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

2018 ◽  
Vol 10 (12) ◽  
pp. 4363 ◽  
Author(s):  
Patricia Arrogante-Funes ◽  
Carlos Novillo ◽  
Raúl Romero-Calcerrada
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Land Cover Changes ◽  
Mountainous Area ◽  
Statistical Association ◽  
Mountain Areas ◽  
Mountain Ranges ◽  
Statistical Trends

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.

scholarly journals Response of vegetation to the 2003 European drought was mitigated by height

2013 ◽  
Vol 10 (10) ◽  
pp. 16075-16100
Author(s):  
S. L. Bevan ◽  
S. O. Los ◽  
P. R. J. North
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Diurnal Temperature Range ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Changes ◽  
High Latitudes ◽  
Vegetation Height ◽  
Direct Measurements ◽  
Surface Models

Abstract. The effects on climate of land-cover change, predominantly from forests to crops or grassland, are reasonably well understood for low and high latitudes but are largely unknown for temperate latitudes. The main reason for this gap in our knowledge is that there are compensating effects on the energy and water balance when land cover changes. To obtain a better understanding of the direction of this response, we analyse the differential response of tall and short vegetation to the 2003 European drought. We analyse precipitation, temperature and normalized difference vegetation index data and compare these with direct measurements of vegetation height. At the height of the 2003 drought we find for tall vegetation a significantly smaller decrease in vegetation index and a smaller diurnal temperature range, indicating less water stress on tall vegetation, which can be explained by access of tall vegetation to deeper soil water. Based on these results we question the current parameterizations of short and tall vegetation in some land-surface models.

scholarly journals Monitoring the Land Cover Changes in Mangrove Areas and Urbanization using Normalized Difference Vegetation Index and Normalized Difference Built-up Index in Krabi Estuary Wetland, Krabi Province, Thailand

2021 ◽  
pp. 1-16
Author(s):  
Katawut Waiyasusri
Keyword(s):  
Land Cover ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Forest Degradation ◽  
Tourism Industry ◽  
Land Cover Changes ◽  
Band Ratio ◽  
The Impact

Krabi Estuary Wetland (KEW) is an outstanding wetland with an estuary environment. At present, the tourism industry has rapidly grown, resulting in the impact of land cover changes. This research aims to assess the changes that have occurred in the KEW from 1999 to 2020 using NDVI and NDBI for monitoring changes in mangrove areas and urbanization in Krabi Province, Thailand. Landsat satellite images in years 1999, 2009 and 2020 were classified by using a band ratio to create land cover maps. The results show that NDVI between 0.41–1.00 clearly shows the mangrove forest area, while NDBI between 0.01–0.40 shows urban and built-up land, and 0.41–1.00 appears as bare land. The NDVI overall accuracy assessment is 82.88%, 97.46% and 88.25% with Kappa values of 0.64, 0.92, and 0.85 for year 1999, 2009 and 2020, respectively. The NDBI overall accuracy assessment is 92.81%, 77.11% and 64% with Kappa values of 0.93, 0.77, and 0.63 for year 1999, 2009 and 2020, respectively. In addition, areas that are sensitive to land-cover change appear around the Chi rat River, Pak Nam Krabi River, and Yuan River, which are tourist areas close to the Krabi and Ao Nang communities. Therefore, it is necessary to speed up the problem solving and find measures to prevent mangrove forest degradation in these 3 mangrove forest areas so that the mangrove forest areas will not decrease rapidly in the future. This research can be valuable for land-cover management in the KEW by policy and decision makers.

scholarly journals Intersection of Physical and Anthropogenic Effects on Land-Use/Land-Cover Changes in Coastal China of Jiangsu Province

2019 ◽  
Vol 11 (8) ◽  
pp. 2370 ◽  
Author(s):  
Xiaowei Chuai ◽  
Jiqun Wen ◽  
Dachang Zhuang ◽  
Xiaomin Guo ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Driving Forces ◽  
Land Use Changes ◽  
Temporal Analysis ◽  
Jiangsu Province ◽  
Land Cover Changes ◽  
Land Use Type

China is experiencing substantial land-use and land-cover change (LUCC), especially in coastal regions, and these changes have caused many ecological problems. This study selected a typical region of Jiangsu Province and completed a comprehensive and detailed spatial-temporal analysis regarding LUCC and the driving forces. The results show that the rate of land-use change has been accelerating, with land-use experiencing the most substantial changes from 2005 to 2010 for most land-use types and the period from 2010 to 2015 showing a reversed changing trend. Built-up land that occupies cropland was the main characteristic of land-use type change. Southern Jiangsu and the coastline region presented more obvious land-use changes. Social-economic development was the main factor driving increased built-up land expansion and cropland reduction. In addition, land-use policy can significantly affect land-use type changes. For land-cover changes, the normalized difference vegetation index (NDVI) for the land area without land-use type changes increased by 0.005 per year overall. Areas with increasing trends accounted for 82.43% of the total area. Both precipitation and temperature displayed more areas that were positively correlated with NDVI, especially for temperature. Temperature correlated more strongly with NDVI change than precipitation for most vegetation types. Our study can be used as a reference for land-use managers to ensure sustainable and ecological land-use and coastal management.

scholarly journals Geospatial Effect on the Mining Operation in Joda/Barbil Area of Odisha, India

2021 ◽  
pp. 36-55
Author(s):  
Siba Prasad Mishra ◽  
Kamal Kumar Barik ◽  
Smruti Ranjan Panda
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Surface Air Temperature ◽  
Mining Operation ◽  
Landsat 8 ◽  
Mining Areas ◽  
Illegal Mining ◽  
Comparison Of The Results

The study aims to investigate the Geospatial effect on the extraction operation in Joda and Barbil mining areas of Keonjhar district, Odisha, India. Present work involves the topography, soil, climate, and stratigraphy investigation of the area. The acquisition of Landsat 8 TIRS (Thermal Infrared), Landsat 5 TM (Thematic Mapper), and CARTOSAT DEM data of temporal and spatial satellite images from various websites. ARC GIS and ERDAS IMAGINE 9.2 software used to find the land use and land cover images (accuracy average 90%). Normalized Difference Vegetation Index (NDVI), and Surface air Temperature (SAT) of Barbil area for 2003, 2007, 2017 and 2018 have been estimated. Comparison of the results have shown that, there is increase in built up, and mining areas whereas the agricultural land and vegetation cover are down scaled. There is constant average SAT rise of 1-2°C in all the land cover classification between 2007 and 2018. The NDVI values show conversion of sparse from dense vegetation in the area. Poor operational strategies in mines operation, like corruption, illegal mining, lack of accountability, overburden wastes/ trailing disposal, ecologic degradation, waterlogging in mine pits, and human rights violations are the root causes of environmental deterioration of the study area. It is pertinent to implement strictly, the Mines and Minerals (Development and Regulation) Amendment Act, India, 2021, regular GIS application to assess the mines volume of extraction, strict vigilance and fixation of accountability for losses of existing mines values, and afforestation/ reforestation of degraded/lost forests in Barbil area.

scholarly journals Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas

2019 ◽  
Vol 6 (4) ◽  
pp. 775
Author(s):  
Eveline Pereira ◽  
Eduarda Silveira ◽  
Inácio Thomaz Bueno ◽  
Fausto Weimar Acerbi Júnior
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Seasonal Changes ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Changes ◽  
Spectral Features ◽  
Object Based ◽  
Global Accuracy ◽  
Or Fires

The Brazilian Savannas have been under increasing anthropic pressure for many years, and land-use/land-cover changes (LULCC) have been largely neglected. Remote sensing provides useful tools to detect changes, but previous studies have not attempted to separate the effects of phenology from deforestation, clearing or fires to improve the accuracy of change detection without a dense time series. The scientific questions addressed in this study were: how well can we differentiate seasonal changes from deforestation processes combining the spatial and spectral information of bi-temporal (normalized difference vegetation index) NDVI images? Which feature best contribute to increase the separability on classification assessment? We applied an object-based remote sensing method that is able to separate seasonal changes due to phenology effects from LULCC by combining spectral and the spatial context using traditional spectral features and semivariogram indices, exploring the full capability of NDVI image difference to train random forest (RF) algorithm. We found that the spatial variability of NDVI values is not affect by vegetation seasonality and, therefore, the combination of spectral features and semivariogram indices provided high global accuracy (97.73%) to separate seasonal changes and deforestation or fires. From the total of 13 features, 6 provided the best combination to increase the separability on classification assessment (4 spatial and 2 spectral features). How to accurately extract LULCC while disregarding the ones caused by phenological differences in Brazilian seasonal biomes undergoing rapid land-cover changes can be achieved by adding semivariogram indices in combination with spectral features as input data to train RF algorithm.

scholarly journals The Study Of Land Cover Change Using Change Vector Approach Integrated With Unsupervised Classification Method: A Case In Duy Tien (Vietnam)

2020 ◽  
Vol 13 (2) ◽  
pp. 175-184
Author(s):  
Si Son Tong ◽  
Thi Lan Pham ◽  
Quoc Long Nguyen ◽  
Thi Thu Ha Le ◽  
Le Hung Trinh ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Unsupervised Classification ◽  
Bare Soil ◽  
Land Cover Changes ◽  
Landsat Images ◽  
Change Dynamics ◽  
Multi Temporal ◽  
Change Vector

Investigating information on land cover changes is an indispensable task in studies related to the variation of the environment. Land cover changes can be monitored using multi-temporal satellite images at different scales. The commonly used method is the post-classification change detection which can figure out the replacement of a land cover by the others. However, the magnitude and dimension of the changes are not been always exploited. This study employs the mixture of categorical and radiometric change methods to investigate the relations between land cover classes and the change magnitude, the change direction of land covers. Applying the Change Vector Analysis (CVA) method and unsupervised classification for two Landsat images acquired at the same day of years in 2000 and in 2017 in Duy Tien district, the experimental results show that a low magnitude of change occurs in the largest area of direction I and direction IV regarding the increase of Normalized Difference Vegetation Index (NDVI), but the opposite trend of (Bare soil Index) BI in the rice field. Alternately, the high magnitude of change is seen in the build-up class which occupies the smallest area with 1700 ha. The characterized changes produced by the CVA method provide a picture of change dynamics of land cover over the period of 2000-2017 in the study area.

scholarly journals PREDICTING FOREST LAND COVER CHANGES OF DONG NAI RESERVE, VIETNAM

2020 ◽  
Vol 25 (3) ◽  
pp. 214-228
Author(s):  
Vasiliy F. Kovyazin ◽  
◽  
Dang Thi Lan Anh ◽  
Dang Viet Hung ◽  
◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Markov Chain Model ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Chain Model ◽  
Forest Land ◽  
Protected Natural Area ◽  
Forest Land Cover

The study was conducted in Dong Nai Reserve, specially protected natural area (SPNA), Vietnam. It aims to analyze and forecast forest land cover in the Reserve. For these purposes were studied satellite images (Landsat 5, Landsat 7 and Landsat 8) taken in 2003, 2011 and 2019. The Normalized Difference Vegetation Index (NDVI) was used to identify vegetation quality. Forest land cover was divided into 5 categories using maximum likelihood classifier algorithm. In order to detect and evaluate forest land cover changes, supervised classification and image differencing method are applied. Then, Cellular Automata and Markov Chain model was employed for making forecast of forest land cover in this area. The results of the study indicate that forest land cover change is being transformed in Dong Nai Reserve. According to our estimation, from 2003 to 2019, the area covered by woody vegetation increased by 7.0 %. By 2035, the area of broad-leaved forests will increase by 1.6 %, due to a decrease in areas of meadows and shrubs. The dynamics of increasing forest land is explained by the measures taken by the Vietnamese government to expand the area of forests in SPNA.

scholarly journals Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications

2019 ◽  
Vol 8 (1) ◽  
pp. 43 ◽  
Author(s):  
Carlos Novillo ◽  
Patricia Arrogante-Funes ◽  
Raúl Romero-Calcerrada
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Type ◽  
Cover Type ◽  
Biogeographical Region ◽  
Cover Class ◽  
Statistical Trends ◽  
Earth System Models

The temporal evolution of vegetation is one of the best indicators of climate change, and many earth system models are dependent on an accurate understanding of this process. However, the effect of climate change is expected to vary from one land-cover type to another, due to the change in vegetation and environmental conditions. Therefore, it is pertinent to understand the effect of climate change by land-cover type to understand the regions that are most vulnerable to climate change. Hence, in this study we analyzed the temporal statistical trends (2001–2016) of the MODIS13Q1 normalized difference vegetation index (NDVI) to explore whether there are differences, by land-cover class and phytoclimatic type, in mainland Spain and the Balearic Islands. We found 7.6% significant negative NDVI trends and 11.8% significant positive NDVI trends. Spatial patterns showed a non-random distribution. The Atlantic biogeographical region showed an unexpected 21% significant negative NDVI trends, and the Alpine region showed only 3.1% significant negative NDVI trends. We also found statistical differences between NDVI trends by land cover and phytoclimatic type. Variance explained by these variables was up to 35%. Positive trends were explained, above all, by land occupations, and negative trends were explained by phytoclimates. Warmer phytoclimatic classes of every general type and forest, as well as some agriculture land covers, showed negative trends.

scholarly journals Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

2018 ◽  
Vol 7 (10) ◽  
pp. 405 ◽  
Author(s):  
Urška Kanjir ◽  
Nataša Đurić ◽  
Tatjana Veljanovski
Keyword(s):  
Land Use ◽  
Time Series ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Land Use Control ◽  
Sentinel 2

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

scholarly journals Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Tahmid Anam Chowdhury ◽  
◽  
Md. Saiful Islam ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Land Use Land Cover ◽  
Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

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