1992—2012年东北水稻生育期变化分析

生产实践中水稻生育期变化是气候条件和品种更新等因素共同作用的结果。利用东北地区13个农业气象站点1992—2012年水稻试验观测资料,分析水稻生育期的变化及其与东北变暖趋势、水稻品种调整的关系。结果表明：水稻生长季平均气温和≥10℃积温在1992—2001年呈显著增加趋势,水稻生长季积温的差别最大可达500℃?d,从2002年开始升温趋势减缓,并略有下降。相应地,2002—2012年与1992—2001年的物候期基本上呈现相反的变化特征,其中抽穗期、乳熟期、成熟期在1992—2001年明显提前,分别提前了3.1、2.9、4.5 d/10a,移栽期、分蘖期、成熟期则在2002—2012年呈现出明显的推迟趋势,分别推迟了4.6、4.7、2.0 d/10a;生育期的变化受多种因素影响,但播种-移栽期、乳熟-成熟期在1992—2001年分别缩短了0.7、1.6 d/10a,而在2002—2012年则分别延长了2.9、2.8 d/10a;总的来说,1992—2012年水稻全生育期整体延长了3.7 d/10a,其主要归因于营养生长期的延长。在试验资料比较完整的12个站点所做的分析表明,在东北水稻种植的生产实践中,不断地通过品种调整适应气候条件的改变,多数站点水稻实际生育期与品种审定生育日数差别较小,说明其品种的选择能够与当年的气候条件较好地匹配,充分利用了当地的气候资源。延吉站、梅河口站、通化站随着气候的持续波动又显示出水稻实际生育期与品种审定生育日数偏差增大的趋势,宁安站和前郭尔罗斯站的水稻种植品种的审定生育日数几乎没有改变。因此,迫切需要对东北气候变化的科学事实和水稻种植适应气候变化的生产实践进行系统的总结,为东北水稻生产适应气候变化提供科学支撑。     

Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS observations and canopy reflectance model

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r² = 0.73 and r² = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm⁻² (Tapajós tower) to 0.470 μg cm⁻² (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r² = 0.73). Chlorophyll pigment concentrations (Chl_A+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤  r² ≤  0.64). Increases in MODIS observed Chl_A+B were preceded by increased PAR during the dry season (0.61 ≤ r² ≤  0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors.     

Using ground observations of a digital camera in the VIS-NIR range for quantifying the phenology of Mediterranean woody species

The spectral reflectance of most plant species is quite similar, and thus the feasibility of identifying most plant species based on single date multispectral data is very low. Seasonal phenological patterns of plant species may enable to face the challenge of using remote sensing for mapping plant species at the individual level. We used a consumer-grade digital camera with near infra-red capabilities in order to extract and quantify vegetation phenological information in four East Mediterranean sites. After illumination corrections and other noise reduction steps, the phenological patterns of 1839 individuals representing 12 common species were analyzed, including evergreen trees, winter deciduous trees, semi-deciduous summer shrubs and annual herbaceous patches. Five vegetation indices were used to describe the phenology: relative green and red (green\red chromatic coordinate), excess green (ExG), normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI). We found significant differences between the phenology of the various species, and defined the main phenological groups using agglomerative hierarchical clustering. Differences between species and sites regarding the start of season (SOS), maximum of season (MOS) and end of season (EOS) were displayed in detail, using ExG values, as this index was found to have the lowest percentage of outliers. An additional visible band spectral index (relative red) was found as useful for characterizing seasonal phenology, and had the lowest correlation with the other four vegetation indices, which are more sensitive to greenness. We used a linear mixed model in order to evaluate the influences of various factors on the phenology, and found that unlike the significant effect of species and individuals on SOS, MOS and EOS, the sites' location did not have a direct significant effect on the timing of phenological events. In conclusion, the relative advantage of the proposed methodology is the exploitation of representative temporal information that is collected with accessible and simple devices, for the subsequent determination of optimal temporal acquisition of images by overhead sensors, for vegetation mapping over larger areas.     

Climatic triggers and phenological responses in Isoetes cangae (Isoetaceae), an endemic quillwort from Amazon Iron Rocky Outcrops,Brazil

South America is the center of diversity of the genus Isoetes and several new species have been described in the last decade, especially in Brazil. Isoetes cangae J.B.S. Pereira, Salino & Stützel was first recognized in 2016 as an endemic species of a single lake in Serra dos Carajás, Brazil, in the southeastern Amazon region. The climate of Amazon is warm with a seasonal precipitation regime, which affects aquatic ecosystem properties and regulates plant phenology. Understanding how climatic and hydrological drivers affect I. cangae is fundamental for its conservation because the species is quite vulnerable due to its restricted distribution. In this study, we evaluated the effects of seasonal climatic variations and water level on the phenology of I. cangae over two years. The maximum leaf length, the number of sporophylls (fertile leaves), and the total number of leaves were assessed. Sporophylls were classified into mature and immature, megasporophylls and microsporophylls. The number of leaves, leaf length, and the number of sporophylls changed drastically among the hydrological seasons, but not the ratio between sporophylls and leaves, suggesting that reproductive effort is distributed accordingly over the years. All these factors also varied between years, indicating that inter-annual climatic variations affect the morphology and reproduction of I. cangae. Sporophylls are produced throughout the year, but their maturation and release increase in the rainy and early dry seasons, respectively. Megasporophylls predominate over microsporophylls throughout the year, and the peak of sporophyll maturation occurs during the rainy season, but lasts longer (till early dry season) for megasporophylls. The reproductive traits were associated with lake water level, suggesting that development is stimulated during the driest and hottest periods. Therefore, our results show that environmental factors play an important role in I. cangae phenology and must be considered in the management and conservation efforts to preserve this ancient Amazonian plant.     

Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin,Eastern Africa

In arid and semi-arid areas, evaporation fluxes are the largest component of the hydrological cycle, with runoff coefficient rarely exceeding 10%. These fluxes are a function of land use and land management and as such an essential component for integrated water resources management. Spatially distributed land use and land cover (LULC) maps distinguishing not only natural land cover but also management practices such as irrigation are therefore essential for comprehensive water management analysis in a river basin. Through remote sensing, LULC can be classified using its unique phenological variability observed over time. For this purpose, sixteen LULC types have been classified in the Upper Pangani River Basin (the headwaters of the Pangani River Basin in Tanzania) using MODIS vegetation satellite data. Ninety-four images based on 8 day temporal and 250 m spatial resolutions were analyzed for the hydrological years 2009 and 2010. Unsupervised and supervised clustering techniques were utilized to identify various LULC types with aid of ground information on crop calendar and the land features of the river basin. Ground truthing data were obtained during two rainfall seasons to assess the classification accuracy. The results showed an overall classification accuracy of 85%, with the producer’s accuracy of 83% and user’s accuracy of 86% for confidence level of 98% in the analysis. The overall Kappa coefficient of 0.85 also showed good agreement between the LULC and the ground data. The land suitability classification based on FAO-SYS framework for the various LULC types were also consistent with the derived classification results. The existing local database on total smallholder irrigation development and sugarcane cultivation (large scale irrigation) showed a 74% and 95% variation respectively to the LULC classification and showed fairly good geographical distribution. The LULC information provides an essential boundary condition for establishing the water use and management of green and blue water resources in the water stress Pangani River Basin.     

Study on inter-seasonal and intra-seasonal relationships of meteorological and agricultural drought indices in the Rajasthan State of India

Use of rainfall anomaly based Standardized Precipitation Index (SPI) and satellite-derived Vegetation Condition Index (VCI) are becoming common to assess the impacts of drought on crops. This study analysed spatio-temporal intra-seasonal and inter-seasonal relationships for 24 years between rainfall and NDVI and between SPI and VCI to understand crop response to water availability in the Rajasthan State, India. To separate the effect of weather and technology on crop growth over time, a modification in VCI was proposed and called “Trend Adjusted VCI” (VCI_Tadj). The VCI_Tadj was computed for early, mid, late and whole crop seasons by deriving pixel wise crop phenology metrics from NDVI profile. Significant linear relationships were found between NDVI and rainfall but phase of crop season affected the strength of this relationship. The SPI and VCI_Tadj were linearly related in all the four seasons, the strength of relationship improved with the progress of crop season and these relationships were stronger than between rainfall and NDVI. These relationships broke down in irrigated croplands. As a result, the anomaly indices of SPI and VCI_Tadj and their intra-seasonal relationships can be used to study the response of crops to water availability for early detection and better prognosis of agricultural drought.     

Land use intensity trajectories on Amazonian pastures derived from Landsat time series

Monitoring changes in land use intensity of grazing systems in the Amazon is an important prerequisite to study the complex political and socio-economic forces driving Amazonian deforestation. Remote sensing offers the potential to map pasture vegetation over large areas, but mapping pasture conditions consistently through time is not a trivial task because of seasonal changes associated with phenology and data gaps from clouds and cloud shadows. In this study, we tested spectral-temporal metrics derived from intra-annual Landsat time series to distinguish between grass-dominated and woody pastures. The abundance of woody vegetation on pastures is an indicator for management intensity, since the duration and intensity of land use steer secondary succession rates, apart from climate and soil conditions. We used the developed Landsat-based metrics to analyze pasture intensity trajectories between 1985 and 2012 in Novo Progresso, Brazil, finding that woody vegetation cover generally decreased after four to ten years of grazing activity. Pastures established in the 80s and early 90s showed a higher fraction of woody vegetation during their initial land use history than pastures established in the early 2000s. Historic intensity trajectories suggested a trend towards more intensive land use in the last decade, which aligns well with regional environmental policies and market dynamics. This study demonstrates the potential of dense Landsat time series to monitor land-use intensification on Amazonian pastures.     

Snowmelt velocity predicts vegetation green-wave velocity in mountainous ecological systems of North America

The timing of spring initiates an important period for resource availability for large trophic webs within ecosystems, including forage for grazing animals, flowers for pollinators, and the higher trophic levels that depend on these resources. Spring timing is highly variable across space, being influenced strongly by the departure of snow cover (i.e. snowmelt timing, in locations with a seasonal snowpack), climate, weather, elevation, and latitude. When spring timing occurs along a gradient (e.g. spring arriving later in higher elevations of mountainous terrain), the organisms that rely on spring resources often migrate to maintain an optimal position for spring resources – a phenomenon known as ‘surfing the green wave.’ While this behavior has been observed by tracking animals, there have been no studies to quantify the green wave as a movement across space and time. Furthermore, considering that snowmelt timing has moderate power to explain green-up timing for a given location, we ask the question: does snowmelt velocity predict green wave velocity? Here, we introduce the first continental maps of snowmelt and green wave velocity for North America from 2001 to 2016 as derived from the MODIS MCD12Q2 phenology dataset. We show that both snowmelt and green wave velocities are influenced strongly by topography, including slope and aspect. Furthermore, we quantify the relationships between snowmelt and green wave velocities according to three variables: direction, speed, and distance traveled. We conclude that mountainous ecoregions, such as the western North American cordillera, have the highest correspondence between snowmelt and green wave velocities, compared to flatter regions such as the Great Plains and tundra. This work will be of interest to wildlife ecologists, biologists, and land managers who seek to conserve migratory animals and the ecosystems that support them.     

Dating flowering cycles of Amazonian bamboo-dominated forests by supervised Landsat time series segmentation

Bamboo-dominated forests are unusual and interesting because their structure and biomass fluctuate in decades-long cycles corresponding to the flowering and mortality rhythm of the bamboo. In southwestern Amazonia, these forests have been estimated to occupy an area of approximately 160 000 km², and a single reproductively synchronized patch can cover up to thousands of square kilometers. Accurate mapping of these forests is challenging, however: the forests are spatially heterogeneous, with bamboo densities varying widely among adjacent sites; much of the area is inaccessible, so field verification of bamboo presence is difficult to obtain and georeferenced records of past flowering events virtually non-existent; and detectability of the bamboo by remote sensing varies considerably during its life cycle. In this study, we develop a supervised time series segmentation approach that allows us to identify both the presence of bamboo forests and the years in which the bamboo flowering and subsequent mortality have occurred. We then apply the method to the entire Landsat TM/ETM+ archive from 1984 to the end of 2018 and validate the classification by visual interpretation of very high resolution imagery. Collecting accurate ground reference data of bamboo presence and bamboo mortality timing is notably difficult in these forests, and we therefore developed a methodology that takes advantage of imperfect reference data obtained from the Landsat time series itself. Our results show that bamboo forests can be differentiated from non-bamboo forests using any of the infrared bands, but band 5 produces the highest classification accuracy. Interestingly, there appears to be a temporal difference in the spectral responses of the three infrared bands to bamboo flowering and mortality: near infrared (band 4) reflectance reacts to the event earlier than shortwave infrared (bands 5 and 7) reflectance. The long Landsat TM/ETM+ archive allows our methodology to detect some areas with two mortality events, with a theoretical maximum interval of 29 years. Analysis of these pixels with repeated mortality confirms that the life cycles of the local bamboo species (Guadua sarcocarpa and G. weberbauerii) last typically 28 years.     

Edible biomass production from some important browse species in the Sahelian zone of West Africa

Acacia senegal, Guiera senegalensis and Pterocarpus lucens, browse species important in the Sahelian zone of Burkina Faso were studied by the estimation of their phenological variation over time and the evaluation of edible biomass production, total and accessible directly to animals. Biomass production was also estimated using dendrometric parameters. All the three species started the foliation phase as soon as the rains started. A. senegal and P. lucens flowered before G. senegalensis and A. senegal lost leaves earlier. The fruiting phase lasted 6–7 months for all species. Accessible edible biomass varied according to the animal species, the plant species and the height of plants. G. senegalensis showed the highest proportion of accessible biomass, but P. lucens had higher total edible biomass. Goats browsing at higher height had more edible biomass at their disposal. The accessible edible biomass was weakly correlated with tree parameters, while crown diameter was the best parameter to predict total edible biomass production, with R² varying from 90% (G. senegalensis) to 98% (P. lucens) in log₁₀ transformation of dependent and independent variables. The single species models developed could be applied in similar agro-ecological zones, taking into account the height stratification of plants. Further investigations on others species are needed to be able to estimate total biomass available for browsing.