. In addition, rain-use efficiency (RUE; the ratio of vegetation productivity to annual precipitation) has been widely used as a proxy to assess land degradation and improvement in drylands [31,[34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Water Loss Due to Increasing Planted Vegetation over the Badain Jaran Desert, China

et al. 2018

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Abstract:Water resources play a vital role in ecosystem stability, human survival, and social development in drylands. Human activities, such as afforestation and irrigation, have had a large impact on the water cycle and vegetation in drylands over recent years. The Badain Jaran Desert (BJD) is one of the driest regions in China with increasing human activities, yet the connection between human management and the ecohydrology of this area remains largely unclear. In this study, we firstly investigated the ecohydrological dynamics and their relationship across different spatial scales over the BJD, using multi-source observational data from 2001 to 2014, including: total water storage anomaly (TWSA) from Gravity Recovery and Climate Experiment (GRACE), normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), lake extent from Landsat, and precipitation from in situ meteorological stations. We further studied the response of the local hydrological conditions to large scale vegetation and climatic dynamics, also conducting a change analysis of water levels over four selected lakes within the BJD region from 2011. To normalize the effect of inter-annual variations of precipitation on vegetation, we also employed a relationship between annual average NDVI and annual precipitation, or modified rain-use efficiency, termed the RUE mo . A focus of this study is to understand the impact of the increasing planted vegetation on local ecohydrological systems over the BJD region. Results showed that vegetation increases were largely found to be confined to the areas intensely influenced by human activities, such as croplands and urban areas. With precipitation patterns remaining stable during the study period, there was a significant increasing trend in vegetation greenness per unit of rainfall, or RUE mo over the BJD, while at the same time, total water storage as measured by satellites has been continually decreasing since 2003. This suggested that the increased trend in vegetation and apparent increase in RUE mo can be attributed to the extraction of ground water for human-planted irrigated vegetation. In the hinterland of the BJD, we identified human-planted vegetation around the lakes using MODIS observations and field investigations. Four lake basins were chosen to validate the relationship between lake levels and planted vegetation. Our results indicated that increasing human-planted vegetation significantly increased the water loss over the BJD region. This study highlights the value of combining observational data from space-borne sensors and ground instruments to monitor the ecohydrological dynamics and the impact of human activities on water resources and ecosystems over the drylands.

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Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales

Cited by 156 publications

References 0 publications

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

Water Loss Due to Increasing Planted Vegetation over the Badain Jaran Desert, China

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