Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

O objetivo deste trabalho foi avaliar a redução do vigor vegetativo da cobertura vegetal do Pampa do Brasil e do Uruguai, por meio da identificação de tendências negativas em séries temporais de imagens. Utilizaram-se séries temporais de imagens de NDVI/EVI do sensor Modis, de 2000 a 2011; imagens de índices de umidade do solo do "climate forecast system reanalysis"; e dados de precipitação pluvial de estações meteorológicas. O estudo quantificou tendências lineares e não lineares nas séries de NDVI e EVI, em áreas de campos. Na tendência monotônica de Mann-Kendall, a 5% de probabilidade, 81,9% da área total estudada foi significativa com o NDVI, e 74,8%, com o EVI; no entanto, o EVI apresentou contraste superior na estimativa dos parâmetros. Os resultados mostraram maior sinal negativo a oeste, com valores médios de R²>0,15, r<-0,3 e τ <-0,15 na tendência dos índices de vegetação, e tendência decrescente para NDVI, EVI e precipitação pluvial, com menores valores médios de umidade do solo. A tendência negativa dos índices de vegetação, relacionada à combinação da ocorrência de deficit hídrico em solos rasos com o sobrepastoreio, indica alterações no padrão de cobertura vegetal do Pampa, com redução do vigor vegetativo

Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes

The nitrogen content in plant foliar tissues (foliar N) regulates photosynthetic capacity and has a major impact on global biogeochemical cycles. Despite its importance, a robust, time, and cost-effective methodology to estimate variation in foliar N concentration across globally represented terrestrial systems does not exist. Although advances in remote sensing data have enabled landscape-scale foliar N predictions, improved accuracy is needed to effectively capture variation in foliar N across ecosystems. Airborne remote sensing imagery was analyzed in conjunction with ground-sampled foliar chemistry data (n = 692), provided by the NEON, to predict foliar N at sites across the United States covering a variety of plant communities and climate types. We developed indices from novel two-band combinations that predicted foliar N more accurately than existing indices (≈8% improvement across all sites and a 45% improvement in arid sites). Compared with two-band indices, we increased accuracy and decreased bias of foliar N predictions by using full-spectrum reflectance information and partial least squares regression (PLSR) models (R2 = 0.638; root mean square error = 0.440). Significant wavelengths included red edge (720–765 nm), near infrared (NIR) reflectance at 1125 nm, and shortwave infrared (SWIR) reflectance at 2050 and 2095 nm, which are regions indicative of foliar traits such as growth type (e.g., leaf area index with NIR) and photosynthetic parameters (e.g., chlorophyll and Rubisco with red and SWIR reflectance, respectively). With the confluence of rapid increases in computing power, several forthcoming or recently launched hyperspectral missions, and the development of large-scale environmental research observatories worldwide, we have an exciting opportunity to estimate foliar N across larger spatial areas covering more diverse biomes than ever before. We anticipate that these predictions will prove to be invaluable in helping to constrain biogeochemical model uncertainties across a global range of terrestrial ecosystems. © 2022 The Authors. Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of America.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Assessing vegetation response to multi-scalar drought across the mojave, sonoran, chihuahuan deserts and apache highlands in the Southwest United States

Understanding the patterns and relationships between vegetation productivity and climatic conditions is essential for predicting the future impacts of climate change. Climate change is altering precipitation patterns and increasing temperatures in the Southwest United States. The large-scale and long-term effects of these changes on vegetation productivity are not well understood. This study investigates the patterns and relationships between seasonal vegetation productivity, represented by Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Evapotranspiration Index (SPEI) across the Mojave, Sonoran, and Chihuahuan Deserts and the Apache Highlands of the Southwest United States over 16 years from 2000 to 2015. To examine the spatiotemporal gradient and response of vegetation productivity to dry and wet conditions, we evaluated the linear trend of different SPEI timescales and correlations between NDVI and SPEI. We found that all four ecoregions are experiencing more frequent and severe drought conditions in recent years as measured by negative SPEI trends and severe negative SPEI values. We found that changes in NDVI were more strongly correlated with winter rather than summer water availability. Investigating correlations by vegetation type across all four ecoregions, we found that grassland and shrubland productivity were more dependent on summer water availability whereas sparse vegetation and forest productivity were more dependent on winter water availability. Our results can inform resource management and enhance our understanding of vegetation vulnerability to climate change. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Climate and socioeconomic factors drive irrigated agriculture dynamics in the lower Colorado river basin

The Colorado River Basin (CRB) includes seven states and provides municipal and industrial water to millions of people across all major southwestern cities both inside and outside the basin. Agriculture is the largest part of the CRB economy and crop production depends on irrigation, which accounts for about 74% of the total water demand cross the region. A better understanding of irrigation water demands is critically needed as temperatures continue to rise and drought intensifies, potentially leading to water shortages across the region. Yet, past research on irrigation dynamics has generally utilized relatively low spatiotemporal resolution datasets and has often overlooked the relationship between climate and management decisions such as land fallowing, i.e., the practice of leaving cultivated land idle for a growing season. Here, we produced annual estimates of fallow and active cropland extent at high spatial resolution (30 m) from 2001 to 2017 by applying the fallow-land algorithm based on neighborhood and temporal anomalies (FANTA). We specifically focused on diverse CRB agricultural regions: the lower Colorado River planning (LCRP) area and the Pinal and Phoenix active management areas (PPAMA). Utilizing ground observations collected in 2014 and 2017, we found an overall classification accuracy of 88.9% and 87.2% for LCRP and PPAMA, respectively. We then quantified how factors such as climate, district water rights, and market value influenced: (1) annual fallow and active cropland extent and (2) annual cropland productivity, approximated by integrated growing season NDVI (iNDVI). We found that for the LCRP, a region of winter cropping and senior (i.e., preferential) water rights, active cropland productivity was positively correlated with cool-season average vapor pressure deficit (R = 0.72; p < 0.01). By contrast, for the PPAMA, a region of summer cropping and junior water rights, annual fallow and active cropland extent was positively correlated with cool-season aridity (precipitation/potential evapotranspiration) (R = 0.46; p < 0.05), and active cropland productivity was positively correlated with warm-season aridity (precipitation/potential evapotranspiration) (R = 0.42; p < 0.01). We also found that PPAMA cropland productivity was more sensitive to aridity when crop prices were low, potentially due to the influence of market value on management decisions. Our analysis highlights how biophysical (e.g., temperature and precipitation) and socioeconomic (e.g., water rights and crop market value) factors interact to explain seasonal patterns of cropland extent, water use and productivity. These findings indicate that increasing aridity across the region may result in reduced cropland productivity and increased land fallowing for some regions, particularly those with junior water rights. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Canopy Temperature Is Regulated by Ecosystem Structural Traits and Captures the Ecohydrologic Dynamics of a Semiarid Mixed Conifer Forest Site

Plant canopy temperature (Tc) is partly regulated by evaporation and transpiration from the canopy surface and can be used to infer changes in stomatal regulation and vegetation water stress. In this study, we used a thermal Unmanned Aircraft Systems in conjunction with eddy covariance, sap flow, and spectral reflectance data to assess the diurnal characteristics of Tc and water stress status over a semiarid mixed conifer forest in Arizona, USA. Diurnal Tc dynamics were closely related to tree sap flow and changes in spectral reflectance associated with stomatal regulation. Consistent with previously reported deviations, we found that on average Tc was 1.8°C lower than the above canopy air temperature (Ta). However, the relationship between Tc and Ta varied significantly according to tree density and tree height classes, with taller and denser trees exhibiting relatively low |Tc-Ta| (2.4 and 2.1°C cooler canopies, respectively) compared to shorter and less-dense tree stands (1.7 and 1.5°C cooler canopies, respectively). We used these data to evaluate space-borne diurnal measurements of Tc and water stress from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. We found that ECOSTRESS observations of Tc accurately tracked seasonal shifts in diurnal surface temperatures and vegetation water stress, and that site-level observations of heterogeneity in forest composition and structure could be applied to separate the processes of canopy transpiration and soil evaporation within the ECOSTRESS footprint. This study demonstrates how proximal and satellite remote sensing approaches can be combined to reveal the diurnal and seasonally dynamic nature of Tc and water stress. © 2022. American Geophysical Union. All Rights Reserved.6 month embargo; first published: 01 February 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Orbital spectral variables, growth analysis and sugarcane yield Variáveis espectrais orbitais, indicadoras de desenvolvimento e produtividade da cana-de-açúcar

Temporal analysis of crop development in commercial fields requires tools for large area monitoring, such as remote sensing. This paper describes the temporal evolution of sugar cane biophysical parameters such as total biomass (BMT), yield (TSS), leaf area index (LAI), and number of plants per linear meter (NPM) correlated to Landsat data. During the 2000 and 2001 cropping seasons, a commercial sugarcane field in Araras, São Paulo state, Brazil, planted with the SP80-1842 sugarcane variety in the 4th and 5th cuts, was monitored using nine Landsat images. Spectral data were correlated with agronomic data, obtained simultaneously to the imagery acquisition. Two methodologies were used to collect spectral data from the images: four pixels (2 &#215; 2) window and average of total pixels in the field. Linear and multiple regression analysis was used to study the spectral behavior of the plants and to correlate with agronomic variables (days after harvest-DAC, LAI, NPM, BMT and TSS). No difference was observed between the methodologies to collect spectral data. The best models to describe the spectral crop development in relation to DAC were the quadratic and cubic models. Ratio vegetation index and normalized difference vegetation index demonstrated correlation with DAC, band 3 (B3) was correlated with LAI, and NDVI was well correlated with TSS and BMT. The best fit curves to estimate TSS and BMT presented r² between 0.68 and 0.97, suggesting good potential in using orbital spectral data to monitor sugarcane fields.<br>Dados de satélites são tradicionalmente utilizados em monitoramento de culturas. O presente trabalho busca contribuir no entendimento da evolução temporal de indicadores de crescimento da cana-de-açúcar como a biomassa total (BMT), produtividade (TSS), índice de área foliar (LAI) e número de plantas por metro (NPM) por meio de dados orbitais dos satélites Landsat 5 e 7, e verificar o seu potencial para o monitoramento desta. Durante as safras 2000 e 2001, uma área comercial em Araras, SP, cultivada com a variedade SP80-1842 no 4º e 5º cortes, foi acompanhada por imagens, buscando-se correlacionar dados espectrais com dados agronômicos. Os dados espectrais foram coletados de duas formas: uma com janelas de quatro pixels e outra com dados médios do talhão (DMt). Regressão linear e múltipla foram usadas para a análise temporal das bandas 3 e 4 e de índices de vegetação. As correlações e ajuste de modelos entre os dados espectrais orbitais e as variáveis agronômicas não apresentaram diferenças estatísticas. Os modelos quadráticos e cúbicos melhor descreveram o desenvolvimento temporal das variáveis espectrais, em função dos dias após o corte e apresentaram significância com os índices de vegetação da razão e por diferença normalizada (NDVI). As correlações entre os dados espectrais médios do talhão e as variáveis agronômicas foram significativas para banda3 e LAI, e entre NDVI e TSS/BMT. Os dados médios do talhão (DMt), para primeira safra (1ªS), para a segunda safra (2ªS) e ambas juntas geraram regressões múltiplas, com coeficientes determinação (r²) variando de 0,68 a 0,97 para a TSS e a BMT, mostrando que os dados espectrais orbitais estudados podem ser empregados no monitoramento da cultura da cana-de-açúcar