scholarly journals Observations for Snow Cover Detection in Akita via the Combination of Normalized Difference Vegetation Index and Normalized Difference Snow Index

2014 ◽  
Vol 12 (ists29) ◽  
pp. Pn_1-Pn_7
Author(s):  
Paipai PAN ◽  
Kazuki SARUTA ◽  
Yuki TERATA ◽  
Guoyue CHEN
Keyword(s):  
Snow Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Normalized Difference Snow Index

scholarly journals Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China

2022 ◽  
Vol 14 (2) ◽  
pp. 262
Author(s):  
Hui Guo ◽  
Xiaoyan Wang ◽  
Zecheng Guo ◽  
Siyong Chen
Keyword(s):  
Snow Cover ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Cycle ◽  
Water Source ◽  
Driving Factors ◽  
Change Rate ◽  
Mean Temperature ◽  
Forested Areas

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.

scholarly journals MODIS NDVI Multi-Temporal Analysis Confirms Farmer Perceptions on Seasonality Variations Affecting Apple Orchards in Kinnaur, Himachal Pradesh

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 724
Author(s):  
Himangana Gupta ◽  
Lakhvinder Kaur ◽  
Mahbooba Asra ◽  
Ram Avtar ◽  
C. Sudhakar Reddy
Keyword(s):  
Seasonal Variations ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Analysis ◽  
Early Warning Systems ◽  
Himachal Pradesh ◽  
Modis Ndvi ◽  
Farmer Perceptions ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Normalized Difference Snow Index

Apple cultivation in the Kinnaur district of the northern Indian State of Himachal Pradesh faces challenges from climatic changes and developmental activities. Farmers in the neighboring districts have already faced a major loss of livelihood due to seasonal changes. Therefore, it is important to study the extent of seasonal variations in the apple growing locations of this region. This study makes that attempt by assessing seasonality variations during a 15-year period from 2004 to 2018 when maximum construction activities occurred in this region. The study uses geospatial and statistical techniques in addition to farmer perceptions obtained during a field visit in November 2019. A temporal pattern using a normalized difference vegetation index (NDVI) based on Moderate Resolution Imaging Spectroradiometer (MODIS) was studied for seven apple-growing locations in the district. The results show high seasonal variations and reduced snowfall at lower elevations, resulting in less chilling hours, which are necessary for the healthy growth of apples. The normalized difference snow index (NDSI) and rainfall show a high correlation with apple growth. Local farmers are unprepared for future seasonal disturbances, as they lack early warning systems, insurance for apple crops, and alternative livelihood options.

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.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

Developing land-state maps of Tram Chim National Park in Vietnam based on monitoring results

2020 ◽  
Vol 963 (9) ◽  
pp. 53-64
Author(s):  
V.F. Kovyazin ◽  
Thi Lan Anh Dang ◽  
Viet Hung Dang
Keyword(s):  
National Park ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Likelihood Method ◽  
Wetland Area ◽  
Water Index ◽  
Effi Ciency ◽  
Normalized Difference Water Index ◽  
Specially Protected Natural Areas ◽  
Protected Natural Areas

Tram Chim National Park in Southern Vietnam is a wetland area included in the system of specially protected natural areas (SPNA). For the purposes of land monitoring, we studied Landsat-5 and Sentinel-2B images obtained in 1991, 2006 and 2019. The methods of normalized difference vegetation index (NDVI) and water objects – normalized difference water index (NDWI) were used to estimate the vegetation in National Park. The allocated land is classifi ed by the maximum likelihood method in ENVI 5.3 into categories. For each image, a statistical analysis of the land after classifi cation was performed. Between 1991 and 2019, land changes occurred in about 57 % of the Tram Chim National Park total area. As a result, the wetland area has signifi cantly reduced there due to climate change. However, the area of Melaleuca forests in Tram Chim National Park has increased due to the effi ciency of reforestation in protected areas. Melaleuca forests are also being restored.

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