Sensoriamento remoto multiespectral no manejo sítio‑específico da adubação nitrogenada

The objective of this work was to evaluate the use of multispectral remote sensing for site‑specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn‑planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil‑adjusted vegetation index (Savi), optimized soil‑adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R2=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0–2.5 kg), medium (2.5–3 kg), and high (3–3.3 kg).O objetivo deste trabalho foi avaliar o uso de sensoriamento remoto multiespectral no manejo sítio‑específico da adubação nitrogenada. Imagens de satélite do “advanced spaceborne thermal emission e reflection radiometer” (Aster) foram obtidas em uma área de 23 ha cultivados com milho, no Irã. Para a coleta das amostras de campo, foi feita a seleção de 53 pixels, por meio do método de amostragem aleatória sistemática. Avaliou-se o teor de nitrogênio total nos tecidos foliares do milho, nesses pixels. Para estimar o teor de nitrogênio da parte aérea do milho, foram utilizados diferentes índices de vegetação, como “normalized difference vegetation index” (NDVI), “soil‑adjusted vegetation index” (Savi), “optimized soil‑adjusted vegetation index” (Osavi), “modified chlorophyll absorption ratio index 2” (MCARI2) e “modified triangle vegetation index 2” (MTVI2). Utilizou-se a técnica de classificação supervisionada com classificador “spectral angle mapper” (SAM) para a geração do mapa de adubação nitrogenada. O MTVI2 apresentou maior correlação (R2=0,87) e é um bom previsor do conteúdo de nitrogênio no estágio V13, 60 dias após o cultivo. Imagens Aster podem ser utilizadas para prever o status de nitrogênio na parte aérea do milho. Os resultados de classificação indicam três níveis de nitrogênio requerido por pixel: baixo (0–2,5 kg), médio (2,5–3 kg) e alto (3–3,3 kg)

Prostorna analiza električne vodljivosti podzemnih voda pomoću običnoga kriginga i metoda umjetne inteligencije (slučaj ravnice Tabriz, Iran)

rtificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m–1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m–1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.u posljednjih nekoliko desetljeća sustavi umjetne inteligencije (AI) su otvorili nove horizonte u analizi problema vodnog inženjeringa te ekoloških problema. U ovoj studiji istražene su performanse običnog kriginga (OK) kao geostatističkog procjenitelja te performanse dvaju naprednih metoda, prva od kojih je umjetna neuronska mreža (ANN), a druga je hibridni sustav ANFIS (Adaptive Neuro-Fuzzy Inference System) koji uz neuronsku mrežu uključuje i neizravnu (fuzzy) logiku. U tu svrhu, zemljopisne koordinate 120 mjernih bunara lociranih u ravnici Tabriz u sjeverozapadnom Iranu definirane su kao ulazi, a električne vodljivosti (EC) podzemnih voda postavljeni su kao izlazi modela. Osamdeset posto podataka nasumce je izabrano za razvoj i obuku (učenje) navedenih modela, a dvadeset posto podataka iskorišteno je za testiranje i provjeru. Na kraju, izlazi modela su uspoređeni s odgovarajućim mjerenim vrijednostima u mjernim bunarima. Rezultati su pokazali da model ANFIS među svim promatranim modelima daje najbolju točnost s korijenom srednje kvadratne pogreške (RMSE) od 1,69 dS.m–1 i koeficijentom korelacije (R) od 0,84. Izračunate vrijednosti RMSE u modelima ANN i OK iznose 1.97, odnosno 2.14 dS.m–1, a koeficijenata korelacije 0,79, odnosno 0,76, respektivno. Prema dobivenim rezultatima ANFIS metoda je precizno predvidjela električnu vodljivost te se stoga može preporučiti za modeliranje saliniteta podzemnih voda

A PCA-OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization

A historical and future impact assessment of mining activities on surface biophysical characteristics change : A remote sensing-based approach

Mining activities and associated actions cause land-use/land-cover (LULC) changes across the world. The objective of this study were to evaluate the historical impacts of mining activities on surface biophysical characteristics, and for the first time, to predict the future changes in pattern of vegetation cover and land surface temperature (LST). In terms of the utilized data, satellite images of Landsat, and meteorological data of Sungun mine in Iran, Athabasca oil sands in Canada, Singrauli coalfield in India and Hambach mine in Germany, were used over the period of 1989-2019. In the first step, the spectral bands of Landsat images were employed to extract historical LULC changes in the study areas based on the homogeneity distance classification algorithm (HDCA). Thereafter, a CA-Markov model was used to predict the future of LULC changes based on the historical changes. In addition, LST and vegetation cover maps were calculated using the single channel algorithm, and the normalized difference vegetation index (NDVI), respectively. In the second step, the trends of LST and NDVI variations in different LULC change types and over different time periods were investigated. Finally, a CA-Markov model was used to predict the LST and NDVI maps and the trend of their variations in future. The results indicated that the forest and green space cover was reduced from 9.95 in 1989 to 5.9 Km(2) in 2019 for Sungun mine, from 42.14 in 1999 to 33.09 Km(2) in 2019 for Athabasca oil sands, from 231.46 in 1996 to 263.95 Km(2) in 2016 for Singrauli coalfield, and from 180.38 in 1989 to 133.99 Km(2) in 2017 for Hambach mine, as a result of expansion and development of of mineral activities. Our findings about Sungun revealed that the areal coverage of forest and green space will decrease to 15% of the total study area by 2039, resulting in reduction of the mean NDVI by almost 0.06 and increase of mean standardized LST from 0.52 in 2019 to 0.61 in 2039. our results further indicate that for Athabasca oil sands (Singrauli coalfield, Hambach mine), the mean values of standardized LST and NDVI will change from 0.5 (0.44 and 0.4) and 0.38 (0.38, 0.35) in 2019 (2016, 2017) to 0.57 (0.5, 0.47) and 0.33 (0.32, 0.28), in 2039 (2036, 2035), respectively. This can be mainly attributed to the increasing mining activities in the past as well as future years. The discussion and conclusions presented in this study can be of interest to local planners, policy makers, and environmentalists in order to observe the damages brought to the environment and the society in a larger picture.Peer reviewe

Decision-level integration window strategy in satellite imagery-derived land surface temperature disaggregation

The purpose of this study is to present a new approach for satellite imagery-derived Land Surface Temperature (LST) disaggregation based on a decision level integration of various disaggregation strategies. Firstly, common disaggregation models including Global Window Strategy (GWS), Regular Local Window Strategy (RLWS), Object-based Window Strategy (OWS), and Conceptual Window Strategy (CWS) were used for LST disaggregation. Secondly, the Disaggregated LST (DLST) obtained from these strategies were integrated using the Decision-level Integration Window Strategy (DIWS). Finally, the efficiency of different strategies in LST disaggregation was evaluated using actual LST (ALST) maps and Actual Soil Temperature (AST) based on Pearson correlation coefficient (r) and Root Mean Square Error (RMSE). The mean r (RMSE) between ALST and DLST obtained from GWS, CWS, OWS, RLWS, and DIWS were 0.75 (1.87), 0.76 (1.90), 0.76 (1.80), 0.82 (1.38), and 0.89 (1.09 °C), respectively. The RMSE between AST and DLST obtained from these strategies were 3.28, 3.17, 2.87, 2.43, and 2.10 °C, respectively. The results showed that the effectiveness of DIWS in LST disaggregation was higher than other strategies

An Analysis of the Discrepancies between MODIS and INSAT-3D LSTs in High Temperatures

In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high spatial resolution can be used as references for validating geostationary LST products. This study attempted to investigate the discrepancies between a Moderate Resolution Imaging Spectro-radiometer (MODIS) and Indian National Satellite (INSAT-3D) LSTs for high temperatures, focusing on six deserts with sand dune land cover in the Middle East from 3 March 2015 to 24 August 2016. Firstly, the variability of LSTs in the deserts of the study area was analyzed by comparing the mean, Standard Deviation (STD), skewness, minimum, and maximum criteria for each observation time. The mean value of the LST observations indicated that the MYD-D observation times are closer to those of diurnal maximum and minimum LSTs. At all times, the LST observations exhibited a negative skewness and the STD indicated higher variability during times of MOD-D. The observed maximum LSTs from MODIS collection 6 showed higher values in comparison with the last versions of LSTs for hot spot regions around the world. After the temporal, spatial, and geometrical matching of LST products, the mean of the MODIS—INSAT LST differences was calculated for the study area. The results demonstrated that discrepancies increased with temperature up to +15.5 K. The slopes of the mean differences were relatively similar for all deserts except for An Nafud, suggesting an effect of View Zenith Angle (VZA). For modeling the discrepancies between two sensors in continuous space, the Diurnal Temperature Cycles (DTC) of both sensors were constructed and compared. The sample DTC models approved the results from discrete LST subtractions and proposed the uncertainties within MODIS DTCs. The authors proposed that the observed LST discrepancies in high temperatures could be the result of inherent differences in LST retrieval algorithms