Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants.

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize.

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher -logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.

Características del hábitat que regulan la estructura de las comunidades de macroinvertebrados en ríos tropicales de montaña (Antioquía, Colombia) / Habitat characteristics that regulate the structure of macroinvertebrate communities in tropical mountain rivers (Antioquía, Colombia)

Introducción: Las comunidades de macroinvertebrados son afectadas simultáneamente por la calidad del agua y las características físicas del hábitat acuático, complicando su uso en la bioindicación. Objetivo: Determinar cuáles variables del hábitat condicionan la comunidad de macroinvertebrados acuáticos en algunas corrientes (quebradas) de montaña del Oriente antioqueño (Colombia). Métodos: El muestreo se realizó en febrero 2021 (periodo de transición seco-lluvia), para evaluar variables físicas y químicas en tres tipos de mesohábitats: rápidos, rizos y pozas en corrientes con coberturas vegetales contrastantes. Los macroinvertebrados fueron recolectados en diez sitios de muestreo con red tipo net, pantalla y manual, y preservados en etanol al 70 %. Resultados: Se recolectaron 4 484 macroinvertebrados (16 órdenes, 46 familias y 75 géneros). El mesohábitat rizo presentó mayores valores de diversidad y abundancia, mientras las pozas presentaron los menores. Hubo diferencias en la concentración de oxígeno, profundidad, velocidad y abundancia de macroinvertebrados entre mesohábitats. Las pozas defirieron de los otros mesohábitats en profundidad, velocidad, así como en la composición, abundancia y riqueza de macroinvertebrados, y fue el hábitat de menor preferencia. Conclusión: La velocidad, profundidad y concentración de oxígeno disuelto, desempeñan un papel muy importante en el establecimiento de las comunidades de macroinvertebrados en los diferentes mesohábitats. En el mismo tipo de mesohábitat, la calidad de la cobertura vegetal determinó la diversidad y abundancia de esta comunidad.

Introduction: Macroinvertebrate communities are affected by water quality and physical characteristics of the aquatic habitat, simultaneously, complicating their use as bioindicators. Objective: To determine which habitat variables regulate the macroinvertebrate community in mountain streams in Eastern of Antioquia (Colombia). Methods: Sampling was carried out in February 2021 (dry-rain transition period), to evaluate physical and chemical variables in three types of mesohabitat: ripples, pools, and rapids in streams with contrasting vegetation covers. The macroinvertebrates were collected from ten sampling sites with a net, screen and manual type net preserved with 70 % ethanol. Results: 4 484 macroinvertebrates were collected (16 orders, 46 families and 75 genera). The ripples mesohabitat presented higher values of diversity and abundance, while the pools presented the lowest. There were differences for oxygen concentration, depth, speed, and macroinvertebrate abundance between mesohabitats. Pools differed from the other mesohabitats in depth, speed, as well as in composition, abundance, and richness in macroinvertebrates, and was the least preferred mesohabitat. Conclusion: Speed, depth, dissolved oxygen concentration played a very important role in the establishment of macroinvertebrates community in different mesohabitats. For the same type of mesohabitat, the quality of the plant cover determined both diversity and abundance of this community.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy.

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots.

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Deforestation and fires in the Brazilian Amazon from 2001 to 2020: Impacts on rainfall variability and land surface temperature.

Deforestation and fires in the Amazon are serious problems affecting climate, and land use and land cover (LULC) changes. In recent decades, the Amazon biome area has suffered constant fires and deforestation, causing severe environmental problems that considerably impact the land surface temperature (LST) and hydrological cycle. The Amazon biome lost a large forest area during this period. Thus, this study aims to analyze the deforestation and burned areas in the Amazon from 2001 to 2020, considering their impacts on rainfall variability and LST. This study used methods and procedures based on Google Earth Engine for analysis: (a) LULC evolution mapping, (b) vegetation cover change analysis using vegetation indices, (c) mapping of fires, (d) rainfall and LST analyses, and (e) analysis of climate influence and land cover on hydrological processes using the geographically weighted regression method. The results showed significant LULC changes and the main locations where fires occurred from 2001 to 2020. The years 2007 and 2010 had the most significant areas of fires in the Brazilian Amazon (233,401 km2 and 247,562 km2, respectively). The Pará and Mato Grosso states had the region's largest deforested areas (172,314 km2 and 144,128 km2, respectively). Deforestation accumulated in the 2016-2020 period is the greatest in the period analyzed (254,465 km2), 92% higher than in the 2005-2010 period and 82% higher than in the 2001-2005 period. The study also showed that deforested areas have been increasing in recent decades, and the precipitation decreased, while an increase is observed in the LST. It was also concluded that indigenous protection areas have suffered from anthropic actions.

Monitoring detailed mangrove hurricane damage and early recovery using multisource remote sensing data.

Due to their location in tropical latitudes, mangrove forests are susceptible to the impact of hurricanes and can be vastly damaged by their high-speed winds. Given the logistic difficulties regarding field surveys in mangroves, remote sensing approaches have been considered a reliable alternative. We quantified trends in damage and early signs of canopy recovery in a fringe Rhizophora mangle area of Marismas Nacionales, Mexico, following the landfall of Hurricane Willa in October 2018. We monitored (2016-2021) broad canopy defoliation using 21 vegetation indices (VI) from the Google Earth Engine tool (GEE). We also mapped a detailed canopy fragmentation and developed digital surface models (DSM) during five study periods (2018-2021) with a consumer-grade unmanned aerial vehicle (UAV) over an area of 100 ha. Based on optical data from the GEE time series, results indicated an abrupt decline in the overall mangrove canopy. The VARI index was the most reliable VI for the mangrove canopy classification from a standard RGB sensor. The impact of the hurricane caused an overall canopy defoliation of 79%. The series of UAV orthomosaics indicate a gradual recovery in the mangrove canopy, while the linear model predicts at least 8.5 years to reach pre-impact mangrove cover conditions. However, the sequence of DSM estimates that the vertical canopy configuration will require a longer time to achieve its original structure.

High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield.

BACKGROUND: Precision agriculture techniques are widely used to optimize fertilizer and soil applications. Furthermore, these techniques could also be combined with new statistical tools to assist in phenotyping in breeding programs. In this study, the research hypothesis was that soybean cultivars show phenotypic differences concerning wavelength and vegetation index measurements. RESULTS: In this research, we associate variables obtained via high-throughput phenotyping with the grain yield and cycle of soybean genotypes. The experiment was carried out during the 2018/2019 and 2019/2020 crop seasons, under a randomized block design with four replications. The evaluated soybean genotypes included 7067, 7110, 7739, 8372, Bonus, Desafio, Maracai, Foco, Pop, and Soyouro. The phenotypic traits evaluated were: first pod height (FPH), plant height (PH), number of branches (NB), stem diameter (SD), days to maturity (DM), and grain yield (YIE). The spectral variables evaluated were wavelengths and vegetation indices (NDVI, SAVI, GNDVI, NDRE, SCCCI, EVI, and MSAVI). The genotypes Maracai and Foco showed the highest grain yields throughout the crop seasons, in addition to belonging to the groups with the highest means for all VIs. YIE was positively correlated with the NDVI and certain wavelengths (735 and 790 nm), indicating that genotypes with higher values for these spectral variables are more productive. By path analyses, GNDVI and NDRE had the highest direct effects on the dependent variable DM, while NDVI had a higher direct effect on YIE. CONCLUSIONS: Our findings revealed that early and productive genotypes can be selected based on vegetation indices and wavelengths. Soybean genotypes with a high grain yield have higher means for NDVI and certain wavelengths (735 and 790 nm). Early genotypes have higher means for NDRE and GNDVI. These results reinforce the importance of high-throughput phenotyping as an essential tool in soybean breeding programs.

High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat.

BACKGROUND: Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density. RESULTS: Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance. CONCLUSIONS: Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy.

Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha-1. Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).

Retrieval of Vegetation Indices Related to Leaf Water Content from a Single Index: A Case Study of Eucalyptus globulus (Labill.) and Pinus radiata (D. Don.).

The vegetation indices derived from spectral reflectance have served as an indicator of vegetation's biophysical and biochemical parameters. Some of these indices are capable of characterizing more than one parameter at a time. This study examines the feasibility of retrieving several spectral vegetation indices from a single index under the assumption that all these indices are correlated with water content. The models used are based on a linear regression adjusted with least squares. The spectral signatures of Eucalyptus globulus and Pinus radiata, which constitute 97.5% of the forest plantation in Valparaiso region in Chile, have been used to test and validate the proposed approach. The linear models were fitted with an independent data set from which their performance was assessed. The results suggest that from the Leaf Water Index, other spectral indices can be recovered with a root mean square error up to 0.02, a bias of 1.12%, and a coefficient of determination of 0.77. The latter encourages using a sensor with discrete wavelengths instead of a continuum spectrum to estimate the forestry's essential parameters.

Temporal record and spatial distribution of fire foci in State of Minas Gerais, Brazil.

The objectives of this study are: (i) to evaluate the space-temporal variability of fire foci by environmental satellites, CHIRPS and remote sensing products based on applied statistics, and (ii) to identify the relational pattern between the distribution of fire foci and the environmental, meteorological, and socioeconomic variables in the mesoregions of Minas Gerais (MG) - Brazil. This study used a time series of fire foci from 1998 to 2015 via BDQueimadas. The temporal record of fire foci was evaluated by Mann-Kendall (MK), Pettitt (P), Shapiro-Wilk (SW), and Bartlett (B) tests. The spatial distribution by burned area (MCD64A1-MODIS) and the Kernel density - (radius 20 km) were estimated. The environmental variables analyzed were: rainfall (mm) and maximum temperature (°C), besides proxies to vegetation canopy: NDVI, SAVI, and EVI. PCA was applied to explain the interaction between fire foci and demographic, environmental, and geographical variables for MG. The MK test indicated a significant increasing trend in fire foci in MG. The SW and B tests were significant for non-normality and homogeneity of data. The P test pointed to abrupt changes in the 2001 and 2002 cycles (El Niño and La Niña moderated), which contributes to the annual increase and in winter and spring, which is identified by the Kernel density maps. Burned areas highlighted the northern and northwestern regions of MG, Triângulo Mineiro, Jequitinhonha, and South/Southwest MG, in the 3rd quarter (increased 17%) and the 4th quarter (increased 88%). The PCA resulted in three PCs that explained 71.49% of the total variation. The SAVI was the variable that stood out, with 11.12% of the total variation, followed by Belo Horizonte, the most representative in MG. We emphasize that the applied conceptual theoretical model defined here can act in the environmental management of fire risk. However, public policies should follow the technical-scientific guidelines in the mitigation of the resulting socioeconomic - environmental damages.

Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves.

Continuum monitoring of mangrove ecosystems is required to maintain and improve upon national mangrove conservation strategies. In particular, mangrove canopy assessments using remote sensing methods can be undertaken rapidly and, if freely available, optimize costs. Although such spaceborne data have been used for such purposes, their application to map mangroves at the species level has been limited by the capacity to provide continuous data. The objective of this study was to assess mangrove seasonal patterns using seven multispectral vegetation indices based on a Sentinel-2 (S2) time series (July 2018 to October 2019) to assess phenological trajectories of various semiarid mangrove classes in the Google Earth Engine platform using Fourier analysis for an area located in Western Mexico. The results indicate that the months from November through December and from May through July were critical in mangrove species discrimination using the EVI2, NDVI, and VARI series. The Random Forest classification accuracy for the S2 image was calculated at 79% during the optimal acquisition period (June 25, 2019), whereas only 55% accuracy was calculated for the non-optimal image acquired date (March 2, 2019). Although mangroves are considered evergreen forests, the phenological pattern of various mangrove canopies, based on these indices, were shown to be very similar to the surrounding land-based semiarid deciduous forest. Consequently, it is believed that the rainfall pattern is likely to be the key environmental factor driving mangrove phenology in this semiarid coastal system and thus the degree of success in mangrove remote sensing classification endeavors. Identifying the optimal dates when canopy spectral conditions are ideal in achieving mangrove species discrimination could be of utmost importance when purchasing more expensive very-high spatial resolution satellite images or collecting spatial data from UAVs.

Pre-planting weed detection based on ground field spectral data.

BACKGROUND: Site-specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non-targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non-targets for each VI using training and validation data sets. RESULTS: The main outcomes of this study for effectively distinguishing weeds from other non-targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near-infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.93. CONCLUSION: This study provided a novel approach to distinguish weeds from other non-targets utilizing a ground-level sensor before cash crop planting based on field spectral data. However, the limitations of this study are related to the spatial resolution to distinguish weeds that might be closer to the one this study presented, and also related to the soil and crop residues conditions at the time of collecting the readings. Overall the results presented contribute to an improved understanding of spectral signatures from different targets (weeds, soils, and residues) before planting time supporting SSWM. © 2019 Society of Chemical Industry.

Spatial and temporal variation of the vegetation of the semiarid Puna in a pastoral system in the Pozuelos Biosphere Reserve.

This study aimed to analyze the spatial and temporal variation of the vegetation in the northern Argentine Puna, utilizing both field sampling and remote-sensing tools. The study was performed within the Pozuelos Biosphere Reserve (Jujuy province, Argentina), which aims to generate socio-economic development compatible with biodiversity conservation. Our study was designed to analyze the dynamics of the Puna vegetation at local scale and assess and monitor the seasonal (dry and wet seasons), interannual, and spatial variation of the vegetation cover, biomass, dominant species, and vegetation indices. Ten vegetation units (with differences in composition, cover, and high and low stratum biomass) were identified at our study site. The diversity of these vegetation units correlated with geomorphology and soil type. In the dry season, the vegetation unit with greatest vegetation cover and biomass was the Festuca chrysophylla grassland, whereas in the wet season, the units with greatest cover and biomass were vegas (peatlands) and short grasslands. The Festuca chrysophylla grasslands and short grasslands were located in areas with clay soils, except peatlands, associated with valleys and coarse-texture soils. The vegetation indices used (NDVI, SAVI, and MSAVI2) were able to differentiate functional types of vegetation and showed a good statistical fit with cover values. Our results suggest that the integrated utilization of remote-sensing tools and field surveys improves the assessment of the Puna vegetation and would allow a periodic monitoring at production unit scale taking into account its spatial and temporal variation.

Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction

Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple Linear Regression models are similarly able to predict biomass, and that associating biometric variables such as number of stalks and plant height with reflectance data can assist model performance, depending on the attributes selected. This indicates that, when estimating biomass in the early stages, sugarcane growers can carry out site-specific management in order to increase yield and reduce the use of inputs.(AU)

Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction

Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple Linear Regression models are similarly able to predict biomass, and that associating biometric variables such as number of stalks and plant height with reflectance data can assist model performance, depending on the attributes selected. This indicates that, when estimating biomass in the early stages, sugarcane growers can carry out site-specific management in order to increase yield and reduce the use of inputs.

Determining the K coefficient to leaf area index estimations in a tropical dry forest.

Vegetation indices are useful tools to remotely estimate several important parameters related to ecosystem functioning. However, improving and validating estimations for a wide range of vegetation types are necessary. In this study, we provide a methodology for the estimation of the leaf area index (LAI) in a tropical dry forest (TDF) using the light diffusion through the canopy as a function of the successional stage. For this purpose, we estimated the K coefficient, a parameter that relates the normalized difference vegetation index (NDVI) to LAI, based on photosynthetically active radiation (PAR) and solar radiation. The study was conducted in the Mata Seca State Park, in southeastern Brazil, from 2012 to 2013. We defined four successional stages (very early, early, intermediate, and late) and established one optical phenology tower at one plot of 20 × 20 m per stage. Towers measured the incoming and reflected solar radiation and PAR for NDVI calculation. For each plot, we established 24 points for LAI sampling through hemispherical photographs. Because leaf cover is highly seasonal in TDFs, we determined ΔK (leaf growth phase) and Kmax (leaf maturity phase). We detected a strong correlation between NDVI and LAI, which is necessary for a reliable determination of the K coefficient. Both NDVI and LAI varied significantly between successional stages, indicating sensitivity to structural changes in forest regeneration. Furthermore, the K values differed between successional stages and correlated significantly with other environmental variables such as air temperature and humidity, fraction of absorbed PAR, and soil moisture. Thus, we established a model based on spectral properties of the vegetation coupled with biophysical characteristics in a TDF that makes possible to estimate LAI from NDVI values. The application of the K coefficient can improve remote estimations of forest primary productivity and gases and energy exchanges between vegetation and atmosphere. This model can be applied to distinguish different successional stages of TDFs, supporting environmental monitoring and conservation policies towards this biome.

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources.

Classification methods based on Gaussian Markov Measure Field Models and other probabilistic approaches have to face the problem of construction of the likelihood. Typically, in these methods, the likelihood is computed from 1D or 3D histograms. However, when the number of information sources grows, as in the case of satellite images, the histogram construction becomes more difficult due to the high dimensionality of the feature space. In this work, we propose a generalization of Gaussian Markov Measure Field Models and provide a probabilistic segmentation scheme, which fuses multiple information sources for image segmentation. In particular, we apply the general model to classify types of crops in satellite images. The proposed method allows us to combine several feature spaces. For this purpose, the method requires prior information for building a 3D histogram for each considered feature space. Based on previous histograms, we can compute the likelihood of each site of the image to belong to a class. The computed likelihoods are the main input of the proposed algorithm and are combined in the proposed model using a contrast criteria. Different feature spaces are analyzed, among them are 6 spectral bands from LANDSAT 5 TM, 3 principal components from PCA on 6 spectral bands and 3 principal components from PCA applied on 10 vegetation indices. The proposed algorithm was applied to a real image and obtained excellent results in comparison to different classification algorithms used in crop classification.