Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region

2011 ◽  
Vol 13 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Meng Qi Hu ◽  
Fei Mao ◽  
Han Sun ◽  
Ying Yu Hou
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Source Region ◽  
Climate Factors ◽  
Index Variation

scholarly journals Spatial and Temporal Changes in the Normalized Difference Vegetation Index and Their Driving Factors in the Desert/Grassland Biome Transition Zone of the Sahel Region of Africa

2020 ◽  
Vol 12 (24) ◽  
pp. 4119
Author(s):  
Shupu Wu ◽  
Xin Gao ◽  
Jiaqiang Lei ◽  
Na Zhou ◽  
Yongdong Wang
Keyword(s):  
Climate Change ◽  
Transition Zone ◽  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climate Factors ◽  
Desert Grassland ◽  
Temperature And Precipitation ◽  
Grassland Biome ◽  
Sahel Region

The ecological system of the desert/grassland biome transition zone is fragile and extremely sensitive to climate change and human activities. Analyzing the relationships between vegetation, climate factors (precipitation and temperature), and human activities in this zone can inform us about vegetation succession rules and driving mechanisms. Here, we used Landsat series images to study changes in the normalized difference vegetation index (NDVI) over this zone in the Sahel region of Africa. We selected 6315 sampling points for machine-learning training, across four types: desert, desert/grassland biome transition zone, grassland, and water bodies. We then extracted the range of the desert/grassland biome transition zone using the random forest method. We used Global Inventory Monitoring and Modelling Studies (GIMMS) data and the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5) meteorological assimilation data to explore the spatiotemporal characteristics of NDVI and climatic factors (temperature and precipitation). We used the multiple regression residual method to analyze the contributions of human activities and climate change to NDVI. The cellular automation (CA)-Markov model was used to predict the spatial position of the desert/grassland biome transition zone. From 1982 to 2015, the NDVI and temperature increased; no distinct trend was found for precipitation. The climate change and NDVI change trends both showed spatial stratified heterogeneity. Temperature and precipitation had a significant impact on NDVI in the desert/grassland biome transition zone; precipitation and NDVI were positively correlated, and temperature and NDVI were negatively correlated. Both human activities and climate factors influenced vegetation changes. The contribution rates of human activities and climate factors to the increase in vegetation were 97.7% and 48.1%, respectively. Human activities and climate factors together contributed 47.5% to this increase. The CA-Markov model predicted that the area of the desert/grassland biome transition zone in the Sahel region will expand northward and southward in the next 30 years.

scholarly journals Quantifying the Relative Contribution of the Climate Change and Human Activity on Runoff in the Choshui River Alluvial Fan, Taiwan

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 825
Author(s):  
Hsin-Yu Chen ◽  
Chia-Chi Huang ◽  
Hsin-Fu Yeh
Keyword(s):  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hydrological Cycle ◽  
Alluvial Fan ◽  
Climate Factors ◽  
Simple Method ◽  
Important Indicator ◽  
Runoff Changes ◽  
The Impact

Climate factors and human activities are the leading causes of changes in the hydrological cycle. In addition to being an important part of the hydrological cycle, runoff is also an important indicator for assessing the amount of available water. Therefore, it is necessary to explore the reasons that have caused changes in runoff. In this study, the causes of runoff changes in the alluvial fan of the Choshui River from 1980–2018 are explored. Two simple methods, including a decomposition method based on the Budyko structure and a method based on climate elasticity, for which the necessary data are easy to obtain, are used to quantify the impact of climate factors and human activities on runoff changes. The results show that the runoff in the long term shows a significant transition point in 2003, where climate factors have contributed more than 90% of the change, while the influence of human activities on the changes in runoff appears to be relatively small. Moreover, the Budyko method and the Normalized Difference Vegetation Index (NDVI) show that the vegetation cover has decreased. In addition to providing a simple method to assess the causes of changes in runoff, this study also analyzes the causes of changes in the runoff of the alluvial fan of the Choshui River to provide a reference for water resource policy and land use management.

Impact of the Chamoli disaster on flood Plain and water quality along the Himalayan rivers

2021 ◽  
Author(s):  
Sansar Meena ◽  
Akshansha Chauhan ◽  
Kushanav Bhuyan ◽  
Ramesh P. Singh
Keyword(s):  
Water Quality ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Source Region ◽  
Surface Characteristics ◽  
Flood Plain ◽  
Radar Data ◽  
Himalayan Rivers ◽  
The Impact

<p>The Himalayan rivers are glacier-fed and are vulnerable to devastating flash floods caused by damming of landslides and outbreak of glacial lakes. On 7 February 2021, around 10:30 am IST, a huge block of glacier mass broke from the Nanda Ghunti glacier. It is evident from the multi-temporal satellite imageries from Planet Scope that snow dust deposited in the affected area. During the course of the event, a huge amount of debris along with broken glacial fragments flooded the Rishi Ganga river and washed away the Hydropower plants; Rishi Ganga and Tapovan, more than 71 people were killed, and about 100 people are still missing. Detailed analysis of optical and radar data has been carried out to show the impact of the rockslide, changes in the surface characteristics of the source region, flood plains of the river and water quality of the Himalayan rivers (Alaknanda and Ganga). We have used five different indices Modified Normalized difference water index (MNDWI), Normalized difference vegetation index (NDVI), Enhanced vegetation index (EVI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), that show pronounced changes in water quality and flood plain at the four different sections of the river. The spectral reflectance and backscattering coefficients derived from high-resolution Planet scope and Sentinel 1 SAR data show characteristics behaviour of the flood plain and water quality. Further, we have also found changes in the water quality of several canals after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal.</p>

scholarly journals Impact of Environmental and Climate Factors on Spatial Distribution of Cutaneous Leishmaniasis in Northeastern Iran: Utilizing Remote Sensing

2020 ◽  
Author(s):  
Mohammad Reza Shirzadi ◽  
Mohammad Javanbakht ◽  
Hassan Vatandoost ◽  
Nahid Jesri ◽  
Abedin Saghafipour ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Soil Moisture ◽  
Cutaneous Leishmaniasis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Specific Humidity ◽  
Climate Factors ◽  
Northern Regions ◽  
Remote Sensing Techniques

Background: Cutaneous leishmaniasis (CL) is a dermal and parasitic disease.. The aim of this study was to determine the effect of environmental and climate factors on spatial distribution of CL in northeastern Iran by utilizing remote sensing from 20 March 2016 to 19 March 2017. Methods: In this ecological study, the data were divided into two parts: The descriptive data on human CL cases were gathered from Communicable Diseases center of Iran. The remote sensing techniques and satellite imagery data (TRMM, MODIS-Aqua, MODIS-Terra and AMSR-2 with spatial resolution 0.25°, 0.05°, 5600m and 10km) of environ­mental and climate factors were used to determine the spatial pattern changes of cutaneous leishmaniasis inci­dence. Results: The incidence of CL in North Khorasan, Razavi Khorasan, and South Khorasan was 35.80 per 100,000 people (309/863092), 34.14 per 100,000 people (2197/6,434,501) and 7.67 per 100,000 people (59/768,898), respectively. The incidence of CL had the highest correlation with soil moisture and evapotranspiration. Moreover, the incidence of dis­ease was significantly correlated with Normalized Difference Vegetation Index (NDVI) and air humidity while it had the lowest correlation with rainfall. Furthermore, the CL incidence had an indirect correlation relation with the air tem­perature meaning that with an increase in the temperature, the incidence of disease decreased. Conclusion: As such, the incidence of disease was also higher in the northern regions; most areas of North Khorasan and northern regions of Razavi Khorasan; where the rainfall, vegetation, specific humidity, evapotranspiration, and soil moisture was higher than the southern areas.

scholarly journals The Relationship between NDVI and Climate Factors at Different Monthly Time Scales: A Case Study of Grasslands in Inner Mongolia, China (1982–2015)

2019 ◽  
Vol 11 (24) ◽  
pp. 7243 ◽  
Author(s):  
Zhifang Pei ◽  
Shibo Fang ◽  
Wunian Yang ◽  
Lei Wang ◽  
Mingyan Wu ◽  
...  
Keyword(s):  
Time Scales ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Growing Season ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Climate Factors ◽  
The Relationship

There are currently only two methods (the within-growing season method and the inter-growing season method) used to analyse the normalized difference vegetation index (NDVI)–climate relationship at the monthly time scale. What are the differences between the two methods, and why do they exist? Which method is more suitable for the analysis of the relationship between them? In this study, after obtaining NDVI values (GIMMS NDVI3g) near meteorological stations and meteorological data of Inner Mongolian grasslands from 1982 to 2015, we analysed temporal changes in NDVI and climate factors, and explored the difference in Pearson correlation coefficients (R) between them via the above two analysis methods and analysed the change in R between them at multiple time scales. The research results indicated that: (1) NDVI was affected by temperature and precipitation in the area, showing periodic changes, (2) NDVI had a high value of R with climate factors in the within-growing season, while the significant correlation between them was different in different months in the inter-growing season, (3) with the increase in time series, the value of R between NDVI and climate factors showed a trend of increase in the within-growing season, while the value of R between NDVI and precipitation decreased, but then tended toward stability in the inter-growing season, and (4) when exploring the NDVI–climate relationship, we should first analyse the types of climate in the region to avoid the impacts of rain and heat occurring during the same period, and the inter-growing season method is more suitable for the analysis of the relationship between them.

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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