scholarly journals A expansão da cana-de-açúcar na bacia hidrográfica do Rio Brilhante, Mato Grosso do Sul: o uso da técnica de NDVI como instrumento para evidenciar dinâmicas territoriais/ The expansion of the sugarcane in the hydrographic basin from Rio Brilhante, Mato Grosso do Sul state: the use of NDVI technique as an instrument for evidencing territorial dynamics

2016 ◽  
pp. 66-81
Author(s):  
Patricia Silva Ferreira ◽  
Charlei Aparecido da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Research ◽  
Mato Grosso ◽  
Hydrographic Basin ◽  
Palabras Clave ◽  
Del Rio ◽  
Mato Grosso Do Sul

RESUMOAs áreas de cana-de-açúcar vêm conquistando cada vez mais espaço para sua expansão, geralmente à custa da conversão de áreas agrícolas e de pastagem. O presente trabalho teve como objetivo apresentar um roteiro metodológico para identificar e mapear as áreas de cultivo da cana-de-açúcar adotando como unidade de análise a bacia hidrográfica do Rio Brilhante – MS. Foram utilizadas imagens de satélite Landsat para construção dos cenários de 2001 e 2015. Para a classificação da cobertura vegetal foram empregadas técnicas de NDVI. Este estudo indica que o emprego de técnicas e ferramentas de sensoriamento remoto mostrou-se eficiente para identificar as características do cultivo da cana-de-açúcar.Palavras-chave: área plantada; sensoriamento remoto; mudança no uso da terra. RESUMENLas zonas de cultivo de la caña de azúcar vienen conquistando más y más espacio para su expansión, generalmente a expensas de la conversión de tierras de cultivo y pasto. El presente estudio presenta una metodología para identificar y mapear las zonas de cultivo de caña de azúcar, adoptando como unidad de análisis la cuenca del Rio Brilhante en el estado de Mato Grosso do Sul. Utilizamos imágenes de satélite norteamericano TM-Landsat5 y OLI-Landsat8 para construir los escenarios de los años de 2001 y 2015, respectivamente, además, se emplearon técnicas Normalized Difference Vegetation Index (NDVI) para la clasificación de la vegetación. Seguros estamos que el uso de herramientas y técnicas de detección alejada, como el NDVI, cuando trabajadas conjuntamente con la investigación de campo, demostraran ser eficaces para identificar las características del cultivo de la caña de azúcar. Palabras clave: zona cultivada; detección alejada; cambio de uso de la tierra. ABSTRACTThe areas of sugarcane cultivation have expanding, often at the expense of the agricultural and pasture areas. Under this perspective, this manuscript aims to present a methodological guide in order to identify and to map up the areas of sugarcane cultivation, whose unity of analysis was located in the surrounding area of the Rio Brilhante hydrographic basin. We aimed to identify the changes through TM-Landsat5 and OLI-Landsat8 satellite pictures by comparing the scenarios from the years of 2001 and 2015, respectively. The classification of the vegetation was made through the technique of Normalized Difference Vegetation Index (NDVI). Thus, this study points that the use of some techniques and remote sensing tools, like NDVI, when used together with the field research, are extremely efficient for identifying the areas of the sugarcane cultivation..Keywords: cropland; remote sensing; land use change.

scholarly journals Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)

2011 ◽  
Vol 3 (3) ◽  
pp. 157
Author(s):  
Daniel Rodrigues Lira ◽  
Maria do Socorro Bezerra de Araújo ◽  
Everardo Valadares De Sá Barretto Sampaio ◽  
Hewerton Alves da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Digital Processing ◽  
Sparse Vegetation ◽  
New Sensors

O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index

scholarly journals Classification of a burnt area based on spectral images

2018 ◽  
Vol 247 ◽  
pp. 00017
Author(s):  
Anna Szajewska
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Burnt Area ◽  
Forest Protection ◽  
The Earth ◽  
Remote Sensing Techniques ◽  
Spectral Ranges ◽  
Mathematical Operations

The use of remote sensing techniques allows obtaining information about processes that occur on the surface of the Earth. In the aspects of fire protection and forest protection, it is important to know a burnt area which was created as a result of a fire of the soil cover or a total fire. The knowledge of this area is necessary to assess losses. Remote sensing techniques allow obtaining images in various spectral ranges. Remote sensing satellites offer multi-band data. Mathematical operations that operate on values coming from different spectral ranges allow determining various remote sensing indicators. The manuscript presents the possibility of using the NDVI (Normalized Difference Vegetation Index) to classify the burnt area. The NDVI is relatively easy to obtain because it operates in the spectral ranges from 630 up to 915 nm, and is obtainable with one detector only. Thus, it can be obtained without any major problems using unmanned aerial vehicles, regardless of time and cloudiness, as is the case when acquiring satellite images. The manuscript describes experimental research and presents the results.

scholarly journals ANÁLISE DA DINAMICA HIDROGEOMORFOLÓGICA NO RIO MIRANDA, ESTADO DE MATO GROSSO DO SUL POR MEIO DE IMAGENS LANDSAT SENSORES TM E OLI

2020 ◽  
Vol 17 (01) ◽  
pp. 53-69
Author(s):  
Ivo Augusto Lopes Magalhães ◽  
Everton de Carvalho ◽  
Aguinaldo Silva ◽  
Beatriz Lima de Paula
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Landsat Images ◽  
Mato Grosso ◽  
Meandering Channel ◽  
River Geomorphology ◽  
Del Rio ◽  
Mato Grosso Do Sul ◽  
Abandoned Meanders

Atualmente no Brasil, os estudos sobre as dinâmicas hidrogeomorfológicas por meio de dados de sensoriamento remoto ainda são escassos. Em rios com extensos percursos e áreas inóspitas faz jus o uso de técnicas de sensoriamento remoto para análise e monitoramento ambiental. Diante do exposto, o objetivo deste estudo foi analisar as mudanças na geomorfologia fluvial do rio Miranda no estado de Mato Grosso do Sul, MS por meio de séries de imagens multitemporais dos Sensores Tematic Mapper do satélite Landsat – 5 e OLI do Satélite Landsat- 8. A planície de inundação do rio Miranda apresenta aproximadamente 600 m de largura e padrão de canal meandrante com índice de sinuosidade de 2.13. Identificou-se áreas em processo erosivo nas margens côncavas, deposição de sedimentos nas margens convexas e presença de meandros abandonados. O paleocinturão de meandros, abandonos de canais e meandros abandonados foram os fenômenos naturais que ocorreram com maior frequência e mais distinguíveis nas imagens Landsat para o período analisado. Palavras-chave: Geomorfologia fluvial. Geoprocessamento. Recursos hídricos. Imagens de satélite.   ANALYSIS OF HYDROGEOMORPHOLOGICAL DYNAMICS IN RIVER MIRANDA, MATO GROSSO DO SUL STATE BY IMAGE LANDSAT SENSORS TM AND OLI ABSTRACT Currently in Brazil, studies on the hydrogeomorphological dynamics through remote sensing data are still scarce. In rivers with extensive pathways and inhospitable areas the use of remote sensing techniques for analysis and monitoring environmental is justified. The objective of this study was to analyze the changes in the river geomorphology of the Miranda River in the state of Mato Grosso of Sul, MS, using multitemporal images series of the Landsat - 5 satellite and Landsat - 5 satellite OLS sensors The floodplain of the Miranda river is approximately 600 m wide and has a meandering channel pattern with a sinuosity index of 2.13. It was identified areas in erosive process in the concave margins, deposition of sediments in the convex margins and presence of abandoned meanders. The paleoculture of meanders, abandonments of channels and abandoned meanders were the natural phenomena that occurred more frequently and more distinguishable in Landsat images for the analyzed period. Keywords: Fluvial geomorphology. Geoprocessing. Water resources. Satellite images.   ANÁLISIS DE DINÁMICA HIDROGEOMORFOLÓGICA EN EL RÍO MIRANDA, ESTADO DE MATO GROSSO DEL SUR POR MEDIO DE IMÁGENES LANDSAT SENSORES TM Y OLI RESUMEN Actualmente en Brasil, los estudios sobre las dinámicas hidrogeomorfológicas por medio de datos de sensoriamiento remoto todavía son escasos. En ríos con extensos recorridos y áreas inhóspitas, el uso de técnicas de detección remota para análisis y monitoreo ambiental. El objetivo de este estudio fue analizar los cambios en la geomorfología fluvial del río Miranda en el estado del Mato Grosso do Sul, MS por medio de series de imágenes multitemporales de los Sensores Tematic Maper del satélite Landsat - 5 y OLI del Satélite Landsat- 8 La planicie de inundación del río Miranda presenta aproximadamente 600 m de ancho y patrón de canal meandrante con índice de sinuosidad de 2.13. Se identificaron áreas en proceso erosivo en los márgenes cóncavos, deposición de sedimentos en las márgenes convexas y presencia de meandros abandonados. La paleocrelación de meandros, abandonos de canales y meandros abandonados fueron los fenómenos naturales que ocurrieron con mayor frecuencia y más distinguibles en las imágenes Landsat para el período analizado. Palabras-clave: Geomorfología fluvial. Geoprocessamento. Recursos hídricos. Imágenes de satélite.

scholarly journals SENSORIAMENTO REMOTO NA DETECÇÃO E ANÁLISE DE FOCOS DE CALOR NA BACIA HIDROGRÁFICA DO RIO MUNIM

2020 ◽  
Vol 18 (01) ◽  
pp. 373-388
Author(s):  
Rennato Oliveira da Silva ◽  
Rhuan Oliveira da Silva ◽  
Thais de Carvalho Araújo ◽  
Carlos Augusto Alves Cardoso Silva ◽  
Ana Karla da Silva Oliveira ◽  
...  
Keyword(s):  
Public Health ◽  
Remote Sensing ◽  
Temporal Analysis ◽  
Heat Sources ◽  
Soil Monitoring ◽  
Hydrographic Basin ◽  
Palabras Clave ◽  
The One ◽  
Del Rio ◽  
Basin Area

Os incêndios são a causa da devastação de milhares de hectares de ecossistemas do planeta, gerando impactos à saúde pública, prejuízos econômicos e ambientais. O presente estudo tem como objetivo realizar uma análise temporal dos focos de calor detectados em diferentes usos e cobertura da terra mapeados entre 2008 e 2018 na Bacia Hidrográfica do Rio Munim (BHRM), leste Maranhense. Os dados utilizados no trabalho foram obtidos junto à plataforma digital do Instituto Nacional de Pesquisas Espaciais (INPE), sendo provenientes dos satélites TERRA, AQUA, GOES, NOAA, MSG-02 e ERS-2. Constatou-se um total de 74.752 ocorrências de focos de incêndios na área da bacia entre os anos de 2008 a 2018, tendo como destaque o mês de novembro o mais crítico registrando 30,33% do total de focos, seguido pelo mês de outubro com 23,03%, período em que praticamente não existe precipitação na região. Verificou-se que o ano de 2015 foi o que apresentou o maior índice de focos com 14.025 ocorrências e que o ano de 2011 foi o menos expressivo, com 2.116 ocorrências. Palavras-chave: Geotecnologias. Uso do solo. Monitoramento.   REMOTE SENSING IN THE DETECTION AND ANALYSIS OF HEAT FOCUSES IN THE HYDROGRAPHIC BASIN OF RIO MUNIM ABSTRACT  Fires are the cause of the devastation of thousands of hectares of ecosystems on the planet, generating impacts on public health, economic and environmental losses. The present study aims to perform a temporal analysis of the heat sources detected in different uses and land cover mapped between 2008 and 2018 in the hydrographic basin of the Munim River, eastern Maranhense. The data used in the study were obtained from the digital platform of the National Institute for Space Research (INPE). The number of outbreaks recorded came from the TERRA, AQUA, GOES, NOAA, MSG-02 and ERS-2 satellites. A total of 74,752 outbreaks in the basin area were found between the years 2008 to 2018, with November as the most critical month, with 30.33% of total outbreaks, followed by October with 23.03%, a period in which there is practically no rainfall in the region. It was found that the year 2015 was the one with the highest rate of outbreaks with 14,025 occurrences and that the year 2011it was the least expressive, with 2,116 occurrences. Keywords: Geotechnologies. Use of the soil. Monitoring.   SENSOR REMOTO EN LA DETECCIÓN Y ANÁLISIS DE ENFOQUES DE CALOR EN LA CUENCA HIDROGRÁFICA DE RÍO MUNIM RESUMEN  Los incendios son la causa de la devastación de miles de hectáreas de ecosistemas en el planeta, generando impactos en la salud pública, pérdidas económicas y ambientales. El presente estudio tiene como objetivo realizar un análisis temporal de las fuentes de calor detectadas en diferentes usos y la cobertura del suelo mapeada entre 2008 y 2018 en la cuenca hidrográfica del río Munim, este de Maranhense. Los datos utilizados en el estudio se obtuvieron de la plataforma digital del Instituto Nacional de Investigación Espacial (INPE). El número de brotes registrados provino de los satélites TERRA, AQUA, GOES, NOAA, MSG-02 y ERS-2. Se encontraron un total de 74,752 brotes en el área de la cuenca entre los años 2008 hasta 2018, con noviembre como el mes más crítico, con 30.33% de los brotes totales, seguido de octubre con 23.03%, un período en el que prácticamente no hay precipitaciones en la región. Se encontró que el año 2015 fue el que tuvo la mayor tasa de brotes con 14.025 ocurrencias y que el año 2011fue el menos expresivo, con 2,116 ocurrencias.  Palabras-clave: Geotecnologías. Uso del suelo. Monitoreo. 

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.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Remote Sensing for Plant Water Content Monitoring: A Review

2021 ◽  
Vol 13 (11) ◽  
pp. 2088
Author(s):  
Carlos Quemada ◽  
José M. Pérez-Escudero ◽  
Ramón Gonzalo ◽  
Iñigo Ederra ◽  
Luis G. Santesteban ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Wave ◽  
Technological Development ◽  
Water Status ◽  
Irrigation Management ◽  
Coefficient Of Determination ◽  
Plant Water ◽  
Monitoring Methods

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

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