Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

Background

A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP). The CASA (Carnegie Ames Stanford Approach) model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP) and soil respiration components. Predicted net ecosystem production (NEP) flux of CO₂ is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire) disturbance histories. Monthly Enhanced Vegetation Index (EVI) image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (from 2000 to 2006) were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD) in areas burned by wildfires over the past two decades.

Results

Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush) cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr^-1 (1 Tg = 10¹² g) for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO₂ on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr^-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO₂ to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr^-1 for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink Fire, and Falls Fire areas.

Conclusions

Rates of recovery for burned forest areas to pre-1988 biomass levels were estimated from a unique combination of remote sensing and CASA model predictions. Ecosystem production and carbon fluxes in the Greater Yellowstone Ecosystem (GYE) result from complex interactions between climate, forest age structure, and disturbance-recovery patterns of the landscape.     

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

ABSTRACT

A fractional vegetation cover (FVC) estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed, which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarse-resolution FVC pixel were all assumed to have the same vegetation growth model. However, this assumption does not hold over heterogeneous areas, meaning that the method cannot be applied to large regions. Therefore, this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite (GLASS) FVC product. The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data. Then, independent dynamic vegetation models were built for each finer-resolution pixel. Finally, the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale. Validation results indicated that the proposed method (R²?=?0.7757, RMSE?=?0.0881) performed better than either the previous method (R²?=?0.7038, RMSE?=?0.1125) or a commonly used method involving look-up table inversions of the PROSAIL model (R²?=?0.7457, RMSE?=?0.1249).     

Driving forces of grassland vegetation changes in Chen Barag Banner,Inner Mongolia

ABSTRACT

Inner Mongolia is an important ecological zone of northern China and 67% of its land area is grassland. This ecologically fragile region has experienced significant vegetation degradation during the last decades. Although the spatial extents and rates of vegetation change have previously been characterized through various remote sensing and GIS studies, the underlying driving factors of vegetation changes are still not well understood. In this study, we first used time-series MODIS NDVI data from 2000 to 2016 to characterize the temporal trend of vegetation changes. These vegetation change trends were compared with climate and socioeconomic variables to determine the potential drivers. We used a set of statistical methods, including multiple linear regression (MLR), spatial correlation analysis, and partial least squares (PLS) regression analyzes, to quantify the spatial distribution of the driving forces and their relative importance to vegetation changes. Results show that the main driving factors and their impact magnitude (weight) are in the order of human activities (r = -0.785, p < 0.01, VIP = 1.37), precipitation (r = 0.541, p < 0.05, VIP = 0.89), temperature (r = -0.319, p > 0.05 VIP = 0.59). The area affected by human activities was 10.57%. Specific human activities, such as coal mining and grazing were negatively associated with vegetation cover, while eco-engineering projects had positive impacts. This study provided thorough quantification of driving forces of vegetation change and enhanced our understanding of their interactions. Our integrated geospatial-statistical approach is particularly important for sustainable development of ecosystem balance in Chen Barag Banner and other areas facing similar challenges.     

Landcover Classification in the Taieri River Catchment,New Zealand: A Focus on the Riparian Zone

Abstract

Riparian vegetation has a fundamental influence on the biological, chemical and physical nature of rivers. The quantification of riparian landcover is now recognised as being essential to the holistic study of the ecosystem characteristics of rivers. Medium resolution satellite imagery is now commonly used as an efficient and cost effective method for mapping vegetation cover; however such data often lack the resolution to provide accurate information about vegetation cover within riparian corridors. To assess this, we measure the accuracy of SPOT multispectral satellite imagery for classification of riparian vegetation along the Taieri River in New Zealand. In this paper, we discuss different sampling strategies for the classification of riparian zones. We conclude that SPOT multispectral imagery requires considerable interpretative analysis before being adequate to produce sufficiently detailed maps of riparian vegetation required for use in stream ecological research.     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

Assessing environmental impacts of urban growth using remote sensing

ABSTRACT

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.     

Earth observations during space shuttle mission STS‐28 8–13 August 1989

Abstract

The paper presents a geospatial modeling approach for the assessment of plant richness in Barsey Rhododendron Sanctuary in Sikkim, a Himalayan State of India located in the “Indo‐Burma” biodiversity hotspot. Remotely sensed data from Indian Remote Sensing Satellite IRS‐1C Linear Imaging Self‐Scanner (LISS‐III) and field‐based methods were synergistically used to model plant richness on 1:50,000 scale. It was found that the sanctuary is dominated by East Himalayan Moist Temperate Forest (55.50%), followed by Rhododendron Forest (23.77%), Degraded Forest (6.66%) and Hemlock Forest (0.78%). The vegetation map prepared through digital interpretation of satellite imagery was subjected to landscape analysis and assessment of biotic disturbance in terms of disturbance index. The disturbance index together with species richness, ecosystem uniqueness, total importance value and terrain complexity was modeled to assess the plant richness in this unique sanctuary. Out of the 120 km² of the total geographical area of the sanctuary, 28.45 per cent was found to possess very high plant richness followed by high (50.84%), medium (6.96%) and low richness (13.75%). It was noted that plant richness assessment at ecosystem level presents a more realistic picture than at landscape level. The study demonstrated that remote sensing coupled with landscape analysis, ground inventory data and geospatial modeling holds good potential for rapid and operational assessment of plant richness.     

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Abstract

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as built-up land features extraction index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the spectral discrimination index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.     

Risk assessment of soil erosion in semi-arid mountainous watershed in Saudi Arabia by RUSLE model coupled with remote sensing and GIS

Soil erosion is the most important factor in land degradation and influences desertification in semi-arid areas. A comprehensive methodology that integrates revised universal soil loss equation (RUSLE) model and GIS was adopted to determine the soil erosion risk (SER) in semi-arid Aseer region, Saudi Arabia. Geoenvironmental factors viz. rainfall (R), soil erodibility (K), slope (LS), cover management and practice factors were computed to determine their effects on average annual soil loss. The high potential soil erosion, resulting from high denuded slope, devoid of vegetation cover and high intensity rainfall, is located towards the north western part of the study area. The analysis is investigated that the SER over the vegetation cover including dense vegetation, sparse vegetation and bushes increases with the higher altitude and higher slope angle. The erosion maps generated with RUSLE integrated with GIS can serve as effective inputs in deriving strategies for land planning/management in the environmentally sensitive mountainous areas.     

Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: a case study on Brussels,Belgium

ABSTRACT

Cities often have a substantial green infrastructure, which provides local ecosystem services that improve the quality of life of urban residents. These services should be explicitly addressed in urban development policies, and areas with insufficient vegetation and limited access to public green spaces should be identified. This paper presents two spatially explicit urban green indicators that are derived using remote sensing imagery, freely available map data and spatial analysis tools from open source geospatial libraries and commercial software. The first indicator represents proportional green cover (public as well as private) in the vicinity of each building within a city. The second indicator quantifies the proximity of public green spaces as walking distances from buildings to actual park entrances. A dasymetric mapping approach was used to take spatial variations in population density into account. This allows representing the indicators from the perspective of citizens instead of buildings, which may be more meaningful for deriving statistics at city level or at the level of neighbourhoods or administrative zones. The potential use of these indicators in a planning context is discussed on a case study carried out for the city of Brussels, Belgium.     

Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas

Fractional vegetation cover (FVC) is an important indicator of mountain ecosystem status. A study on the seasonal changes of FVC can be beneficial for regional eco-environmental security, which contributes to the assessment of mountain ecosystem recovery and supports mountain forest planning and landscape reconstruction around megacities, for example, Beijing, China. Remote sensing has been demonstrated to be one of the most powerful and feasible tools for the investigation of mountain vegetation. However, topographic and atmospheric effects can produce enormous errors in the quantitative retrieval of FVC data from satellite images of mountainous areas. Moreover, the most commonly used analysis approach for assessing FVC seasonal fluctuations is based on per-pixel analysis regardless of the spatial context, which results in pixel-based FVC values that are feasible for landscape and ecosystem applications. To solve these problems, we proposed a new method that incorporates the use of a revised physically based (RPB) model to correct both atmospheric and terrain-caused illumination effects on Landsat images, an improved vegetation index (VI)-based technique for estimating the FVC, and an adaptive mean shift approach for object-based FVC segmentation. An array of metrics for segmented FVC analyses, including a variety of area metrics, patch metrics, shape metrics and diversity metrics, was generated. On the basis of the individual segmented FVC values and landscape metrics from multiple images of different dates, remote sensing of the seasonal variability of FVC was conducted over the mountainous area of Beijing, China. The experimental results indicate that (a) the mean value of the RPB–NDVI in all seasons was increased by approximately 10% compared with that of the atmospheric correction-NDVI; (b) a strong consistency was demonstrated between ground-based FVC observations and FVC estimated through remote sensing technology (R² = 0.8527, RMSE = 0.0851); and (c) seasonal changes in the landscape characteristics existed, and the landscape diversity reached its maximum in May and June in the study area.     

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract

An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.     

Reclamation areas and their development studied by vegetation indices

Abstract

Large areas in the Czech Republic were used for open casts of brown coal mining. Many of them have been already closed. Reclamation of them and of their dumps is the next step intheir development. It is possible to divide used reclamations into the forest, hydrologic, agricultural and other ones – roads, etc. Their age varies from 45 years to as yet unfinished. Reclaimed areas are documented in reclamation projects. Information about age and land use determined groups of these areas to be evaluated by vegetation indices. 100 areas with forest type were evaluated. Eight vegetation indices (NDVI, DVI, RVI, PVI, SAVI, MSAVI, TSAVI and EVI) were calculated and their average value in each area in 1988, 1992 and 1998 Thematic Mapper data were compared. Changes over years showed close relation to precipitations of previous periods. This relation was confirmed by evaluation of forest areas situated near reclamation areas. Positive/negative changes of vegetation indices were different for different groups and different vegetation indices. An overview of results of vegetation indices is presented for individual areas whose land use comprised at least partly forest stand. Results in a 4-year period (1988–1992) were in many areas by many indices negative, changes in 10 years were in most areas by most vegetation indices positive. Changes, minimum values and maximum values in groups were compared. Evaluation of vegetation indices brought again various results. One vegetation index is not sufficient to prove improvement/deterioration of vegetation changes. Precipitation state before measurement should be controlled. Temporary shortage of precipitation can cause vegetation cover deterioration, which is also only temporary. The best development derived from vegetation indices evaluation was found at forest reclamation with mixed tree stand that was 10–20 years old. The method was derived as a tool for post-finishing control of vegetation development of reclamations performed in several year periods.     

Elephants move faster in small fragments of low productivity in Amboseli ecosystems: Kenya

Understanding factors affecting the behaviour and movement patterns of the African elephant is important for wildlife conservation, especially in increasingly human-dominated savanna landscapes. Currently, knowledge on how landscape fragmentation and vegetation productivity affect elephant speed of movement remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explains elephant speed of movement in the Amboseli ecosystem in Kenya. We used GPS collar data from five elephants to quantify elephant speed of movement for three seasons (wet, dry and transitional). We then used multiple regression to model the relationship between speed of movement and landscape fragmentation, as well as vegetation productivity for each season. Results of this study demonstrate that landscape fragmentation and vegetation productivity predicted elephant speed of movement poorly (R² < 0.4) when used as solitary covariates. However, a combination of the covariates significantly (p < 0.05) explained variance in elephant speed of movement with improved R² values of 0.69, 0.45, 0.47 for wet, transition and dry seasons, respectively.     

Autumn NDVI contributes more and more to vegetation improvement in the growing season across the Tibetan Plateau

ABSTRACT

Detecting changes in vegetation, distinguishing the persistence of changes, and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics. Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (NDVI) version NDVI_3g dataset in the Tibetan Plateau, the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982. Dynamic trends in vegetation experienced an obvious increase at regional scale, but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended. At pixel scale, areas with significant browning significantly expanded over 14 periods for all seasons, but for significant greening significantly increased only in autumn. The changes of vegetation activity in spring were the most drastic among three seasons. Increased increments of NDVI in summer, spring, and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods. Vegetation activity was mainly regulated by a thermal factor, and the dominant climatic drivers of vegetation growth varied across different seasons and regions. We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.     

Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery

Careful evaluation of forest regeneration and vegetation recovery after a fire event provides vital information useful in land management. The use of remotely sensed data is considered to be especially suitable for monitoring ecosystem dynamics after fire. The aim of this work was to map post-fire forest regeneration and vegetation recovery on the Mediterranean island of Thasos by using a combination of very high spatial (VHS) resolution (QuickBird) and hyperspectral (EO-1 Hyperion) imagery and by employing object-based image analysis. More specifically, the work focused on (1) the separation and mapping of three major post-fire classes (forest regeneration, other vegetation recovery, unburned vegetation) existing within the fire perimeter, and (2) the differentiation and mapping of the two main forest regeneration classes, namely, Pinus brutia regeneration, and Pinus nigra regeneration. The data used in this study consisted of satellite images and field observations of homogeneous regenerated and revegetated areas. The methodology followed two main steps: a three-level image segmentation, and, a classification of the segmented images. The process resulted in the separation of classes related to the aforementioned objectives. The overall accuracy assessment revealed very promising results (approximately 83.7% overall accuracy, with a Kappa Index of Agreement of 0.79). The achieved accuracy was 8% higher when compared to the results reported in a previous work in which only the EO-1 Hyperion image was employed in order to map the same classes. Some classification confusions involving the classes of P. brutia regeneration and P. nigra regeneration were observed. This could be attributed to the absence of large and dense homogeneous areas of regenerated pine trees in the study area.     

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.     

Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices

ABSTRACT

Fractional green vegetation cover (FVC) is a useful indicator for monitoring grassland status. Satellite imagery with coarse spatial but high temporal resolutions has been preferred to monitor seasonal and inter-annual FVC dynamics in wide geographic area such as Mongolian steppe. However, the coarse spatial resolution can cause a certain uncertainty in the satellite-based FVC estimation, which calls attention to develop a robust statistical test for the relationship between field FVC and satellite-derived vegetation indices. In the arid and semi-arid Mongolian steppe, nadir pointing digital camera images (DCI) were collected and used to produce a FVC dataset to support the evaluation of satellite-based FVC retrievals. An optimal DCI processing method was determined with respect to three color spaces (RGB, HIS, L*a*b*) and six green pixel classification algorithms, from which a country-wide dataset of DCI-FVC was produced and used for evaluating the accuracy of satellite-based FVC estimates from MODIS vegetation indices. We applied three empirical and three semi-empirical MODIS-FVC retrieval models. DCI data were collected from 96 sites across the Mongolian steppe from 2012 to 2014. The histogram algorithm using the hue (H) value of the HIS color space was the optimal DCI method (r² = 0.94, percent root-mean-square-error (RMSE) = 7.1%). For MODIS-FVC retrievals, semi-empirical Baret model was the best-performing model with the highest r² (0.69) and the lowest RMSE (49.7%), while the lowest MB (+1.1%) was found for the regression model with normalized difference vegetation index (NDVI). The high RMSE (>50% or so) is an issue requiring further enhancement of satellite-based FVC retrievals accounting for key plant and soil parameters relevant to the Mongolian steppe and for scale mismatch between sampling and MODIS data.     

Vegetation species mapping in a coastal-dune ecosystem using high resolution satellite imagery

Vegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation.