Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain

Arrogante-Funes, Patricia; Novillo, Carlos J.; Calcerrada, Raúl Romero; Vázquez-Jiménez, René; Ramos-Bernal, Rocío N.

doi:10.3390/ijgi6110353

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…This method was used to get NDVI pixel purity belonging to at least 82% of the land cover. This form of having pure pixels was used by Arrogante-Funes et al [63,64].…”

Section: Temporal Trend Analysesmentioning

confidence: 99%

Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)

2018

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Currently, there exists growing evidence that warming is amplified with elevation resulting in rapid changes in temperature, humidity and water in mountainous areas. The latter might result in considerable damage to forest and agricultural land cover, affecting all the ecosystem services and the socio-economic development that these mountain areas provide. The Mediterranean mountains, moreover, which host a high diversity of natural species, are more vulnerable to global change than other European ecosystems. The protected areas of the mountain ranges of peninsular Spain could help preserve natural resources and landscapes, as well as promote scientific research and the sustainable development of local populations. The temporal statistical trends (2001–2016) of the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) interannual dynamics are analyzed to explore whether the NDVI trends are found uniformly within the mountain ranges of mainland Spain (altitude > 1000 m), as well as in the protected or non-protected mountain areas. Second, to determine if there exists a statistical association between finding an NDVI trend and the specific mountain ranges, protected or unprotected areas are studied. Third, a possible association between cover types in pure pixels using CORINE (Co-ordination of Information on the Environment) land cover cartography is studied and land cover changes between 2000 and 2006 and between 2006 and 2012 are calculated for each mountainous area. Higher areas are observed to have more positive NDVI trends than negative in mountain areas located in mainland Spain during the 2001–2016 period. The growing of vegetation, therefore, was greater than its decrease in the study area. Moreover, differences in the size of the area between growth and depletion of vegetation patterns along the different mountains are found. Notably, more negatives than expected are found, and fewer positives are found than anticipated in the mountains, such as the Cordillera Cantábrica (C.Cant.) or Montes de Murcia y Alicante (M.M.A). Quite the reverse happened in Pirineos (Pir.) and Montes de Cádiz y Málaga (M.C.M.), among others. The statistical association between the trends found and the land cover types is also observed. The differences observed can be explained since the mountain ranges in this study are defined by climate, land cover, human usage and, to a small degree, by land cover changes, but further detailed research is needed to get in-depth detailed conclusions. Conversely, it is found that, in protected mountain areas, a lower NDVI pixels trend than expected (>20%) occurs, whereas it is less than anticipated in unprotected mountain areas. This could be caused by management and the land cover type.

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“…This method was used to get NDVI pixel purity belonging to at least 82% of the land cover. This form of having pure pixels was used by Arrogante-Funes et al [63,64].…”

Section: Temporal Trend Analysesmentioning

confidence: 99%

Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)

2018

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“…With satellite sensors, the study of phenology is possible at an adequate temporal and spatial resolution, without a high cost for the users. For example, the satellite sensors of MODIS [17,18], Landsat [19,20], AVHRR [21], VIIRS [22], and MERIS [23] have been widely used in phenological studies, and various regional [24,25], national [26,27], and global [28][29][30] phenological studies have been conducted. Concerning China's grasslands, the plant phenology in northwest China has been hotly discussed [27,[31][32][33], because northwest China is well known for its extremely harsh environment and high sensitivity to climate change [34,35].…”

Section: Introductionmentioning

confidence: 99%

“…On one hand, the changes in the phenology and their relation to temperature or precipitation have been studied repeatedly (e.g., [10,36,37]). On the other hand, the temporal-spatial patterns of the phenological changes (the variations of dates of the SOS, EOS, POS, and LOS) have been examined (e.g., [26,31,38]). In particular, the starting date of the plant growing season (SOS) has provoked fierce debate (e.g., [34,[39][40][41][42][43][44]).…”

Section: Introductionmentioning

confidence: 99%

Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010

Wang

Zhou

Peng

2018

IJGI

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Because the dynamics of phenology in response to climate change may be diverse in different grasslands, quantifying how climate change influences plant growth in different grasslands across northern China should be particularly informative. In this study, we explored the spatiotemporal variation of the phenology (start of the growing season [SOS], peak of the growing season [POS], end of the growing season [EOS], and length of the growing season [LOS]) across China’s grasslands using a dataset of the GIMMS3g normalized difference vegetation index (NDVI, 1985–2010), and determined the effects of the annual mean temperature (AMT) and annual mean precipitation (AMP) on the significantly changed phenology. We found that the SOS, POS, and EOS advanced at the rates of 0.54 days/year, 0.64 days/year, and 0.65 days/year, respectively; the LOS was shortened at a rate of 0.62 days/year across China’s grasslands. Additionally, the AMT combined with the AMP explained the different rates (ER) for the significantly dynamic SOS in the meadow steppe (R2 = 0.26, p = 0.007, ER = 12.65%) and typical steppe (R2 = 0.28, p = 0.005, ER = 32.52%); the EOS in the alpine steppe (R2 = 0.16, p < 0.05, ER = 6.22%); and the LOS in the alpine (R2 = 0.20, p < 0.05, ER = 6.06%), meadow (R2 = 0.18, p < 0.05, ER = 16.69%) and typical (R2 = 0.18, p < 0.05, ER = 19.58%) steppes. Our findings demonstrated that the plant phenology in different grasslands presented discrepant dynamic patterns, highlighting the fact that climate change has played an important role in the variation of the plant phenology across China’s grasslands, and suggested that the variation and relationships between the climatic factors and phenology in different grasslands should be explored further in the future.

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“…In this manner, it is easy to find studies where MISR products have been successfully used. Various authors (e.g., [36][37][38][39][40]) have proven their utility for characterizing the vegetal canopy and for mapping vegetation and land covers at medium and large spatial scales. This paper deals with the application of multiangular MISR data to study the effect of surface anisotropy and directional information on the classification accuracy of almost pure pixels of different land covers with a different rate of disaggregation classes in mainland Spain.…”

Section: Introductionmentioning

confidence: 99%

Improving Land Cover Classifications with Multiangular Data: MISR Data in Mainland Spain

2018

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In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land covers with different rate of disaggregation classes (from four to 35 different classes) from a Mediterranean bioregion in Iberian, Spain. We used various MISR band groups from nadir to blue, green, red, and NIR channels at nadir and off-nadir. The MISR data utilized here were provided by the L1B2T product (275 m spatial resolution) and belonged to two different orbits. We performed 23 classifications with the k-means algorithm to test multiangular data, number of clusters, and iteration effects. Our findings confirmed that the multiangular information, in addition to the multispectral information used as the input of the k-means algorithm, improves the land cover classification accuracy, and this improvement increased with the level of disaggregation. A very large number of clusters produced even better improvements than multiangular data.

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Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain

Cited by 4 publications

References 38 publications

Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)

Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)

Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010

Improving Land Cover Classifications with Multiangular Data: MISR Data in Mainland Spain

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