scholarly journals Remote sensing of seasonal climatic constraints on leaf phenology across pantropical evergreen forest biome

2021 ◽  
Author(s):  
Qian Li ◽  
Xiuzhi Chen ◽  
Wenping Yuan ◽  
Haibo Lu ◽  
Ruoque Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Phenology ◽  
Evergreen Forest ◽  
Climatic Constraints ◽  
Forest Biome

scholarly journals MANEJO, QUALIDADE E DINÂMICA DA DEGRADAÇÃO DE PASTAGENS NA MATA ATLÂNTICA DE MINAS GERAIS - BRASIL

Nativa ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 370 ◽  
Author(s):  
Luís Flávio Pereira ◽  
Cecilia Fátima Carlos Ferreira ◽  
Ricardo Morato Fiúza Guimarães
Keyword(s):  
Remote Sensing ◽  
Atlantic Forest ◽  
Quality Index ◽  
Minas Gerais ◽  
Google Earth ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Mata Atlântica ◽  
Mata Atlantica ◽  
Forest Biome

Pastagens sob práticas de manejo ineficientes tornam-se degradadas, provocando sérios problemas socioambientais e econômicos. Assim, entender a dinâmica dos sistemas pastoris e suas interações com o meio físico torna-se essencial na busca de alternativas sustentáveis para a agropecuária. Estudou-se manejo, dinâmica anual e interações socioambientais em pastagens de uma bacia hidrográfica no bioma Mata Atlântica em Minas Gerais, Brasil, durante o ano hidrológico 2016/2017. Utilizou-se dados de campo, relatos de agricultores e sensoriamento remoto via imagens LANDSAT 8 OLI e Google Earth Pro®. Foi proposto um índice de qualidade para pastagens da região. As pastagens apresentaram, em média, qualidade moderada. Níveis de degradação foram altos, oscilando de forma quadrática (níveis 2, 4, 5 e IDP) e potencial (nível 1) com a precipitação (p < 0,01), o que sugere que a irrigação possa ser prática eficiente no controle da degradação. Durante o ano, pelo menos 51,27% das pastagens apresentaram algum sinal de degradação, atingindo-se a marca de 91,32%, no período seco. Os resultados sugerem pior qualidade e maiores níveis de degradação de pastagens em terras elevadas e declivosas. Devido às condições socioambientais locais, indica-se o uso de sistemas silvipastoris agroecológicos no manejo das pastagens.Palavras-chave: uso da terra, sensoriamento remoto, relação solo paisagem, Zona da Mata, índice de qualidade. MANAGEMENT, QUALITY AND DEGRADATION DYNAMICS OF PASTURES IN ATLANTIC FOREST BIOME, MINAS GERAIS – BRASIL ABSTRACT:Pastures under inefficient management practices get degraded, leading to serious socioeconomic and environmental issues. That being said, understanding the dynamics of such systems and their interaction with the environment is essential when it comes to looking towards sustainable alternatives for livestock activities. The management, annual dynamics and socio-environmental interactions in pastures in an hydrographic basin located in Atlantic Forest biome, Minas Gerais, Brasil, were studied during the hydrological year of 2016/2017. Field data and farmers reports were utilized, such as remote sensing via images from LANDSAT 8 OLI and Google Earth Pro®. A quality index was proposed for the pastures, which usually presented medium quality. Degradation levels were high, oscillating in a quadratic basis (levels 2, 4, 5 and IDP) and potential (level 1) with precipitation (p < 0,01), which suggests that irrigation might be an efficient practice when it comes to degradation control. During the year, at least 51,27% of pastures have presented signs of degradation, achieving 91,32% in dry periods. The results suggest less quality and bigger degradation levels in pastures located in high and steep areas. Considering the local environmental conditions, agroecological silvopasture systems are recommended regarding the pastures management.Keywords: land use, remote sensing, soil/landscape relationships, Zona da Mata, quality index.

scholarly journals Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest

2020 ◽  
Vol 17 (18) ◽  
pp. 4523-4544 ◽  
Author(s):  
Rui Cheng ◽  
Troy S. Magney ◽  
Debsunder Dutta ◽  
David R. Bowling ◽  
Barry A. Logan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Seasonal Variation ◽  
Primary Production ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Evergreen Forest ◽  
Carotenoid Content ◽  
Co2 Uptake ◽  
Evergreen Forests ◽  
Spectral Components

Abstract. Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimation of gross primary production (GPP) from remote sensing indices represents a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, these have mostly been investigated with intermittent field campaigns or with narrow-band spectrometers in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400–900 nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components co-varied with carotenoid content and GPP, supporting the interpretation of the photochemical reflectance index (PRI) and the chlorophyll/carotenoid index (CCI). Although the entire 400–900 nm range showed additional spectral changes near the red edge, it did not provide significant improvements in GPP predictions. We found little seasonal variation in both normalized difference vegetation index (NDVI) and the near-infrared vegetation index (NIRv) in this ecosystem. In addition, we quantitatively determined needle-scale chlorophyll-to-carotenoid ratios as well as anthocyanin contents using full-spectrum inversions, both of which were tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using partial least-squares regression (PLSR) explained approximately 87 % of the variability in observed GPP. Our results linked the seasonal variation in reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400–900 nm associated with GPP seasonality in evergreen forests.

scholarly journals Leaf phenology as one important driver of seasonal changes in isoprene emissions in central Amazonia

2018 ◽  
Vol 15 (13) ◽  
pp. 4019-4032 ◽  
Author(s):  
Eliane G. Alves ◽  
Julio Tóta ◽  
Andrew Turnipseed ◽  
Alex B. Guenther ◽  
José Oscar W. Vega Bustillos ◽  
...  
Keyword(s):  
Leaf Age ◽  
Leaf Phenology ◽  
Evergreen Forest ◽  
Forest Site ◽  
Area Index ◽  
Isoprene Emission ◽  
Evergreen Forests ◽  
Central Amazonia ◽  
Meteorological Observations ◽  
Emission Capacity

Abstract. Isoprene fluxes vary seasonally with changes in environmental factors (e.g., solar radiation and temperature) and biological factors (e.g., leaf phenology). However, our understanding of the seasonal patterns of isoprene fluxes and the associated mechanistic controls is still limited, especially in Amazonian evergreen forests. In this paper, we aim to connect intensive, field-based measurements of canopy isoprene flux over a central Amazonian evergreen forest site with meteorological observations and with tower-mounted camera leaf phenology to improve our understanding of patterns and causes of isoprene flux seasonality. Our results demonstrate that the highest isoprene emissions are observed during the dry and dry-to-wet transition seasons, whereas the lowest emissions were found during the wet-to-dry transition season. Our results also indicate that light and temperature cannot totally explain isoprene flux seasonality. Instead, the camera-derived leaf area index (LAI) of recently mature leaf age class (e.g., leaf ages of 3–5 months) exhibits the highest correlation with observed isoprene flux seasonality (R2=0.59, p<0.05). Attempting to better represent leaf phenology in the Model of Emissions of Gases and Aerosols from Nature (MEGAN 2.1), we improved the leaf age algorithm by utilizing results from the camera-derived leaf phenology that provided LAI categorized into three different leaf ages. The model results show that the observations of age-dependent isoprene emission capacity, in conjunction with camera-derived leaf age demography, significantly improved simulations in terms of seasonal variations in isoprene fluxes (R2=0.52, p<0.05). This study highlights the importance of accounting for differences in isoprene emission capacity across canopy leaf age classes and identifying forest adaptive mechanisms that underlie seasonal variation in isoprene emissions in Amazonia.

scholarly journals QUALIDADE DA PAISAGEM: ESTUDO DE CASO NA FLORESTA OMBRÓFILA MISTA

FLORESTA ◽  
2004 ◽  
Vol 34 (1) ◽  
Author(s):  
Elaine de Cacia De Lima ◽  
Carlos Roberto Sanquetta ◽  
Flávio Felipe Kirchner ◽  
Eliane Regina Ferretti
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Time Series ◽  
Spatial Language ◽  
Quality Changes ◽  
Araucaria Forest ◽  
Forest Coverage ◽  
Landscape Quality ◽  
Forest Biome

A pesquisa desenvolvida em uma propriedade inserida no bioma da Floresta Ombrófila Mista, localizada no município de General Carneiro, no estado do Paraná, objetivou realizar um diagnóstico da qualidade da paisagem ao longo de uma série temporal, com a utilização de geotecnologias como o sensoriamento remoto e o geoprocessamento. O estudo proporcionou a elaboração dos produtos de uso e cobertura do solo referente aos anos de 1952, 1980 e 2000, os quais foram cruzados a partir da programação em LEGAL (linguagem espacial para geoprocessamento algébrico) no software SPRING 3.6, gerando as cartas de qualidade da paisagem de 1952-1980 e 1980-2000. Através de análises constatou-se variações significativas relacionadas à constituição das características elementares que compõem e integram os aspectos da paisagem, caracterizando uma melhoria na qualidade paisagística do recorte espacial. LANDSCAPE QUALITY: A CASE STUDY IN THE ARAUCARIA FOREST Abstract This research was carried out in a property located in the Araucaria Forest Biome, General Carneiro municipality, Paraná State, Brazil. It aims to analyze the landscape quality changes along a time series by using geotechnology, such as remote sensing and geoprocessing. This work provided land use and forest coverage maps respective to 1952, 1980 and 2000, which were integrated through the LEGAL (spatial language for algebraic geoprocessing) programming language on the SPRING 3.6 software. The landscape quality changes in the property for the periods 1952-1980 and 1980-2000 were examined. The analysis showed significant variations related to the landscape quality during the periods of study. It was concluded that the landscape quality changes positively along time in the study site.

scholarly journals Monitoring Mega-Crown Leaf Turnover from Space

2020 ◽  
Vol 12 (3) ◽  
pp. 429
Author(s):  
Emma R. Bush ◽  
Edward T. A. Mitchard ◽  
Thiago S. F. Silva ◽  
Edmond Dimoto ◽  
Pacôme Dimbonda ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Leaf Phenology ◽  
Optical Data ◽  
Spectral Signature ◽  
Earth System ◽  
Leaf Turnover ◽  
The Tropics

Spatial and temporal patterns of tropical leaf renewal are poorly understood and poorly parameterized in modern Earth System Models due to lack of data. Remote sensing has great potential for sampling leaf phenology across tropical landscapes but until now has been impeded by lack of ground-truthing, cloudiness, poor spatial resolution, and the cryptic nature of incremental leaf turnover in many tropical plants. To our knowledge, satellite data have never been used to monitor individual crown leaf phenology in the tropics, an innovation that would be a major breakthrough for individual and species-level ecology and improve climate change predictions for the tropics. In this paper, we assessed whether satellite data can detect leaf turnover for individual trees using ground observations of a candidate tropical tree species, Moabi (Baillonella toxisperma), which has a mega-crown visible from space. We identified and delineated Moabi crowns at Lopé NP, Gabon from satellite imagery using ground coordinates and extracted high spatial and temporal resolution, optical, and synthetic-aperture radar (SAR) timeseries data for each tree. We normalized these data relative to the surrounding forest canopy and combined them with concurrent monthly crown observations of new, mature, and senescent leaves recorded from the ground. We analyzed the relationship between satellite and ground observations using generalized linear mixed models (GLMMs). Ground observations of leaf turnover were significantly correlated with optical indices derived from Sentinel-2 optical data (the normalized difference vegetation index and the green leaf index), but not with SAR data derived from Sentinel-1. We demonstrate, perhaps for the first time, how the leaf phenology of individual large-canopied tropical trees can directly influence the spectral signature of satellite pixels through time. Additionally, while the level of uncertainty in our model predictions is still very high, we believe this study shows that we are near the threshold for orbital monitoring of individual crowns within tropical forests, even in challenging locations, such as cloudy Gabon. Further technical advances in remote sensing instruments into the spatial and temporal scales relevant to organismal biological processes will unlock great potential to improve our understanding of the Earth system.

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