scholarly journals A Framework to Tackling the Synchrony between Social and Ecological Phases of the Annual Cyclic Movement of Transhumant Pastoralism

2020 ◽  
Vol 12 (8) ◽  
pp. 3462
Author(s):  
Natalia Pérez León ◽  
Octavio Bruzzone ◽  
Marcos H. Easdale
Keyword(s):  
Fourier Transform ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Changes ◽  
Data Series ◽  
Mountainous Areas ◽  
Cyclic Movement ◽  
The Difference ◽  
Annual Time ◽  
Transhumant Pastoralism

Transhumant pastoralism is mobile livelihood strategy in which families and their herds move seasonally from lowlands, where they settle during the winter, towards the highlands, located in mountainous areas, during the summer. We propose a framework, rooted in a socio-environmental coevolutionary perspective, for the transhumant annual cycle as comprised by the winter-phase, the summer-phase, and movement transitions between them. The aim was to assess the level of synchrony between ecological phases and social phases and the benefit of moving between pasturelands in selected study cases from Patagonia, Argentina. Ecological phases were addressed by the difference between vegetation productivity of winter- and summer-lands, with Fourier transform applied to data series of the Normalized Difference Vegetation Index (NDVI). Social phases were estimated by the proportion of annual time spent by pastoralists and their herds in each site and during transitions, respectively, obtained from interviews. The framework was sensitive to capturing differences across study cases. There was an observed tendency towards more synchronisation in the cases with closer distances and asynchrony in the cases with longer distances and longer movement transitions between pasturelands. Results are encouraging as a step towards the development of a monitoring system of both transhumant pastoralism activity and environmental changes.

scholarly journals Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

2016 ◽  
Vol 51 (7) ◽  
pp. 858-868
Author(s):  
Marcos Cicarini Hott ◽  
Luis Marcelo Tavares de Carvalho ◽  
Mauro Antonio Homem Antunes ◽  
Polyanne Aguiar dos Santos ◽  
Tássia Borges Arantes ◽  
...  
Keyword(s):  
Vegetative Growth ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Growth ◽  
Growth Index ◽  
Data Series ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Very High

Abstract: The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.

Factors influencing the pattern of fire severities in a large wildfire under extreme meteorological conditions in the Mediterranean basin

2009 ◽  
Vol 18 (7) ◽  
pp. 755 ◽  
Author(s):  
Imma Oliveras ◽  
Marc Gracia ◽  
Gerard Moré ◽  
Javier Retana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Severity ◽  
Fire Behavior ◽  
Negative Relationship ◽  
Canopy Cover ◽  
Mediterranean Ecosystems ◽  
Meteorological Conditions ◽  
Landsat Images ◽  
The Difference

In Mediterranean ecosystems, large fires frequently burn under extreme meteorological conditions, but they are usually characterized by a spatial heterogeneity of burn severities. The way in which such mixed-severity fires are a result of fuels, topography and weather remains poorly understood. We computed fire severity of a large wildfire that occurred in Catalonia, Spain, as the difference between the post- and pre-fire Normalized Difference Vegetation Index (NDVI) values obtained through Landsat images. Fuel and topographic variables were derived from remote sensing, and fire behavior variables were obtained from an exhaustive reconstruction of the fire. Results showed that fire severity had a negative relationship with percentage of canopy cover, i.e. green surviving plots were mainly those with more forested conditions. Of the topographic variables, only aspect had a significant effect on fire severity, with higher values in southern than in northern slopes. Fire severity was higher in head than in flank and back fires. The interaction of these two variables was significant, with differences between southern and northern aspects being small for head fires, but increasing in flank and back fires. The role of these variables in determining the pattern of fire severities is of primary importance for interpreting the current landscapes and for establishing effective fire prevention and extinction policies.

scholarly journals Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature

2021 ◽  
Vol 13 (1) ◽  
pp. 1561-1577
Author(s):  
Sajjad Hussain ◽  
Muhammad Mubeen ◽  
Ashfaq Ahmad ◽  
Nasir Masood ◽  
Hafiz Mohkum Hammad ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Changes ◽  
Anthropogenic Activities ◽  
Cropping Pattern ◽  
Landsat Images ◽  
Urban Climatology

Abstract The rapid increase in urbanization has an important effect on cropping pattern and land use/land cover (LULC) through replacing areas of vegetation with commercial and residential coverage, thereby increasing the land surface temperature (LST). The LST information is significant to understand the environmental changes, urban climatology, anthropogenic activities, and ecological interactions, etc. Using remote sensing (RS) data, the present research provides a comprehensive study of LULC and LST changes in water scarce and climate prone Southern Punjab (Multan region), Pakistan, for 30 years (from 1990 to 2020). For this research, Landsat images were processed through supervised classification with maps of the Multan region. The LULC changes showed that sugarcane and rice (decreased by 2.9 and 1.6%, respectively) had less volatility of variation in comparison with both wheat and cotton (decreased by 5.3 and 6.6%, respectively). The analysis of normalized difference vegetation index (NDVI) showed that the vegetation decreased in the region both in minimum value (−0.05 [1990] to −0.15 [2020]) and maximum value (0.6 [1990] to 0.54 [2020]). The results showed that the built-up area was increased 3.5% during 1990–2020, and these were some of the major changes which increased the LST (from 27.6 to 28.5°C) in the study area. The significant regression in our study clearly shows that NDVI and LST are negatively correlated with each other. The results suggested that increasing temperature in growing period had a greatest effect on all types of vegetation. Crop-based classification aids water policy managers and analysts to make a better policy with enhanced information based on the extent of the natural resources. So, the study of dynamics in major crops and surface temperature through satellite RS can play an important role in the rural development and planning for food security in the study area.

scholarly journals Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Jan Musial ◽  
Alicja Malinska ◽  
Maria Budzynska ◽  
Radoslaw Gurdak ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Incidence Angle ◽  
Temporal Frequency ◽  
European Space Agency ◽  
Wetland Ecosystems ◽  
Space Agency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

scholarly journals Cambios en la cobertura del suelo en el municipio de Nechí: Una aproximación al impacto ambiental de la minería, 1986-2010

2017 ◽  
Vol 26 (45) ◽  
Author(s):  
Michael Ezequiel Gómez-Rodríguez ◽  
Francisco José Molina-Pérez ◽  
Diana María Agudelo-Echavarría ◽  
Julio Eduardo Cañón-Barriga ◽  
Fabio De Jesús Vélez-Macías
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Bare Soil ◽  
Water Bodies ◽  
Actual State ◽  
Landsat Images ◽  
Mining History ◽  
The Difference

The municipality of Nechí (Antioquia, Colombia) has a long mining history associated with the extraction of gold. This paper evaluates the evolution of land cover changes caused by this mining activity over 24 years. The spatial analysis was based on the Normalized Difference Vegetation Index (NDVI) of three LANDSAT images (1986, 1996 and 2010). The difference in NDVI values between 1986 and 2010 were used to determine the actual state of vegetation, the direction of change (improvement, stability or deterioration), and the area associated with each soil cover. Polygons for different types of coverage (forest, pasture, bare soil, and water bodies) were extracted from each satellite image to quantify the changes and develop land cover maps for each year. Results show that almost 124.8 km² of forest have been lost during the analyzed period. By contrast, water bodies gained an area of 66.3 km². Both results may be related to the type of gold exploitation in the region.

scholarly journals Spectral Index-Based Monitoring (2000–2017) in Lowland Forests to Evaluate the Effects of Climate Change

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 411 ◽  
Author(s):  
Ferenc Kovács ◽  
András Gulácsi
Keyword(s):  
Climate Change ◽  
Biomass Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Climate Extremes ◽  
Vegetation Period ◽  
Great Plain ◽  
Data Series ◽  
Water Shortages

In the next decades, climate change will put forests in the Hungarian Great Plain in the Carpathian Basin to the test, e.g., changing seasonal patterns, more intense storms, longer dry periods, and pests are expected to occur. To aid in the decision-making process for the conservation of ecosystems depending on forestry, how woods could adapt to changing meso- and microclimatic conditions in the near future needs to be defined. In addition to trendlike warming processes, calculations show an increase in climate extremes, which need to be monitored in accordance with spatial planning, at least for medium-scale mappings. We can use the MODIS sensor dataset if up-to-date terrestrial conditions and multi-decadal geographical processes are of interest. For geographic evaluations of changes, we used vegetation spectral indices; Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), based on the summer half year, 16-day MODIS data composites between 2000 and 2017 in an intensively forested study area in the Hungarian Great Plain. We delineated forest areas on the Danube–Tisza Interfluve using Corine Land Cover maps (2000, 2006, and 2012). Mid-year changes over the nearly two-decade-long period are currently in balance; however, based on their reactions, forests are highly sensitive to abrupt changes caused by extreme climatic events. The higher occurrence of years or periods with extreme water shortages marks an observable decrease in biomass production, even in shorter index time series, such as that between 2004 and 2012. In the drought-stricken July-August periods, the effect of a dry year, subsequent to years with more precipitation, immediately pushes back the green mass and the reduction in the biomass production could become persistent, according to climatology predictions. The changes of specific sub-periods in the vegetation period can be evaluated even in a relatively short, 18-year data series, including the change in the growing values of the vegetative growth in spring or the increase in the summertime biomass production. Standardized differences highlight spatial differences in the biomass production; in response to years with the highest (negative) biomass difference; typically, the northern and southwestern parts of the Danube–Tisza Interfluve in the study area have longer lasting losses in biomass production. A comparison of NDVI and EVI values with the PaDI drought index and the vegetation indices of LANDSAT Operational Land Imager sensor respectively confirms our results.

scholarly journals Fully Automated Countrywide Monitoring of Fuel Break Maintenance Operations

2020 ◽  
Vol 12 (18) ◽  
pp. 2879
Author(s):  
Valentine Aubard ◽  
João E. Pereira-Pires ◽  
Manuel L. Campagnolo ◽  
José M. C. Pereira ◽  
André Mora ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Google Earth ◽  
Training Data ◽  
Fire Control ◽  
Earth Observations ◽  
Detection Approach ◽  
The Difference ◽  
The Impact

Fuel break (FB) networks are strategic locations for fire control and suppression. In order to be effective for wildfire control, they need to be maintained through regular interventions to reduce fuel loads. In this paper, we describe a monitoring system relying on Earth observations to detect fuel reduction inside the FB network being implemented in Portugal. Two fast automated pixel-based methodologies for monthly monitoring of fuel removals in FB are developed and compared. The first method (M1) is a classical supervised classification using the difference and postdisturbance image of monthly image composites. To take into account the impact of different land cover and phenology in the detection of fuel treatments, a second method (M2) based on an innovative statistical change detection approach was developed. M2 explores time series of vegetation indices and does not require training data or user-defined thresholds. The two algorithms were applied to Sentinel-2 10 m bands and fully processed in the cloud-based platform Google Earth Engine. Overall, the unsupervised M2, which is based on a Welch t-test of two moving window averages, gives better results than the supervised M1 and is suitable for an automated countrywide fuel treatment detection. For both methods, two vegetation indices, the Modified Excess of Green and the Normalized Difference Vegetation Index, were compared and exhibited similar performances.

scholarly journals Temporal Stability of Vegetation Cover across the Loess Plateau Based on GIMMS during 1982–2013

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 315
Author(s):  
Chunyan Zhang ◽  
Shan Guo ◽  
Yanning Guan ◽  
Danlu Cai ◽  
Xiaolin Bian
Keyword(s):  
Soil Erosion ◽  
Loess Plateau ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Changes ◽  
Temporal Stability ◽  
Reference Conditions ◽  
Reference Period ◽  
The Loess Plateau

The Loess Plateau, covering approximately 640,000 km2, has experienced the most severe soil erosion in the world. A greening tendency has been noticed since implementing the Grain to Green Program (GTGP), which may prevent further soil erosion. Therefore, understanding the underpinning basis of greening stability and persistence is important for sustainable improvement. Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets for 1982–2013 were used to investigate the temporal stability and persistent time (PT) of vegetation over the Loess Plateau, utilizing the coefficient of variation (CV) and the estimation of tendencies of vegetation greening starting from the selected reference conditions. Two periods from 1982 to 1999 (as the reference period) and 2000 to 2013 were selected by considering the GTGP since 1999. The results indicate that: (1) A significant increase in vegetation cover occurred in the low NDVI area (NDVI < 0.3), with a high fluctuation from 2000 to 2013 compared with the reference period. Moreover, the fluctuation in vegetation is more related to precipitation variation since 1999. (2) Most areas recovered in the greening trend of the first period starting in 2009, occurring in 28.7% (2628 of 9148) of the total area. (3) The revegetated areas have a low PT and a high CVvi, that is, the revegetated areas need a long time to recover from disturbances. Therefore, we identify the sensitive areas with PT = 4; further management needs to be implemented for sustainable development in these areas. These results provide a method to quantify the stability and persistence of the complex interactions between vegetation greenness and environmental changes, particularly in fragile areas.

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