Desarrollo de curvas espectrales del crecimiento anual de la vegetación, usando sensores remotos (original) (raw)
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Journal of Geographical Sciences, 2012
Spatially-explicit estimation of aboveground biomass (AGB) plays an important role to generate action policies focused in climate change mitigation, since carbon (C) retained in the biomass is vital for regulating Earth's temperature. This work estimates AGB using both chlorophyll (red, near infrared) and moisture (middle infrared) based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer (MODIS) and MOD44B vegetation continuous fields (VCF) data. The study area is located in San Luis Potosí, Mexico, a region that comprises a part of the upper limit of the intertropical zone. AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature. Linear and nonlinear (exponential) models were fitted to find their predictive potential when using satellite spectral data as explanatory variables. Highly-significant correlations (p = 0.01) were found between all the explaining variables tested. NDVI62, linked to chlorophyll content and moisture stress, showed the highest correlation. The best model (nonlinear) showed an index of fit (Pseudor 2 ) equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables. Validation correlation coefficients were similar for both models: linear (r = 0.87**) and nonlinear (r = 0.86**).
Agricultural and Forest Meteorology, 2000
A coupled vegetation growth and soil-vegetation-atmosphere transfer (SVAT) model is used in conjunction with data collected in the course of the SALSA program during the 1997-1999 growing seasons in Mexico. The objective is to provide insights on the interactions between grassland dynamics and water and energy budgets. These three years exhibit drastically different precipitation regimes and thus different vegetation growth.
Remote Sensing
A key step in the processing of satellite imagery is the radiometric correction of images to account for reflectance that water vapor, atmospheric dust, and other atmospheric elements add to the images, causing imprecisions in variables of interest estimated at the earth's surface level. That issue is important when performing spatiotemporal analyses to determine ecosystems' productivity. In this study, three correction methods were applied to satellite images for the period 2010-2014. These methods were Atmospheric Correction for Flat Terrain 2 (ATCOR2), Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Dark Object Substract 1 (DOS1). The images included 12 sub-scenes from the Landsat Thematic Mapper (TM) and the Operational Land Imager (OLI) sensors. The images corresponded to three Permanent Monitoring Sites (PMS) of grasslands, 'Teseachi', 'Eden', and 'El Sitio', located in the state of Chihuahua, Mexico. After the corrections were applied to the images, they were evaluated in terms of their precision for biomass estimation. For that, biomass production was measured during the study period at the three PMS to calibrate production models developed with simple and multiple linear regression (SLR and MLR) techniques. When the estimations were made with MLR, DOS1 obtained an R 2 of 0.97 (p < 0.05) for 2012 and values greater than 0.70 (p < 0.05) during 2013-2014. The rest of the algorithms did not show significant results and DOS1, which is the simplest algorithm, resulted in the best biomass estimator. Thus, in the multitemporal analysis of grassland based on spectral information, it is not necessary to apply complex correction procedures. The maps of biomass production, elaborated from images corrected with DOS1, can be used as a reference point for the assessment of the grassland condition, as well as to determine the grazing capacity and thus the potential animal production in such ecosystems.
Geofocus Revista Internacional De Ciencia Y Tecnologia De La Informacion Geografica, 2012
The management of forests as carbon (C) reservoirs could be a valid strategy for mitigating global climate change. In Salta, Argentina, there is an urgent need for updated information on biomass stocks in order to assess the C sequestering and release made by native forests. We studied three ecosystems (Chaco, Yungas and shrubland) by combining different data: a) field-estimated above-ground biomass (AGB); b) field-spectral data, and c) spectral data from remote sensing. AGB was estimated through allometric equations. Radiometric measurements were synthesized into a set of spectral vegetation indices (VI). The satellite data was calibrated with those obtained through field radiometry, allowing us to find a predictive AGB model which indicates an AGB average of 85 ± 250 t.ha-1 for the center of the province of Salta. The model which was finally selected increases the level of estimate detail made at the national level and will allow the monitoring of such data.
2011
The use of spectral indexes or vegetation VI, based on iso-LAI lines in the spectral space of red (R) and near infrared (NIR), presents different relations in vegetative and senescence periods with biophysical variables such as aerial biomass or Bm, the index of foliar area or LAI, or aerial vegetation cover or fv. With the ISVI index based on isosoil lines of the R-NIR space, the temporal patterns may be identified by an expo-linear and exponential model. Thus, the slopes for the vegetative phase and for senescence can be used to quantify the stress level utilizing the concept of environmental equivalence. This scheme of characterization was analyzed with radiometric and fv measurements in crops of bean (Phaseolus vulgaris L.), chickpea (Cicer arietinum L.), safflower (Carthamus tinctorius L.), sorghum (Sorghum bicolor, L.Moench), and wheat (Triticum spec. L.) of the Valle del Yaqui, State of Sonora, México. The obtained results support the approximation of the characterization proposed for the stress level.
Journal of Arid Land, 2018
Estimation of above-ground biomass is vital for understanding ecological processes. Since direct measurement of above-ground biomass is destructive, time consuming and labor intensive, canopy cover can be considered as a predictor if a significant correlation between the two variables exists. In this study, relationship between canopy cover and above-ground biomass was investigated by a general linear regression model. To do so, canopy cover and above-ground biomass were measured at 5 sub-life forms (defined as life forms grouped in the same height classes) using 380 quadrats, which is systematic-randomly laid out along a 10-km transect, during four sampling periods (May, June, August, and September) in an arid rangeland of Marjan, Iran. To reveal whether obtained canopy cover and above-ground biomass of different sampling periods can be lumped together or not, we applied a general linear model (GLM). In this model, above-ground biomass was considered as a dependent or response variable, canopy cover as an independent covariate or predictor factor and sub-life forms as well as sampling periods as fixed factors. Moreover, we compared the estimated above-ground biomass derived from remotely sensed images of Landsat-8 using NDVI (normalized difference vegetation index), after finding the best regression line between predictor (measured canopy cover in the field) and response variable (above-ground biomass) to test the robustness of the induced model. Results show that above-ground biomass (response variable) of all vegetative forms and periods can be accurately predicted by canopy cover (predictor), although sub-life forms and sampling periods significantly affect the results. The best regression fit was found for short forbs in September and shrubs in May, June and August with R 2 values of 0.96, 0.93 and 0.91, respectively, whilst the least significant was found for short grasses in June, tall grasses in August and tall forbs in June with R 2 values of 0.71, 0.73 and 0.75, respectively. Even though the estimated above-ground biomass by NDVI is also convincing (R 2 =0.57), the canopy cover is a more reliable predictor of above-ground biomass due to the higher R 2 values (from 0.75 to 0.96). We conclude that canopy cover can be regarded as a reliable predictor of above-ground biomass if sub-life forms and sampling periods (during growing season) are taken into account. Since, (1) plant canopy cover is not distinguishable by remotely sensed images at the sub-life form level, especially in sparse vegetation of arid and semi-arid regions, and (2) remotely sensed-based prediction of above-ground biomass shows a less significant relationship (R 2 =0.57) than that of canopy cover (R 2 ranging from 0.75 to 0.96), which suggests estimating of plant biomass by canopy cover instead of cut and weighting method is highly recommended. Furthermore, this fast, nondestructive and robust method that does not endanger rare species, gives a trustworthy prediction of above-ground biomass in arid rangelands.
Using AVHRR data for quantitive estimation of vegetation conditions: Calibration and validation
Advances in Space Research, 1998
NDVI-derived Vegetation Condition Index (VCI) was compared with vegetation density, biomass and reflectance measured in the fields. The VCI numerically estimates fluctuation of NDVI related to in&a-annual weather change only and is a measure of weather impact on vegetation. Test fields were located in different climatic (annual precipitation 150-700 mm) and ecological zones (semi-desert to steppe-forest) with elevation from 200 to 700 m in Kazakhstan. A range of NDVI variation was from 0.05 to 0.47. The determination coefficient between AVHRRderived vegetation state and actually measured vegetation density of more than 0.76 was achieved. For the first time it was shown that the VCI-derived vegetation condition data can be effectively used for quantitative assessments of both vegetation state and productivity (density and biomass) over large areas.
Review of spectral vegetation indices and methods for estimation of crop biophysical variables
Aerospace Research in Bulgaria, 2017
In present article a brief overview is presented on spectral vegetation indices and methods for estimation of crop main biophysical variables and their proxies. The main VIs used in estimation of nitrogen and chlorophyll, biomass, LAI and fAPAR, fCover, and photosynthesis are summarized. Biophysical variables and vegetation indices A number of techniques have evolved to derive the biophysical variables of vegetation using remote sensing data; these can be grouped into three broad categories: the inversion of radiative transfer models [39], machine learning (for example neural networks) [4] and the use of vegetation Indices. There are generally few ways of deriving the biophysical estimates using empirical or semi-empirical relationships: 1) single regression; 2) stepwise linear regression; 3) partial least squares (PLS) regression; 4) artificial neural networks [12]. Methods based on vegetation indices (VIs) have the benefit of being computationally simple while they are generally less site specific and more universally applicable than the other methods. The performance of the different indices and selected "optimal" wavebands depends on vegetation and land cover type, the variables to be retrieved, sun/view geometry to name but a few [12]. Satellite spectral data has the potential to measure the reflected radiation from many plants, thus making assessment of biophysical variables feasible on canopy level. The regression models relate in situ measurements and VIs. The VIs are mathematical transformations of the original spectral reflectance that are designed to reduce the additive and multiplicative errors associated with atmospheric effects, solar illumination, soil background effects, and sensor viewing geometry [29].