Abstract
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 R2 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.
Highlights
Grassland ecosystems play an important role in biodiversity conservation, ecosystem services provision, and the global carbon cycle [1]
The reflectance means from the three Permanent Monitoring Sites (PMS), obtained after applying the correction methods (CM) for the period 2010–2014, were compared (Figures 6–8)
High values of reflectance were obtained by the bands corresponding to the Near Infra-Red (NIR) and Shortwave Infra-Red (SWIR) regions for the three PMS
Summary
Grassland ecosystems play an important role in biodiversity conservation, ecosystem services provision, and the global carbon cycle [1]. Biomass inventory of grasslands has been driven by traditional methods of evaluation, which include extensive field sampling [7,8] Even though these methods are accurate, they are costly, as well as time-and labor-consuming, when large pieces of land have to be covered [9]. With the aim of developing more effective monitoring methods, there have been numerous studies on indirect methods to estimate the biomass of grasslands using remote sensing information [10,11,12]. In this endeavor, optical sensors, radar, and Lidar systems have been used [13]. All these studies have sought to find relationships between grassland structural variables and satellite image spectral data [14]
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