Temporal and Spatial Analysis of Rainfall and Associated Normalized Difference Vegetation Index (NDVI) over the Pune District, India

2017 ◽  
Vol 3 (4) ◽  
pp. 1-6
Author(s):  
Shobha S. Nandargi ◽  
Abhijeet S. Kamble
Keyword(s):  
Spatial Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal And Spatial

scholarly journals Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

scholarly journals Temporal and Spatial changes in Crop Patterns, Use of inputs and Hydrological Alteration in the case of Fogera floodplain, Ethiopia

2021 ◽  
Author(s):  
Mare Desta ◽  
Gete Zeleke ◽  
William. A. Payne ◽  
Wubneh Abebe
Keyword(s):  
Water Flow ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Trends ◽  
Primary Data ◽  
Nitrogen And Phosphorus ◽  
Rice Varieties ◽  
Hydrological Alteration ◽  
Spatial Changes ◽  
Temporal And Spatial

Abstract BauckgroundMore than half of the world's population consumes rice. The area under modern rice varieties has expanded, the use of chemical fertilizers and pesticides has increased in various countries. The hydrology of wetlands are also influenced by its chemical and physical characteristics. Hence, this research focused on temporal and spatial changes in crop patterns, input usage, and hydrological change in Fogera floodplain, with the objectives: a. what are the spatial and temporal trends in crops production pattern? b. What inputs have been used in the past and present to produce rice and other crops? c. What looks like the hydrological alteration of the area? The primary data was gathered through a questionnaire, focus group discussions, interviews, and field observations. Secondary data from Landsat imageries, SWAT input data, water flow, normalized difference vegetation index, and hydrological alteration of the site were collected. To analyze data, tables, graphs, and charts percentage, mean, and correlation were used. ResultNDVI results indicated that rice crop is growing while other variables are decreasing. artificial inputs are currently used but before the introduction of rice were not. Recession farming activities have also diminished wetland. Annual average water flow and rainfall have been trending upward. Flow of water with Nitrogen and Phosphorous has a negative correlation, with Pearson's values -0.069 and -0.072, respectively whereas the value 0.242 indicates that nitrogen and phosphorus have a positive relationship. ConclusionIn conclusion, these extended and intensification of farming practices have an impact on the biodiversity of fauna and flora of the area.

GIS-based automated mapping hazardous exogenous processes in the Tunka depression

2020 ◽  
Vol 957 (3) ◽  
pp. 8-20
Author(s):  
O.V. Bezgodova ◽  
Е.А. Rasputina
Keyword(s):  
Spatial Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Digital Terrain Model ◽  
Model Space ◽  
Slope Aspect ◽  
Rock Falls ◽  
Slope Length ◽  
Space Images ◽  
Automated Mapping

The authors consider automated mapping and spatial analysis of hazardous exogenous processes using a digital elevation model, space images and GIS technologies. A highly detailed map of dangerous exogenous processes within the Tunka depression has been created. At the initial stage, using digital terrain model data, the maps of natural characteristics, which cause the distribution of exogenous processes, were made. This is an elevation map, steepness of slopes and slope aspect maps, normalized difference vegetation index map (NDVI), topographic humidity index and slope length (LS) factor maps. Additional information was obtained from the analysis of space images, landscape conditions and field data. Certain sets and ranges of indicators that characterize the spatial distribution of each class of hazardous exogenous processes were chosen. Seven classes of the most intense hazardous exogenous processes were revealed. The main hazardous exogenous processes in the territory of the Tunka depression were noted on a vector basis using the method of spatial analysis in geographic information systems. Fluvial, slope erosion, and cryogenic-slope classes are most common. Fluvial class of exogenous processes is most dangerous due to mudflows within the valleys of small and temporary watercourses. Slope erosion class is represented by gully erosion, and cryogenic-slope is most often manifested in the form of solifluction. The local danger is represented by screes and rock falls, which are included in the group of gravitational-slope processes.

scholarly journals Temporal and Spatial Changes and Driving Forces of NDVI From 1982 - 2015 in Qinba Mountains, China

2021 ◽  
Author(s):  
Yaru Zhang ◽  
Yi He ◽  
Yanlin Li ◽  
Liping Jia
Keyword(s):  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Slow Growth ◽  
Driving Forces ◽  
Vegetation Coverage ◽  
Factors Affecting ◽  
Spatial Changes ◽  
Change Trend ◽  
Temporal And Spatial

Abstract The spatiotemporal variation and driving force of Normalized Difference Vegetation Index (NDVI) is helpful to regional ecological environment protection and natural resource management. Using the Sen and Mann–Kendall methods, Hurt index, Space transfer matrix and Geodetector, this study investigated the temporal and spatial changes and driving forces of NDVI during 1982 - 2015. The results showed that:（1）For the period 1982 to 2015, the high vegetation coverage was mainly distributed in Qinling Mountains and Daba mountain, while the value of NDVI was low in high altitude area in the west, low altitude in the East and Hanjiang River valley.（2）The change trend of NDVI in Qinba Mountains is mainly to maintain stable and slow growth. And the slow growth changes significantly. NDVI increased slowly mainly in the East and northwest.（3）The future change trend of NDVI in Qinba Mountain is mainly slow growth and stability, which indicates that the ecological construction in Qinba Mountains is good. (4) Through the geographical detector, the main factors affecting NDVI in Qinba Mountains are natural factors mainly including rainfall, soil type and digital elevation model (DEM), while human activities mainly including population density have little influence on NDVI in Qinba Mountains. Natural environment factors and human activities make a great difference on the spatial distribution of NDVI. This study provides a help for the sustainable development of the naturel environment in Qinba Mountains.

Association Analysis of NDVI Changes and Topographic Factors

2013 ◽  
Vol 333-335 ◽  
pp. 1205-1208
Author(s):  
De Li Liu ◽  
Ya Shuang Zhang ◽  
Nan Lin
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Topographic Factors ◽  
Gis Software ◽  
Relationship Of ◽  
The Relationship ◽  
Slope Direction

Based on the TM remote sensing data of the Huadian city in 1991 and 2011 and based on the DEM data,using the normalized difference vegetation index (NDVI) change classification method,to Extraction the elevation,slope,slope direction data and the vegetation index data of the study area.Then using the spatial analysis function of GIS software to overlay the two different period NDVI data and analysis the NDVI change of area and spatial. Using the same method to overlay and analysis the relationship of NDVI data and elevation,slope,slope direction.Research shows that the variation of NDVI in the study area has relationship with the topographic factors change.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

2019 ◽  
Vol 2 (1) ◽  
pp. 11-14
Author(s):  
Wahyu Adi
Keyword(s):  
Coral Reefs ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Interpretation ◽  
Vegetation Coverage ◽  
Small Island ◽  
Shallow Waters ◽  
Small Islands ◽  
Seagrass Beds ◽  
Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha.   Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

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.

ANALISIS SPEKTRAL CITRA SPOT 7 UNTUK IDENTIFIKASI KAWASAN MANGROVE DI PANTAI TIMUR KABUPATEN LAMPUNG SELATAN

2019 ◽  
Vol 3 ◽  
pp. 1213
Author(s):  
Nirmawana Simarmata ◽  
Fitralia Elyza ◽  
Rezalian Vatiady
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index

Konversi hutan manggrove merupakan sumber utama emisi CO dengan jumlah sebesar 1,7 ± 0,6 Pg karbon per tahun. Kegiatan konversi hutan mangrove menjadi lahan tambak melepaskan cadangan karbon ke atmosfir dalam jumlah yang cukup berarti. Ekspansi usaha pertambakan udang di kawasan pesisir Provinsi Lampung semakin meluas dari tahun ke tahun yang berdampak serius pada kondisi hutan mangrove. Kebijakan pembukaan tambak baru telah mengubah bentang hutan mangrove dan akan menimbulkan kerugian sosial yang jauh lebih besar. Menanggapi permasalahan tersebut, Indonesia menjadi salah satu negara yang mengikuti program Reduce Emission from Deforestation and Degradation atau REDD+ dalam melakukan inventarisasi karbon hutan. Indonesia memiliki potensi sumberdaya hutan mangrove yang sangat melimpah. Potensi hutan mangrove Indonesia cukup besar, Indonesia memiliki luas hutan mangrove terbesar di dunia. Salah satunya di Kabupaten Lampung Selatan merupakan kawasan dengan tutupan yang relatif luas di Provinsi Lampung. Karakteristik hutan mangrove dianalisis berdasarkan nilai spektral nya dengan menggunakan indeks vegetasi. Jenis data penginderaan jauh yang digunakan untuk penelitian ini adalah citra SPOT 7. Citra SPOT 7 dianalisis menggunakan Normalized Difference Vegetation Index (NDVI) sehingga diperoleh nilai kehijauan objek mangrove. Nilai indeks vegetasi pada kawasan penelitian mempunyai range antara 0.2 – 0.7. Nilai indeks vegetasi digunakan sebagai parameter untuk memetakan kawasan hutan mangrove di Kabupaten Lampung Selatan.

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