scholarly journals Intercomparison of AVHRR GIMMS3g, Terra MODIS, and SPOT-VGT NDVI Products over the Mongolian Plateau

2019 ◽  
Vol 11 (17) ◽  
pp. 2030 ◽  
Author(s):  
Yongqing Bai ◽  
Yaping Yang ◽  
Hou Jiang
Keyword(s):  
Vegetation Index ◽  
Environmental Changes ◽  
Rapid Development ◽  
Pearson Correlation ◽  
Regional Scale ◽  
The Other ◽  
Mean Bias Error ◽  
Landsat 8 ◽  
Mongolian Plateau ◽  
Spot Vgt Ndvi

The rapid development of remote sensing technology has promoted the generation of different vegetation index products, resulting in substantive accomplishment in comprehensive economic development and monitoring of natural environmental changes. The results of scientific experiments based on various vegetation index products are also different with the variation of time and space. In this work, the consistency characteristics among three global normalized difference vegetation index (NDVI) products, namely, GIMMS3g NDVI, MOD13A3 NDVI, and SPOT-VGT NDVI, are intercompared and validated based on Landsat 8 NDVI at biome and regional scale over the Mongolian Plateau (MP) from 2000 to 2014 by decomposing time series datasets. The agreement coefficient (AC) and statistical scores such as Pearson correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and standard deviation (STD) are used to evaluate the consistency between three NDVI datasets. Intercomparison results reveal that GIMMS3g NDVI has the highest values basically over the MP, while SPOT-VGT NDVI has the lowest values. The spatial distribution of AC values between various NDVI products indicates that the three NDVI datasets are highly consistent with each other in the northern regions of the MP, and MOD13A3 NDVI and SPOT-VGT NDVI have better consistency in expressing vegetation cover and change trends due to the highest proportions of pixels with AC values greater than 0.6. However, the trend components of decomposed NDVI sequences show that SPOT-VGT NDVI values are about 0.02 lower than the other two datasets in the whole variation periods. The zonal characteristics show that GIMMS3g NDVI in January 2013 is significantly higher than those of the other two datasets. However, in July 2013, the three datasets are remarkably consistent because of the greater vegetation coverage. Consistency validation results show that values of SPOT-VGT NDVI agree more with Landsat 8 NDVI than GIMMS3g NDVI and MOD13A3 NDVI, and the consistencies in the northeast of the MP are higher than northwest regions.

scholarly journals Phenological-metric algorithm for mapping soybean in savanna biome in Brazil

2019 ◽  
pp. 1456-1466
Author(s):  
Bernard S. de Oliveira ◽  
Manuel E. Ferreira ◽  
Alexandre C. Coutinho ◽  
Júlio C. D. M. Esquerdo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Census Data ◽  
Pearson Correlation ◽  
Original Data ◽  
Landsat 8 ◽  
Interpolation Filter ◽  
Modis Evi ◽  
Terra Modis ◽  
Global Accuracy

Agricultural expansion in Brazil is still intense for commodities (such soybeans and corn), mostly cultivated over large portions of the Cerrado biome. Therefore, the development and application of techniques based on remote sensing to map crop areas at a regional level, in a dynamic and more precise way is urgently necessary. In this context, the objective of this study is the improvement of techniques for mapping soybean crops in Brazil, through an analysis of the Centro Goiano mesoregion of Goiás state (a core area of Cerrado), using a time series of Enhanced Vegetation Index (EVI) images provided by TERRA/MODIS orbital sensor, in a test period between 2002 and 2010. Despite their proven quality, MODIS EVI images already contain atmospheric interferences inherent to the acquisition process, such as the presence of clouds. Thus, a set of methods to minimize such artifacts was applied to the data of this study. In general, the methodological procedures comprise of (1) the application of the pixel reliability band aiming to remove pixels contaminated by clouds; (2) the use of contaminated pixel estimates (excluded from the time series); (3) application of an interpolation filter to fill the void pixels in each scene, obtaining continuous and smoothed spectral-temporal profiles for each land use classes; and (4) the classification of agricultural areas using a specific algorithm for crops in the Cerrado region of Goiás. The areas reconstituted in the images matched neighboring pixels, maintaining good coherence with the original data. Likewise, areas mapped with soybeans had a high correlation with official IBGE census data, with a global accuracy value of 78%, and Pearson Correlation coefficient of 0.64. The application of this technique to other imagery sensors (such as RapidEye, Landsat 8 and Sentinel 2) is highly encouraged due a better spatial and temporal resolution (when applied together in a temporal image cube), ensuring more efficient crop monitoring in Brazil.

scholarly journals Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

2020 ◽  
Vol 12 (8) ◽  
pp. 1297
Author(s):  
Roberto Filgueiras ◽  
Everardo Chartuni Mantovani ◽  
Elpídio Inácio Fernandes-Filho ◽  
Fernando França da Cunha ◽  
Daniel Althoff ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Spatial Resolution ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Landsat 8 ◽  
Modis Ndvi

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

scholarly journals Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kim-Anh Nguyen ◽  
Yuei-An Liou ◽  
Ha-Phuong Tran ◽  
Phi-Phung Hoang ◽  
Thanh-Hung Nguyen
Keyword(s):  
Soil Salinity ◽  
Vegetation Index ◽  
Near Infrared ◽  
Mekong Delta ◽  
The Other ◽  
Landsat 8 ◽  
Salinity Intrusion ◽  
Landsat 8 Oli ◽  
Environmental Indices ◽  
Salinity Index

AbstractSalinity intrusion is a pressing issue in the coastal areas worldwide. It affects the natural environment and causes massive economic loss due to its impacts on the agricultural productivity and food safety. Here, we assessed the salinity intrusion in the Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI image was utilized to derive indices for soil salinity estimate including the single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Statistical analysis between the electrical conductivity (EC1:5, dS/m) and the environmental indices derived from Landsat 8 OLI image was performed. Results indicated that spectral values of near-infrared (NIR) band and VSSI were better correlated with EC1:5 (r2 = 0.8 and r2 = 0.7, respectively) than the other indices. Comparative results show that soil salinity derived from Landsat 8 was consistent with in situ data with coefficient of determination, R2 = 0.89 and RMSE = 0.96 dS/m for NIR band and R2 = 0.77 and RMSE = 1.27 dS/m for VSSI index. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by mapping the soil salinity contamination for better selection of accomodating crop types to reduce economical loss in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be potentially useful as a fast-approach to detect the soil salinity in the other regions with low cost and considerable accuracy.

scholarly journals Modelling Soil Carbon Content in South Patagonia and Evaluating Changes According to Climate, Vegetation, Desertification and Grazing

2018 ◽  
Vol 10 (2) ◽  
pp. 438 ◽  
Author(s):  
Pablo Peri ◽  
Yamina Rosas ◽  
Brenton Ladd ◽  
Santiago Toledo ◽  
Romina Lasagno ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Environmental Changes ◽  
Regional Scale ◽  
Monitoring Network ◽  
Stepwise Multiple Regression ◽  
Linear Regression Models ◽  
Erosion Processes ◽  
Precipitation Seasonality ◽  
Southern Patagonia

In Southern Patagonia, a long-term monitoring network has been established to assess bio-indicators as an early warning of environmental changes due to climate change and human activities. Soil organic carbon (SOC) content in rangelands provides a range of important ecosystem services and supports the capacity of the land to sustain plant and animal productivity. The objectives in this study were to model SOC (30 cm) stocks at a regional scale using climatic, topographic and vegetation variables, and to establish a baseline that can be used as an indicator of rangeland condition. For modelling, we used a stepwise multiple regression to identify variables that explain SOC variation at the landscape scale. With the SOC model, we obtained a SOC map for the entire Santa Cruz province, where the variables derived from the multiple linear regression models were integrated into a geographic information system (GIS). SOC stock to 30 cm ranged from 1.38 to 32.63 kg C m−2. The fitted model explained 76.4% of SOC variation using as independent variables isothermality, precipitation seasonality and vegetation cover expressed as a normalized difference vegetation index. The SOC map discriminated in three categories (low, medium, high) determined patterns among environmental and land use variables. For example, SOC decreased with desertification due to erosion processes. The understanding and mapping of SOC in Patagonia contributes as a bridge across main issues such as climate change, desertification and biodiversity conservation.

scholarly journals DECISIONAL TREE MODELS FOR LAND COVER MAPPING AND CHANGE DETECTION BASED ON PHENOLOGICAL BEHAVIORS. APPLICATION CASE: LOCALIZATION OF NON-FULLY-EXPLOITED AGRICULTURAL SURFACES IN THE EASTERN PART OF THE HAOUZ PLAIN IN THE SEMI-ARID CENTRAL MOROCCO

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 365-373
Author(s):  
B. Sebbar ◽  
A. Moumni ◽  
A. Lahrouni
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Arid Zone ◽  
Smallholder Farmers ◽  
Regional Scale ◽  
Land Cover Mapping ◽  
Landsat 8 ◽  
Great Effort ◽  
Tree Classifier ◽  
Semi Arid

Abstract. Great effort has been recently employed for the development of a modern and competitive agriculture in Morocco, growth in the agricultural sector is determined largely through the realization of thousands of new projects, and the support of the smallholder farmers at a national scale. Modernization of irrigation systems, and enlargement of the extent and spatial distribution of irrigated areas holds the key to increase annual productions. In this context, we established a unique procedure for monitoring the agricultural surfaces not fully exploited in terms of potential and production, in the semi-arid zone of the Haouz plain, central Morocco. We derived Normalized Difference Vegetation Index (NDVI) time series from Sentinel-2 (S2) and Landsat 8 (L8) high spatial resolution satellite images from 2016 to 2018. Seasonal phenological changes and land-cover dynamics, in addition to elevation models and landscape slopes, helped determine periods and thresholds suitable for classes separability, and establish a set of rules to be implemented in a Decision tree classifier model for a detailed land-cover mapping of the last three years. The agricultural zone was successfully separated from mountains and hills, and the derived maps of the three years yielded satisfying result with an OAthat reached above 91% for quite detailed landscape-type information. The outputs of this work hold promise to provide valuable information for planners, decision-makers and regional offices, to help smallholder farmers. Although this approach has been developed at regional-scale, it holds the potential to be adapted to larger scales, with the appropriate selection of land-cover types, and carful adjustment in the threshold values.

scholarly journals Estimation of Salinity Intrusion by Using Landsat 8 OLI Data in The Mekong Delta, Vietnam

2018 ◽  
Author(s):  
Phuong Ha Tran ◽  
Anh Kim Nguyen ◽  
Yuei-An Liou ◽  
Phung Phi Hoang ◽  
Hung Thanh Nguyen
Keyword(s):  
Climate Change ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Mekong Delta ◽  
The Other ◽  
Landsat 8 ◽  
Agricultural Activities ◽  
Salinity Intrusion ◽  
Landsat 8 Oli ◽  
Salinity Index

Salinity intrusion is one of the most serious consequences of climate change coupled with rising sea level that significantly affects agricultural activities in many parts of the world. This phenomenon has increasingly become more serious and frequently occurred in the Mekong Delta of Vietnam. As a result, Vietnam has been ranked among top five countries where have been devastatingly impacted by climate change, in particular, its Tra Vinh Province characterized by coastal plain and alluvial deposit. In addition, this area is of the tropical monsoon zone of long rainy season with source of salt brought from the sea by the tides and sea level rise. Regions that are contaminated by salt are located in lowland and often suffer from floods linking to tidal effects with salty water from river systems and channels. Soil salinity evaluation is critical for coastal protection, restoration, and agricultural planning since it can be considered as an agricultural indicator to evaluate quality of soil. Here, we attempt to estimate the soil salinity in Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI images are utilized to derive indices for soil salinity evaluation including single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Subsequently, satistical analysis between soil salinity, electrical conductivity (EC, dS/m), and environmental indices derived from Landsat 8 OLI image is performed. Results indicate that spectral value of Near Infrared (NIR) band and VSSI are highly correlated with EC (R2 = 0.7779 and R2 = 0.6957, respectively) in comparison with the other indices. Comparative results show that soil salinity derived from Landsat 8 is consistent with in situ data. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by providing the base map of soil salinity contamination for better selection of accomodating crop types to reduce economical lost in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be applied in the other regions.

scholarly journals Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

2018 ◽  
Vol 10 (10) ◽  
pp. 1614 ◽  
Author(s):  
Haishuo Wei ◽  
Juanle Wang ◽  
Kai Cheng ◽  
Ge Li ◽  
Altansukh Ochir ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Feature Space ◽  
Gobi Desert ◽  
Landsat 8 ◽  
Mongolian Plateau ◽  
Size Index ◽  
Modified Soil

The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.

scholarly journals Relationships among phenology, climate and biomass across subtropical forests in Argentina

2018 ◽  
Vol 34 (2) ◽  
pp. 93-107 ◽  
Author(s):  
Cecilia Blundo ◽  
Nestor I. Gasparri ◽  
Agustina Malizia ◽  
Matthew Clark ◽  
Genoveva Gatti ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Environmental Changes ◽  
Structural Characteristics ◽  
Regional Scale ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Relative Importance ◽  
Forest Types ◽  
Subtropical Forests

Abstract:Phenology is a key ecosystem process that reflects climate–vegetation functioning, and is an indicator of global environmental changes. Recently, it has been suggested that land-use change and timber extraction promote differences in forest phenology. We use remote-sensing data to describe regional leaf phenological patterns in combination with field data from 131 plots in old-growth and disturbed forests distributed over subtropical forests of Argentina (54–65°W). We assessed how climate is related to phenological patterns, and analysed how changes in forest structural characteristics such as stock of above-ground biomass relate to the observed phenological signals across the gradient. We found that the first three axes of a principal component analysis explained 85% of the variation in phenological metrics across subtropical forests, ordering plots mainly along indicators of seasonality and productivity. At the regional scale, the relative importance of forest biomass in explaining variation in phenological patterns was about 15%. Climate showed the highest relative importance, with temperature and rainfall explaining Enhanced Vegetation Index metrics related to seasonality and productivity patterns (27% and 47%, respectively). Within forest types, climate explains the major fraction of variation in phenological patterns, suggesting that forest function may be particularly sensitive to climate change. We found that forest biomass contributed to explaining a proportion of leaf phenological variation within three of the five forest types studied, and this may be related to changes in species composition, probably as a result of forest use.

scholarly journals The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 670 ◽  
Author(s):  
Wan Shafrina Wan Mohd Jaafar ◽  
Khairul Nizam Abdul Maulud ◽  
Aisyah Marliza Muhmad Kamarulzaman ◽  
Asif Raihan ◽  
Syarina Md Sah ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Forest Cover ◽  
Environmental Changes ◽  
Rapid Change ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Forest Cover Change

Over the past few decades, there has been a rapid change in forest and land cover, especially in tropical forests due to massive deforestation. The major factor responsible for the changes is to fulfill the growing demand of increasing population through agricultural intensification, rural settlements, and urbanization. Monitoring forest cover and vegetation are essential for detecting regional and global environmental changes. The present study evaluates the influence of deforestation on land surface temperature (LST) in the states of Kedah and Perak, Malaysia, between 1988 and 2017. The trend in forest cover change over the time span of 29 years, was analyzed using Landsat 5 and Landsat 8 satellite images to map the sequence of forest cover change. With the measurement of deforestation and its relationship with LST as an end goal, the Normalized Difference Vegetation Index (NDVI) was used to determine forest health, and the spectral radiance model was used to extract the LST. The findings of the study show that nearly 16% (189,423 ha) of forest cover in Perak and more than 9% (33,391 ha) of forest cover in Kedah have disappeared within these 29 years as a result of anthropogenic activities. The correlation between the LST and NDVI is related to the distribution of forests, where LST is inversely related to NDVI. A strong correlation between LST and NDVI was observed in this study, where the average mean of LST in Kedah (25 °C) is higher than in Perak (22.6 °C). This is also reflected by the decreased NDVI value from 0.6 to 0.5 in 2017 at both states. This demonstrated that a decrease in the vegetation area leads to an increase in the surface temperature. The resultant forest change map would be helpful for forest management in terms of identifying highly vulnerable areas. Moreover, it could help the local government to formulate a land management plan.

scholarly journals Applying Remotely Sensed Environmental Information to Model Mosquito Populations

2021 ◽  
Vol 13 (14) ◽  
pp. 7655
Author(s):  
Maria Kofidou ◽  
Michael de Courcy Williams ◽  
Andreas Nearchou ◽  
Stavroula Veletza ◽  
Alexandra Gemitzi ◽  
...  
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Performance Metrics ◽  
Environmental Changes ◽  
Environmental Parameters ◽  
Remotely Sensed ◽  
Control Measures ◽  
Mosquito Population ◽  
Landsat 8 ◽  
Ann Model

Vector borne diseases have been related to various environmental parameters and environmental changes like climate change, which impact their propagation in time and space. Remote sensing data have been used widely for monitoring environmental conditions and changes. We hypothesized that changes in various environmental parameters may be reflected in changes in mosquito population size, thus impacting the temporal and spatial patterns of vector diseases. The aim of this study is to analyze the effect of environmental variables on mosquito populations using the remotely sensed Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from Landsat 8, along with other factors, such as altitude and water covered areas surrounding the examined locations. Therefore, a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was developed and tested for its ability to predict mosquito populations. The model was applied in NE Greece using mosquito population data from 17 locations where mosquito traps were placed from June to October 2019. All performance metrics indicated a high predictive ability of the model. LST was proved to be the factor with the highest relative importance in the prediction of mosquito populations, whereas the developed model can predict mosquito populations 13 days ahead to allow a substantial window for appropriate control measures.

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