Heuristic Approach to Temporal Assignments of Spatial Grid Points for Vegetation Monitoring

2019 ◽  
Vol 10 (3) ◽  
pp. 1-19
Author(s):  
Virginia M. Miori ◽  
Nicolle Clements ◽  
Brian W. Segulin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heuristic Approach ◽  
Spatial Problem ◽  
African Region ◽  
Vegetation Monitoring ◽  
Testing Procedures ◽  
Reward Function ◽  
Grid Points ◽  
False Discoveries

In this research, vegetation trends are studied to give valuable information toward effective land use in the East African region, based on the normalized difference vegetation index (NDVI). Previously, testing procedures controlling the rate of false discoveries were used to detect areas with significant changes based on square regions of land. This article improves the assignment of grid points (pixels) to regions by formulating the spatial problem as a multidimensional temporal assignment problem. Lagrangian relaxation is applied to the problem allowing reformulation as a dynamic programming problem. A recursive heuristic approach with a penalty/reward function for pixel reassignment is proposed. This combined methodology not only controls an overall measure of combined directional false discoveries and nondirectional false discoveries, but make them as powerful as possible by adequately capturing spatial dependency present in the data. A larger number of regions are detected, while maintaining control of the mdFDR under certain assumptions.

scholarly journals Heuristic Approach to Multidimensional Temporal Assignment of Spatial Grid Points for Effective Vegetation Monitoring and Land Use in East Africa

2018 ◽  
Author(s):  
Virginia M. Miori ◽  
Nicolle Clements ◽  
Brian W. Segulin
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Heuristic Approach ◽  
Spatial Problem ◽  
African Region ◽  
Vegetation Monitoring ◽  
Testing Procedures ◽  
Grid Points ◽  
False Discoveries

Abstract. In this research, vegetation trends are studied to give valuable information toward effective land use in the East African region, based on the Normalized Difference Vegetation Index (NDVI). Previously, testing procedures controlling the rate of false discoveries were used to detect areas with significant changes based on square regions of land. This paper improves the assignment of grid points (pixels) to regions by formulating the spatial problem as a multidimensional temporal assignment problem. Lagrangian relaxation is applied to the problem allowing reformulation as a dynamic programming problem. A recursive heuristic approach with a penalty/reward function for pixel reassignment is proposed. This combined methodology not only controls an overall measure of combined directional false discoveries and nondirectional false discoveries, but make them as powerful as possible by adequately capturing spatial dependency present in the data. A larger number of regions are detected, while maintaining control of the mdFDR under certain assumptions. Data Link: https://figshare.com/s/ed0ba3a1b24c3cb31ebf DOI: https://figshare.com/articles/NDVI_and_Statistical_Data_for_Generating_Homogeneous_Land_Use_Recommendations/5897581

scholarly journals Normalized Difference Vegetation Index in the Integration of Conservation Education

2018 ◽  
Vol 3 (1) ◽  
pp. 47 ◽  
Author(s):  
Ali Rahmat ◽  
Mustofa Abi Hamid ◽  
Muhammad Khoiru Zaki ◽  
Abdul Mutolib
Keyword(s):  
Developing Countries ◽  
Satellite Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Degradation ◽  
Conservation Education ◽  
Global Environment ◽  
Vegetation Monitoring ◽  
June July ◽  
Status Data

Forest plays an important role to support a global environment. Currently, forest degradation occurs in developing countries. Therefore, the excellent strategies to against the forest degradation must be found. One of the best solutions is understanding the information of vegetation condition. Here, the objective of this paper was to apply a method as the assessment of vegetation monitoring using satellite data in the integration of conservation education forest at great forest Wan Abdul Rachman in Lampung Province, Indonesia. In this study, normalized difference vegetation index (NDVI) was used, completed with satellite data (namely MODIS). This technique helps in monitoring vegetation status. Data NDVI from MODIS satellite data showed that forest area decrease very small from 2000-2017. The data was obtained for June, July, and the end of September.

scholarly journals EXPLOITATION OF SENTINEL-2 IMAGES FOR LONG-TERM VEGETATION MONITORING AT A FORMER ORE PROCESSING SITE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1533-1537
Author(s):  
S. Fabre ◽  
A. Elger ◽  
T. Riviere
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Environmental Changes ◽  
Biomass Accumulation ◽  
Growth Patterns ◽  
Absolute Difference ◽  
Vegetation Monitoring ◽  
Ore Processing ◽  
Processing Site ◽  
Sentinel 2

Abstract. Excess metals in the soil or in plant tissues tend to have negative effects on plant health, growth, and biomass accumulation. The search for stressed or unusual growth patterns in cover vegetation has been enhanced by the use of vegetation index in the context of excessive exposure to heavy metals in the soil. This study aims to improve the monitoring of phyto-stabilized and natural vegetation of an ore processing site for several years after its closure by using multiple Sentinel-2 images. The time series is made up of 13 images, one image per season for four years. NDVI (Normalized Difference Vegetation Index), the most widely known and used vegetation index in the scientific literature, is used in combination with other spectral indexes identifying built-up areas and bare soils in order to enhance vegetation. A change detection technique based on absolute difference of vegetation maps is applied to detect abrupt changes related to meteorological conditions and significant environmental changes.

scholarly journals FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING

2018 ◽  
Vol XLII-1 ◽  
pp. 155-160 ◽  
Author(s):  
M. Gašparović ◽  
D. Medak ◽  
I. Pilaš ◽  
L. Jurjević ◽  
I. Balenović
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Land Cover Classification ◽  
Vegetation Monitoring ◽  
Sentinel 2

<p><strong>Abstract.</strong> Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.</p>

scholarly journals Synergy between Low Earth Orbit (LEO)—MODIS and Geostationary Earth Orbit (GEO)—GOES Sensors for Sargassum Monitoring in the Atlantic Ocean

2021 ◽  
Vol 13 (8) ◽  
pp. 1444
Author(s):  
Audrey Minghelli ◽  
Cristele Chevalier ◽  
Jacques Descloitres ◽  
Léo Berline ◽  
Philippe Blanc ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ocean Color ◽  
Surface Wind ◽  
Circulation Model ◽  
Low Earth Orbit ◽  
Geostationary Satellite ◽  
Earth Orbit ◽  
Vegetation Monitoring ◽  
Satellite Sensors

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil, and West Africa, leading to heavy environmental and socio-economic impacts. Ocean color remote sensing observations as performed by sun-synchronous satellite sensors such as MODIS (NASA), MERIS (ESA), or OLCI (ESA/Copernicus) are used to provide quantitative assessments of Sargassum coverage through the calculation of indices as the Alternative Floating Algae Index (AFAI). Sun-synchronous sensors usually provide at best one daytime observation per day of a given oceanic area. However, such a daily temporal revisit rate is not fully satisfactory to monitor the dynamics of Sargassum aggregation due to their potentially significant drift over the course of the day as a result of oceanic currents and sea surface wind stress. In addition, the sun glint and the presence of clouds limit the use of low earth orbit observations, especially in tropical zones. The high frequency sampling provided by geostationary sensors can be a relevant alternative approach in synergy with ocean color sun-synchronous sensors to increase the temporal resolution of the observations, thus allowing efficient monitoring of Sargassum dynamics. In this study, data acquired by a geostationary satellite sensor located at 36,000 km from Earth, namely GOES-16 (NASA/NOAA), which was primarily designed for meteorology applications, are analyzed to investigate the Sargassum dynamics. The results demonstrate that a GOES-16 hourly composite product is appropriate to identify Sargassum aggregations using an index commonly used for vegetation monitoring, namely NDVI (Normalized Difference Vegetation Index). It is also shown that GOES hourly observations can significantly improve the simulated drift obtained with a transport circulation model, which uses geostrophic current, wind, and waves. This study thus highlights the significant relevance of the effective synergy between sun-synchronous and geostationary satellite sensors for characterizing the Sargassum dynamics. Such a synergy could be summarized as follows: (i) A sun-synchronous sensor enables accurate Sargassum detection and quantitative estimates (e.g., fractional coverage) through AFAI Level-2 products while (ii) a geostationary sensor enables the determination of the displacement features of Sargassum aggregations (velocity, direction).

scholarly journals Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

2019 ◽  
Vol 11 (10) ◽  
pp. 1192 ◽  
Author(s):  
Nianxu Xu ◽  
Jia Tian ◽  
Qingjiu Tian ◽  
Kaijian Xu ◽  
Shaofei Tang
Keyword(s):  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Bare Soil ◽  
Vegetation Canopy ◽  
Vegetation Monitoring ◽  
Red Edge ◽  
Positive Correlations

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

scholarly journals ANÁLISE DOS EFEITOS DO DÉFICIT HÍDRICO NA RESPOSTA TEMPORAL DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NO ESTADO DO AMAZONAS

2016 ◽  
Vol 38 (2) ◽  
pp. 1064
Author(s):  
Célia Maria Paiva ◽  
Alice Da Silva Gonçalves de Jesus
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Vegetation Monitoring ◽  
Annual Cycles ◽  
Annual Behavior ◽  
Weather Stations ◽  
Water Surplus ◽  
Orbital Data

Vegetation indices derived by remote sensing can help identify occurrence of drought on a regional scale. The Normalized Difference Vegetation Index (NDVI) has been widely used in vegetation monitoring, with promising results. This study aims to identify the patterns of temporal response of NDVI in relation to occurrence of deficit/surplus water to different regions of the Amazon biome, as well as understand its seasonal and inter-annual cycles. Therefore, data from five weather stations of the National Institute of Meteorology (INMET) and a set of orbital data of  EFAI-NDVI were used. The study period covers the years 1982 to 1990. The results indicate that the response of NDVI to the occurrence of drought is one month in all regions. In the case of water surplus, that response varies from one to four months. Seasonally, the highest NDVI values occur after the rainy season in the region and the lowest values occurring after the dry season. On inter-annual behavior, the NDVI decreases in El Niño years and rises in La Niña years.

scholarly journals Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa

2021 ◽  
Vol 3 ◽  
Author(s):  
Shahriar Pervez ◽  
Amy McNally ◽  
Kristi Arsenault ◽  
Michael Budde ◽  
James Rowland
Keyword(s):  
Food Security ◽  
East Africa ◽  
Random Error ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Surface Models ◽  
Vegetation Monitoring ◽  
Vegetation Fraction ◽  
Surface Models

The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration (ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.

scholarly journals Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors

2020 ◽  
Vol 12 (16) ◽  
pp. 2542
Author(s):  
Han Lu ◽  
Tianxing Fan ◽  
Prakash Ghimire ◽  
Lei Deng
Keyword(s):  
Precision Agriculture ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Change Analysis ◽  
Vegetation Monitoring ◽  
Ground Point ◽  
Highly Correlated

In recent years, the use of unmanned aerial vehicles (UAVs) has received increasing attention in remote sensing, vegetation monitoring, vegetation index (VI) mapping, precision agriculture, etc. It has many advantages, such as high spatial resolution, instant information acquisition, convenient operation, high maneuverability, freedom from cloud interference, and low cost. Nowadays, different types of UAV-based multispectral minisensors are used to obtain either surface reflectance or digital number (DN) values. Both the reflectance and DN values can be used to calculate VIs. The consistency and accuracy of spectral data and VIs obtained from these sensors have important application value. In this research, we analyzed the earth observation capabilities of the Parrot Sequoia (Sequoia) and DJI Phantom 4 Multispectral (P4M) sensors using different combinations of correlation coefficients and accuracy assessments. The research method was mainly focused on three aspects: (1) consistency of spectral values, (2) consistency of VI products, and (3) accuracy of normalized difference vegetation index (NDVI). UAV images in different resolutions were collected using these sensors, and ground points with reflectance values were recorded using an Analytical Spectral Devices handheld spectroradiometer (ASD). The average spectral values and VIs of those sensors were compared using different regions of interest (ROIs). Similarly, the NDVI products of those sensors were compared with ground point NDVI (ASD-NDVI). The results show that Sequoia and P4M are highly correlated in the green, red, red edge, and near-infrared bands (correlation coefficient (R2) > 0.90). The results also show that Sequoia and P4M are highly correlated in different VIs; among them, NDVI has the highest correlation (R2 > 0.98). In comparison with ground point NDVI (ASD-NDVI), the NDVI products obtained by both of these sensors have good accuracy (Sequoia: root-mean-square error (RMSE) < 0.07; P4M: RMSE < 0.09). This shows that the performance of different sensors can be evaluated from the consistency of spectral values, consistency of VI products, and accuracy of VIs. It is also shown that different UAV multispectral minisensors can have similar performances even though they have different spectral response functions. The findings of this study could be a good framework for analyzing the interoperability of different sensors for vegetation change analysis.

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