RESUMO
Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.
Assuntos
Florestas , Árvores , Argentina , Biodiversidade , ClimaRESUMO
Knowledge of the agricultural calendar of crops is essential to better estimate and forecast the cultivation of large-scale crops. The aim of this study was to estimate sowing date (SD), date of maximum vegetative development (DMVD), and harvest date (HD) of soybean and corn in the state of Paraná, Brazil. Dates from 120 farms and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2011 to 2014 were used into a seasonal trend analysis to obtain soybean and corn seasonal patterns. The results indicate that the majority soybean is sown during October and the DMVD occurs between the second ten-day period of December and the first ten-day period of January. Owing to the spatial variability of the SD, the difference in the maturation cycles of the cultivars, and regional climatic variation, the HD of soybean varied greatly during the studied cropyears, ranging from mid-February to late March. The SD of corn is before that of soybean, and mainly occurs in late September to mid-October. The DMVD mainly occurs during December, and the HD is distributed throughout January to March in Paraná. When comparing the estimated dates with observed dates the mean error (ME) varied from 0.2 days earlier to 3.3 days after the observed date for soybean with root mean square error (RMSE) from 1.93 to 14.73 days. For corn, the ME varied from 10.3 days to 18.5 days after the observed date with RMSE from 18.02 to 27.82 days.
O conhecimento do calendário agrícola das culturas é essencial para melhor estimar e prever o cultivo de culturas em larga escala. O objetivo deste estudo foi estimar a data da semeadura (SD), a data de data de máximo desenvolvimento vegetativo (DMVD) e a data da colheita (HD) de soja e milho no estado do Paraná, Brasil. Datas de 120 fazendas e o Índice de Vegetação Aprimorado (EVI) do Espectrorradiômetro de Imagem de Resolução Moderada (MODIS) de 2011 a 2014 foram utilizados em uma análise de tendência sazonal para obter padrões sazonais de soja e milho. Os resultados indicam que a maioria da soja é semeada em outubro e a DMVD ocorre entre o segundo decêndio de dezembro e o primeiro decêndio de janeiro. Devido à variabilidade espacial do SD, à diferença nos ciclos de maturação das cultivares e à variação climática regional, a HD da soja variou bastante durante as safras estudadas, variando de meados de fevereiro a final de março. A SD do milho é anterior a da soja e ocorre principalmente no final de setembro a meados de outubro. O DMVD ocorre principalmente em dezembro e a HD está distribuída entre janeiro e março no Paraná. Ao comparar as datas estimadas comas datas observadas, o erro médio (ME) variou de 0,2 dias antes a 3,3 dias após a data observada para a soja com a raiz do erro quadrático médio (RMSE) de 1,93 a 14,73 dias. Para o milho, o ME variou de10,3 dias a 18,5 dias após a data observada, com RMSE de 18,02 a 27,82 dias.
Assuntos
24444 , Glycine max/crescimento & desenvolvimento , Zea mays/crescimento & desenvolvimentoRESUMO
Knowledge of the agricultural calendar of crops is essential to better estimate and forecast the cultivation of large-scale crops. The aim of this study was to estimate sowing date (SD), date of maximum vegetative development (DMVD), and harvest date (HD) of soybean and corn in the state of Paraná, Brazil. Dates from 120 farms and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2011 to 2014 were used into a seasonal trend analysis to obtain soybean and corn seasonal patterns. The results indicate that the majority soybean is sown during October and the DMVD occurs between the second ten-day period of December and the first ten-day period of January. Owing to the spatial variability of the SD, the difference in the maturation cycles of the cultivars, and regional climatic variation, the HD of soybean varied greatly during the studied cropyears, ranging from mid-February to late March. The SD of corn is before that of soybean, and mainly occurs in late September to mid-October. The DMVD mainly occurs during December, and the HD is distributed throughout January to March in Paraná. When comparing the estimated dates with observed dates the mean error (ME) varied from 0.2 days earlier to 3.3 days after the observed date for soybean with root mean square error (RMSE) from 1.93 to 14.73 days. For corn, the ME varied from 10.3 days to 18.5 days after the observed date with RMSE from 18.02 to 27.82 days.(AU)
O conhecimento do calendário agrícola das culturas é essencial para melhor estimar e prever o cultivo de culturas em larga escala. O objetivo deste estudo foi estimar a data da semeadura (SD), a data de data de máximo desenvolvimento vegetativo (DMVD) e a data da colheita (HD) de soja e milho no estado do Paraná, Brasil. Datas de 120 fazendas e o Índice de Vegetação Aprimorado (EVI) do Espectrorradiômetro de Imagem de Resolução Moderada (MODIS) de 2011 a 2014 foram utilizados em uma análise de tendência sazonal para obter padrões sazonais de soja e milho. Os resultados indicam que a maioria da soja é semeada em outubro e a DMVD ocorre entre o segundo decêndio de dezembro e o primeiro decêndio de janeiro. Devido à variabilidade espacial do SD, à diferença nos ciclos de maturação das cultivares e à variação climática regional, a HD da soja variou bastante durante as safras estudadas, variando de meados de fevereiro a final de março. A SD do milho é anterior a da soja e ocorre principalmente no final de setembro a meados de outubro. O DMVD ocorre principalmente em dezembro e a HD está distribuída entre janeiro e março no Paraná. Ao comparar as datas estimadas comas datas observadas, o erro médio (ME) variou de 0,2 dias antes a 3,3 dias após a data observada para a soja com a raiz do erro quadrático médio (RMSE) de 1,93 a 14,73 dias. Para o milho, o ME variou de10,3 dias a 18,5 dias após a data observada, com RMSE de 18,02 a 27,82 dias.(AU)