Sistema para el Seguimiento del funcionamiento de ecosistemas en la Red de Parques Nacionales de España mediante Teledetección

<p>Management of protected areas in the current context of global change requires approaches to characterize and to monitor ecosystem functioning. Remote sensing provides adequate tools for that because it provides índices that inform repeatedly and for large areas of land, about matter and energy exchanges between the biota and land surface. Considering this principle, and the continuous improvements in the availability of satellite data of higher quality and friendly use, we have developed with the Autonomous Organization of National Parks of Spain (OAPN), a monitoring system that complements other monitoring initiatives from this agency to inform about the conservation status of national parks. The system, called REMOTE, is based on the most used spectral vegetation indices on scientific literature, EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), derived from the time series of satellite images of the MODIS-Terra sensor. The systems allows to progress in identification of reference conditions to understand and predict ecosystems response against environmental perturbations or management actions, and their directional changes (trends) they are experiencing. Likewise, establishment of reference conditions helps to identify anomalies that warn of sudden changes in ecosystem functioning. The system uses as ecosystem functioning indicators three attributes related to the annual carbon gains (net primary production) by the canopy, their seasonality and phenology. In addittion, Remote has been designed and programmed on open and free software allowing future modifications and improvements in an easy way. The implementation of this system aims to inform decision-makers and managers of the Network of National Parks of Spain about the health and conservation status of ecosystems.</p>

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Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan

Remote Sensing ◽

10.3390/rs12152433 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2433 ◽

Cited By ~ 3

Author(s):

Iman Rousta ◽

Haraldur Olafsson ◽

Md Moniruzzaman ◽

Hao Zhang ◽

Yuei-An Liou ◽

...

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Condition Index ◽

Vegetation Coverage ◽

P Value ◽

Human Society ◽

Time Series Regression ◽

Drought Conditions

Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI.

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The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

Remote Sensing ◽

10.3390/rs12121979 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1979

Author(s):

Dandan Xu ◽

Deshuai An ◽

Xulin Guo

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Series Data ◽

Vegetation Indices ◽

Series Data ◽

Area Index ◽

Plant Area Index ◽

The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

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Mining and Restoration Monitoring of Rare Earth Element (REE) Exploitation by New Remote Sensing Indicators in Southern Jiangxi, China

Remote Sensing ◽

10.3390/rs12213558 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3558

Author(s):

Lifeng Xie ◽

Weicheng Wu ◽

Xiaolan Huang ◽

Penghui Ou ◽

Ziyu Lin ◽

...

Keyword(s):

Remote Sensing ◽

Rare Earth ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Rapid Expansion ◽

Open Pit ◽

Open Pit Mining ◽

Chemical Extraction

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km2 in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area.

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Investiganting Correlation LST and Vegetation Indices Using Landsat Images for the Warmest Month: A Case Study of Iasi County

Annals of Valahia University of Targoviste Geographical Series ◽

10.2478/avutgs-2018-0004 ◽

2018 ◽

Vol 18 (1) ◽

pp. 33-40

Author(s):

Paul Macarof ◽

Stefan Groza ◽

Florian Statescu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Global Scale ◽

Structural Variations ◽

Landsat Images ◽

Change Models

Abstract In this paper is investigating correlation between land surface temperature and vegetation indices (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index 2 - EVI2 and Modified Soil Adjusted Vegetation Index - MSAVI) using Landsat images for august, the warmest month, for study area. Iaşi county is considered as study area in this research. Study Area is geographically situated on latitude 46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. Land surface temperature (LST) can be used to define the temperature distribution at local, regional and global scale. First use of LST was in climate change models. Also LST is use to define the problems associated with the environment. A Vegetation Indices (VI) is a spectral transformation what suppose spatial-temporal intercomparisons of terrestrial photosynthetic dynamics and canopy structural variations. Landsat5 TM, Landsat7 ETM+ and Landsat8 OLI, all data were used in this study for modeling. Landsat images was taken for august 1994, 2006 and 2016. Preprocessing of Landsat 5/7/8 data stage represent that process that prepare images for subsequent analysis that attempts to compensate/correct for systematic errors. It was observed that the “mean” parameter for LST increased from 1994 to 2016 at approximately 5°C. Analyzing the data from VI, it can be assumed that the built-up area increased for the Iasi county, while the area occupied by dense vegetation has decreased. Many researches indicated that between LST and VI is a linear relationship. It is noted that the R2 values for the LST-VI correlations decrease from 1994 (i.g.R2= 0.72 for LST-NDVI) in 2016 (i.g.R2= 0.23 for LST-NDVI). In conclusion, these correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.

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Correction of Directional Effects in VEGETATION NDVI Time-Series

Remote Sensing ◽

10.3390/rs13061130 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1130

Author(s):

Jonathan León-Tavares ◽

Jean-Louis Roujean ◽

Bruno Smets ◽

Erwin Wolters ◽

Carolien Toté ◽

...

Keyword(s):

Time Series ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Global Scale ◽

Frequency Noise ◽

Surface Reflectance ◽

Computationally Efficient ◽

Land Surface Reflectance

Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.

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Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing

Remote Sensing ◽

10.3390/rs12040671 ◽

2020 ◽

Vol 12 (4) ◽

pp. 671 ◽

Cited By ~ 2

Author(s):

Matthew Dannenberg ◽

Xian Wang ◽

Dong Yan ◽

William Smith

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Microwave Remote Sensing ◽

Self Organizing Maps ◽

Growth Cycles ◽

Growing Seasons ◽

Temperature And Precipitation ◽

Optical Fluorescence

Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global “phenoregions” using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions “self-organized” along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear.

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Analysis of Relationship between Vegetation Indices and Land Surface Temperature using GIS and Python in Middle Ganga Plain, India

10.21523/gcj5.18020201 ◽

2019 ◽

Vol 2 (2) ◽

pp. 53-61

Author(s):

Aman Arora ◽

Masood Siddiqui ◽

Manish Pandey

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Empirical Relationship ◽

Vegetation Indices ◽

Temporal Distribution ◽

Ganga Plain ◽

Middle Ganga Plain

Global vegetation dynamics is a significant phenomenon being monitored from space. This study attempts to establish relationship among vegetation changes and land surface temperature using the data derived from satellite products in the Middle Ganga Plain, India using python programming. Ten years of MODIS Land Surface Temperature (LST) on board Terra, Normalized Difference Vegetation Index/Enhanced Vegetation Index (NDVI/EVI) (1km spatial and 8-days composite temporal resolution for LST and 250m spatial and 16-days composite for NDVI/EVI) has been used in this study. The average LST for the month of January was 23.0°C which fell to 15.7°C for the same month in 2015; whereas in March it was recorded to be 35.3°C and reduced to 32.3°C in 2015. Mean NDVI value has been recorded to be 0.44 in January 2000 which has slightly increased to reach 0.50 for the same month in 2015. For the month of September, it was recorded at 0.49 in 2000 and 0.52 for the same month in 2015. This paper attempts to analyze the spatio-temporal distribution and empirical relationship of vegetation cover and LST using Python.

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A Comparative Study for Performance of Five Landsat-based Vegetation Indices: Their Relations to Some Ecological and Terrain Variables

Journal of Geoinformatics & Environmental Research ◽

10.38094/jgier119 ◽

2020 ◽

Vol 1 (1) ◽

pp. 20-37

Author(s):

Ayad Al-Quraishi ◽

Hawar Razvanchy ◽

Heman Gaznayee

Keyword(s):

Aspect Ratio ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Point Of View ◽

Vegetation Growth ◽

Spectral Vegetation Indices ◽

Landsat 7 ◽

Total Study Area

Spectral vegetation indices and their relations to some ecological and terrain variables in the Iraqi Kurdistan Region (IKR) is the main objective of this study. A mosaic of two Landsat-7 ETM+ images was utilized to produce five spectral vegetation indices, and Terra ASTER Digital Elevation Model (DEM) dataset were employed. The Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Tasseled Cap Greenness, Land Surface Temperature (LST) were utilized for this study. The results of the current study revealed that MSAVI2 is more reliable and accurate in depicting the vegetation presence in the IKR, which is occupied 34.7% of the total study area in 2014. In terms of terrain variables, all vegetation indices responded to variation of aspect ratio variation. It was found that the densest vegetation exists between 180 to 350°. Mainly, in the South (157.5°-202.5°), Southwest (202.5°-247.5°), West (247.5°-292.5°), Northwest (292.5°-337.5°), and North (337.5°-360°). In contrast, from the aspect ratio point of view, vegetation cover growth was in its maximum status in the shaded side of the mountains, more than the sunny side. Additionally, the adequate slope for vegetation growth in the mountainous lands is 9-17%. Statistically, the LST appeared negative relations with vegetation indices and elevation

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HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

2020 ◽

Vol 7 (1) ◽

pp. 21

Author(s):

Faradina Marzukhi ◽

Nur Nadhirah Rusyda Rosnan ◽

Md Azlin Md Said

Keyword(s):

Oil Palm ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Soil Nutrient ◽

Oil Palm Plantation ◽

Sustainable Environment ◽

Moisture Analysis ◽

The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽

10.3390/technologies9020040 ◽

2021 ◽

Vol 9 (2) ◽

pp. 40

Author(s):

Guang Yang ◽

Yuntao Ma ◽

Jiaqi Hu

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Extraction Methods ◽

Average Error ◽

Geographic Coordinate System ◽

Optimal Thresholds ◽

Moderate Resolution Imaging Spectroradiometer ◽

Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

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