Thematic Mapper Sensor Research Articles

LULC changes in the region of the proposed Pwalugu hydropower project using GIS and remote sensing technique

<p>Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.</p>

A Framework of Generating Land Surface Reflectance of China Early Landsat MSS Images by Visibility Data and Its Evaluation

The Landsat time-series dataset is one of the most widely used datasets for land surface research due to its long time-series and Land Surface Reflectance (LSR) product. Though the United States Geological Survey (USGS) provides Landsat LSR products for later Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI), no early Landsat 1–5 Multispectral Scanner System (MSS) LSR product is generated currently, limiting the research traced back to the 1970s. Atmospheric correction is one of the necessary preprocesses for generating LSR products. However, it is challenging for MSS images, not only because the image quality is lower and bands are different compared with the current sensors, but also because of the multiple effects of other preprocesses, such as radiometric calibration. Based on the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) model, we propose a novel framework for generating Landsat 1–5 MSS LSR data of China. Ground-based visibility records are introduced to replace the images-based aerosol optical depth (AOD) to effectively generate MSS LSR data of the 1970s. We evaluate the generated MSS LSR data by the cross-validation of the simultaneous observation of MSS and TM sensors in Landsat 4 and Landsat 5 using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) surface reflectance product as the truth value. The evaluation result shows that the generated MSS LSR data is comparable with the later Landsat TM LSR product, with slightly larger uncertainties. In addition, it shows that the non-atmospheric factors (e.g., the difference of relative spectral responses of TM and MSS, the georegistration errors, the radiometric calibration uncertainty, and image noises) bring larger uncertainties than the atmospheric factors (e.g., the AOD retrieval method by visibility) to the cross-validation results. We apply the MSS LSR data generated by the proposed framework on time series analysis in the regions of interest (ROIs) of the spectral-stable land cover in China for all the MSS sensors. The application demonstrates the potential and promise of the MSS LSR data generated by the proposed framework.

Fire risk associated with landscape changes, climatic events and remote sensing in the Atlantic Forest using ARIMA model

The human influence on climate change has increased the occurrence of extreme events and made heat waves and droughts more frequent and severe, which leads to an increased number of fires. It is likely that in the past, the Atlantic Forest has responded to the occurrence of extreme events with fires that may have contributed to the reduction of forest cover in the state. Just as there is a hypothesis that in the future, with the greater recurrence of these events, the Atlantic Forest will be susceptible to greater risk of forest fires. This study aims to develop a model using the Autoregressive Integrated Moving Average Model (ARIMA) to evaluate the danger of forest fires for past and future climatic periods, in the State of Rio de Janeiro. Images from the Thematic Mapper sensor and the Enhanced Thematic Mapper sensor from 1985 to 2015 were used in order to classify them into anthropized and forest areas. A set of meteorological variables on a daily and monthly scale for the period 1985 to 2015 was used to calculate the F-index on a monthly scale. ARIMA was used to simulate the observed and future F-index data up to 2030. The results show higher values of Normalized Difference Fraction Index (NDFI) in areas to the south and southwest of the State, coinciding with the areas of greater predominance of Atlantic Forest. The most degraded regions are in the northeast and north, and the year 2000 showed the largest area of degraded forest. By analyzing the F-index for the past, it was possible to observe a gradual increase in fires, which were associated with extreme events, mainly La Niña. ARIMA modeling allowed us to identify a class change from high to very high in terms of fire danger in the past and future. In 2030, the minimum value of the F-index reached 2.98, which is considered very high in May and June. By analyzing the entire future period on a monthly scale, the highest fire danger values were found in August and September. It is important that actions be taken to minimize the effects of climate change, once that these changes causes an increase in the occurrence of extreme events, which favor a fire-prone climate, increasing the number of forest fires.

ARTIFICIAL INTELLIGENCE AND ORBITAL IMAGES APPLICATION FOR ANALYSIS OF SPATIAL LAND USE AND COVERAGE PATTERNS

The study aimed to analyze the performance of different machine learning (ML) algorithms in predicting land use and land cover patterns from time series spectral data from Thematic Mapper (TM) and Operational Land Imager (OLI) sensors. The QGIS software was used, where the import of TM / Landsat 5 images began in 2004 and 2009 and OLI/Landsat 8 for 2015 and 2019, to obtain information to characterize and differentiate usage patterns and land cover. Subsequently, training and testing of the algorithms, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naive Bayes (NB), were carried out in the proportions of 80%-20%, 70%-30%, 60%-40% in the KNIME software. The performance was analyzed based on global accuracy and the Kappa index. The RF and SVM for the years 2004 and 2009 showed the best performance (global accuracy), while for the years 2015 and 2019, they were the K-NN and the RF. The Kappa index values indicated that the classifications of the algorithms varied from 0.80 – 1.00. The proportion of 60% (training) and 40% (test) was the one that provided the best results for all the dates analyzed. The data from the pixels sampled from the land use and land cover patterns of the TM and OLI sensor images proved to be efficient for the ML process in the KNIME software.

Study of morphometry and land coverage to help urban planning in Barbosa and Barbosinha Brooks watershed, Lins - SP

Urbanization in hydrographic basins promotes changes in the hydrological cycle through the impermealized areas. Not only surface runoff is increased, reducing infiltration, but also the susceptibility to extreme hydrological events. The objective of this study is to analyze the natural susceptibility of Córregos Barbosa e Barbosinha (Barbosa and Barbosinha Brooks) watershed to flooding. Morphometric parameters of land use and occupation were analyzed, in order to subsidize management and planning in an area of urban expansion. The analysis of land use and occupation was based on the supervised classification method of Landsat-5 Thematic Mapper (TM) sensor and Landsat-8 Operational Land Imager sensor (OLI) satellite images dated 1990, 2006 and 2020. Morphometric indices were calculated using the SPRING 5.4.3 software and a SRTM image with a spatial resolution of 30 meters. The results indicate that the natural susceptibility to flooding of the study area is medium to high, and can be intensified by the dynamics of land use and occupation and increasing impervious areas in the basin. Over the period of study, the growth of impervious areas was 133% relative to 1990.

Evaluation of land use changes in Ardabil province using satellite image processing

By evaluating land use change and land cover, it is possible to plan and land manage to reduce the impact of alteration and degradation of exploitations. The purpose of this study was to evaluate land use changes in Ardabil province between 1987 and 2015 using Thematic Mapper (TM) sensor of Landsat 5 and Operational Land Imager (OLI) sensor of Landsat 8 satellite images. Firstly, the efficiency of the object-based classification method was evaluated using ground control points, error matrix, and comparing total accuracy and kappa coefficients for the resulting maps. Total accuracy and Kappa coefficients for 1987 were 82.3% and 0.70, and 2015 were 94% and 0.90, respectively. Change detection results showed that from 1987 to 2015, rangelands decreased by 4.2%, equivalent to 75039.3 hectares and forests decreased by 0.5%, equivalent to 9393.7 hectares compared to the total province area and irrigation farming increased by 3.1%, equivalent to 55276.8 hectares, residential areas increased by 0.8%, equivalent to 14989.7 hectares and dry farming increased by 0.7%, equivalent to 12632.4 hectares compared to the total province area. The most significant changes that occurred from 1987 to 2015 were the conversion of rangelands to dry farming with 13.8%, equivalent to 141794.2 hectares and irrigation farming lands with 4.8%, equivalent to 49836.9 hectares of rangelands, irrigation farming to dry farming with 7.7%, equivalent to 11464.8 hectares and forests to rangelands with 6.1%, equivalent to 1059.9 hectares which indicate that the requirement of a serious revision of the management policies.

Temporal dynamics change of land use/land cover in Jhansi district of Uttar Pradesh over past 20 years using LANDSAT TM, ETM+ and OLI sensors

Mapping and monitoring land use land cover (LULC) dynamics is vital for sustainable land development, planning and management. Using advanced techniques of remote sensing (RS) and geographic information system (GIS), the present study is an effort to monitor the changes in LULC patterns of Jhansi District of Uttar Pradesh for the periods 2000–2010 and 2010–2020. Satellite images from Landsat-5 Thematic Mapper (TM) for 2001; Landsat-7 Enhanced Thematic Mapper (ETM+) for 2010 and Landsat 8 (OLI) for 2020 were used to extract Land use/Land cover classes. Supervised classification using Maximum Likelihood Classifier (MLC) was applied to monitor LULC of the study area. Six major LULC classes viz; cropland, fallow/barren land, built-up area, water bodies and forest have been identified and showed that major land use in the district is crop land. The accuracy assessment of the classified map was done using 256 random points distributed all over the image. Results indicated that LULC in Jhansi district has undergone a series of changes over the past two decades. Results showed that crop land, built-up area and water bodies have increased by 27.16% (1367.26 Km2), 0.58% (29.4 Km2) and 0.3% (15.26 Km2), respectively while fallow/barren land and forest have decreased by27.55% (1386.58 Km2), and 0.5% (25.34 Km2), respectively. The research revealed that substantial shift from fallow/barren land (27.55%) into crop land. The analysis and findings of this study facilitate researchers, policy makers, land use planners and managers to implement the appropriate land-use management plan for Jhansi district.

Application of CA-Markov Model for the Analysis of Urban Growth in Gumel Town Jigawa State of Nigeria

The land use land cover change and its modeling approach has recently been considered by the scientific community to observe environmental changes. Remote Sensing and Geographical Information System (GIS) give major techniques which can be useful in the analysis at the town locale as well as the city levels. RS data from Thematic Mapper sensor of 1998, 2002, 2010, 2014, and 2018, Operational Land Imager of 2018 were used for the analysis. The study used a supervised classification technique for classification, and Cellular Automata Markovian (CA-Markov) Model analysis was used for future projection of 2038. The result shows that the projected the year 2038 with 0.9079 K-standard value of stimulation, the study further uses Maximum likelihood classifier (MLC) a supervised classification method to classify the images. The study reveals a continuous pattern of urban growth from 3.96 km2 in 2018 to 4.73 in 2038 in terms of settlement growth, dense vegetation has decreased from 11.73 in 2018 to 8.55 in 2038 also shrubland has decreased from 44.82 to 36.30, the last bare land has increased more than all the classes from 162.69 to 173.69. The findings of the present study are useful for planners and decision-makers in sustainable natural resource management.

Mapping past landscapes using landsat data: Upper Paraná River Basin in 1985

During the last decades, the science of remote sensing of the Earth's surface has produced an enormous amount of data. In parallel, with the increase in computational capacity, several classification methods have been applied to the satellite retrievals. This timely combination allows recovering more accurate knowledge about the land cover maps of past times. Therefore, the main goal of this work was to develop a land cover product for the year 1985 in the Upper Paraná River Basin (UPRB-1985), one of the largest and most economically important river basins in the world. The land cover map was developed using a supervised classifier - SVM (Support Vector Machine) applied to data from Landsat TM (Thematic Mapper) sensor. The classification process was carried out based on 52 scenes collected during 1985 and a total of 17,040 training samples across the basin. Pixel and Object-based methods were used to classify Landsat scenes. The generated mapping accuracy was assessed using statistical criteria adopted in the literature - Global Accuracy and Kappa Index. The McNemar's test result showed no significant differences (at the 5% level) between the Pixel-based and Object-based classifications, even with the Object-based classification accuracy was slightly higher (Global Accuracy of 79.8%). However, some relationship between the relief and the classification approach was observed. In sub-basins with high slopes, the mean overall accuracy values of the Pixel-based classification approach were 13.1% higher than the Object-based approach. By mapping past land cover, this work is strategic information to understand ongoing processes, as well as to assess changes in land cover that have occurred over time and evaluate to what extent they explain the variability in the hydrology of the region.

THE URBAN HEAT ISLAND OF PORTO ALEGRE, RS, SOUTHERN BRAZIL: AN ANALYSIS BETWEEN 1985 AND 2019 THROUGH THE RADIATIVE TRANSFER IN THE INFRARED THERMAL

Abstract. The objective of this study was to verify the evolution of surface temperature associated with land use and land cover from 1985 to 2019 in Porto Alegre, RS, Brazil. The methodological procedures were performed in five steps: 1. Definition of the study area; 2. Land use and land cover classification from images of Landsat 5 satellite Thematic Mapper (TM) and Operational Land Imager (OLI) from Landsat 8 satellite; 3. Calculation of surface temperature from TM sensor band 6 and OLI sensor band 10; 4. Analysis of temperature evolution over the historical series; and 5. Temporal relation between surface temperature and land use and land cover classes. The results demonstrated that higher temperatures were associated to the evolution of two classes of land use and land cover: urban area and exposed soil, with the former occupying 31% in 1989 to 75% in 2018 of the study area. When comparing the first and last decade of the historical series for each season, there was an average increase of 4.18 °C in the surface temperature of the districts. Thus, adopting policies that mitigate the effects caused by densification and urban sprawl are necessary, mainly through the conservation of vegetated areas and water reservoirs, as these are crucial for the maintenance of air humidity and evapotranspiration.

Evaluation of Topographic Correction Effects for Landsat-5 Thematic Mapper Images with Complex Lighting Conditions

A number of methods are available for topographic correction of remote sensing data, and various approaches have been proposed for the evaluation of topographic correction effects. However, few studies have evaluated the topographic correction effects for images obtained under all possible sun elevation conditions. This study employed three correction methods, namely, Statistical-Empirical (SE), C-correction (C), and the Sun-Canopy-Sensor + C-correction (SCS + C) methods. These were used for topographic correction of 11 images obtained by the Landsat-5 Thematic Mapper sensor from June to December. An evaluation of the effectiveness of these three methods was carried out based on five evaluation indices, with the goal of investigating the variation in the topographic correction effect with changes in lighting conditions and the adaptability of topographic correction methods to those conditions. The results showed that the SE method effectively reduced the dependence of surface reflectance on the terrain when the solar elevation was greater than 46°, yielding a very stable surface reflectance and a low proportion of outliers. At sun elevation below 46°, the SCS + C method has good correction effect. After topographic correction using these three methods, the correction effect was compromised as the lighting conditions weakened. Within areas where the topographic and geographical conditions are similar to that of this study, the correction effect can be divided into three levels based on the solar elevation while obtaining the images, i.e., > 60°, 50–60° and < 50°. Caution should be taken when conducting quantitative research on images obtained when the solar elevation is < 50°.

Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85).

Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA

The objective of this work is to examine the spatial distribution of Continental Surface Temperature (CST) of the urban area of Belem / PA and the influence of the change of use and soil cover from remote sensing techniques. Products from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) sensors coupled, respectively, to Landsat 5 and 8 satellites were used. The images acquired from the years 1994, 2008 and 2017 were processed, resampled (spatial resolution of 120 meters) and, finally, centroids were extracted with a total of 1252 points, using the Quantum GIS software. Subsequently, spectral indices, NDVI, NDBI and albedo were calculated, which represent, respectively, the presence of vegetation, exposed soil or built area and reflectivity rate. The results showed that CST showed an increase in all sectors of the study area, mainly between the years 2008 and 2017. The sector with the highest elevation of the CST was the urban center, as it presented values below 25.0 ºC in the image of 1994 and above 35.0 ºC in the 2017 image. In contrast, the ecological park sector showed the lowest increase in CST, from 20.0 ºC (1994) to 25.0 ºC (2017). According to the analysis of the spectral indices, the intensification of CST is directly associated with the strong territorial expansion, since from the NDVI values the degradation of the vegetation cover was noted. This degradation is observed in the comparisons of the images, in which it is possible to verify the decrease in the NDVI values in the entire study area, whose values represent the decrease in the vegetation cover. The sector with the greatest withdrawal of green areas was the northern zone, as it showed a drop in NDVI values, from 0.7 in 1994 to 0.3 in the 2017 image. It was also observed that the density of the constructed area was intensified, presenting increasing values of NDBI. Added to these NDVI and NDBI values, higher reflectivity rate values were noted, whose values in the urban center of Belem in 1994 were 0.1% and which exceeded 0.5% in the image for the year 2017, ratifying the impact of changes in land cover and the direct association between changes in the environment and CST. In general, the results indicate that the uncontrolled expansion of the urban process and the change in land cover cause the intensification of CST.

Land use/land cover change dynamics and their effects on land surface temperature in the western region of the state of São Paulo, Brazil

In recent years, the impacts associated with changes in land use and land cover (LULC) over natural processes have received attention. Examining these changes can assist in urban/rural planning activities, temperature change analysis, and environmental monitoring. Land surface temperature (LST) is one of the main physical parameters used to measure the impacts proportioned by LULC changes. Several studies have investigated the implications of LULC changes over LST variations using remote sensing data. However, this dynamic remains unknown in multiple environmentally important areas, like the western region of Sao Paulo state, Brazil, where one last remnant of the Atlantic Forest biome is located. This paper demonstrates that significant LST differences exist in distinct LULC classes. Results indicated that bare soil and pasture areas contribute to increasing LST values, while water bodies and arboreous vegetation attenuate them. As the LULC pattern changes, it reflects on both LST and air temperature. Over a 30-year analysis in the studied area, the LST increased 3.50 °C (0.117 °C per year). This multi-temporal analysis was based on four winter/autumn periods: 1987, 1997, 2007, and 2017, using images from Landsat 5 Thematic Mapper sensor for 1987 to 2007 and Landsat 8 Operational Land Imager and Thermal Infrared Sensor sensors for 2017. A supervised classification was applied to create LULC maps for each period. These findings bring a multi-temporal environmental diagnosis to the region, which is important to optimize future environmental planning actions like the restoration of degraded areas. It is inferred that continuous monitoring of LULC dynamics is required to devise sustainable land use policies in favor of environmental protection and regional economic development.

DINÂMICA DO USO E COBERTURA DO SOLO NA BACIA HIDROGRÁFICA DO RIO MUNIM – MA, 1984 – 2018

O uso de geotecnologias na gestão territorial das bacias hidrográficas, é uma ferramenta que tem se intensificado a cada ano e tornou-se indispensável para o planejamento e manutenção da ocupação territorial na área de abrangência de uma bacia hidrográfica. Esse estudo teve por objetivo avaliar as mudanças na cobertura vegetal da bacia hidrográfica do Rio Munim (BHRM) com auxílio de técnicas de sensoriamento remoto. Foram utilizadas imagens do satélite Landsat 5 e 8 no período entre 1984 a 2018, realizado o processo de classificação supervisionada e validação da classificação por meio de imagem de satélite de alta resolução em ambiente de sistema de informações geográficas (SIG). Percebeu-se a tendência a um crescimento progressivo na área total definida como solo exposto levando em consideração um aumento de 13,23% da área em relação ao ano de 1984. Ao longo dos anos a BHRM perdeu uma quantidade substancial de cobertura vegetal. No ano de 2018 predomina o uso do solo vegetação densa com 40,76%.

Local, Daily, and Total Bio-Optical Models of Coastal Waters of Manfredonia Gulf Applied to Simulated Data of CHRIS, Landsat TM, MIVIS, MODIS, and PRISMA Sensors for Evaluating the Error

The spatial–temporal resolution of remote data covers coastal water variability, but this approach offers a lower accuracy than in situ observations. Two of the major error sources occur due to the parameterization of bio-optical models and spectral capability of the remote data. These errors were evaluated by exploiting data acquired in the coastal waters of Manfredonia Gulf. Chlorophyll-a concentrations, absorption of the colored dissolved organic material at 440 nm (aCDOM440nm), and tripton concentrations measured in situ varied between 0.09–1.76 mgm−3, 0.00–0.41 m−1, and 1.97–8.90 gm−3. In accordance with the position and time of in situ surveys, 36 local models, four daily models, and one total bio-optical model were parameterized and validated using in situ data before applying to Compact High-Resolution Imaging Spectrometer (CHRIS) mode 1, CHRIS mode 2, Landsat Thematic Mapper (TM), Multispectral Infrared and Visible Imaging Spectrometer (MIVIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Precursore Iperspettrale della Missione Applicativa (PRISMA) simulated data. Concentrations retrieved from PRISMA data using local models highlighted the smallest errors. Because tripton abundance is great and tripton absorptions were better resolved than those of chlorophyll-a and colored dissolved organic material (CDOM), tripton concentrations were adequately retrieved from all data using total models, while only local models adequately retrieved chlorophyll-a concentrations and aCDOM440nm from CHRIS mode 1, CHRIS mode 2, MIVIS, and MODIS data. Therefore, the application of local models shows smaller errors than those of daily and total models; however, the capability to resolve the absorption of water constituents and analyze their concentration range can dictate the model choice. Consequently, the integration of more models allows us to overcome the limitations of the data and sensors.

Remote observations with images from landsat satellites to determine the environmental impact of agrarian reform in the Brazilian Midwest between 2004 and 2014.

The Midwest region of Brazil has a high concentration of land and is the primary producer of grains and livestock in the country, activities with a negative impact on environment. Agrarian reform allows redistribution of land and reinforces family agriculture, which is considered to be favorable to environmental protection. The aim of this study was to use field and remote observations to verify the evolution of environmental preservation and land use in rural settlements in the Midwest region of Brazil. 54 settlements distributed in the region were analyzed. We used images from Landsat 5 satellite obtained by the Thematic Mapper sensor in 2004 and images from Landsat 8 satellite from 2014. The NDVI and NDWI vegetation indexes were used to classify urban, agricultural, pasture, savannah and forest areas. Forests declined in all states, however Mato Grosso, the only one with Amazonian forest, where settlements presented lowest compliance with environmental legislation. The evolution indicates the transformation of forest areas into savannah. Settlers predominantly engage in dairy farming, which requires large areas of land and thus exerts significant pressure on the environment. Productive areas are poorly exploited, and better technical assistance could have a significant impact on environmental protection.

Atividade portuária, globalização e crescimento de Rio Grande e São José do Norte - RS - Brasil (1990 - 2010)

No século XXl a globalização tem impactado as relações porto-cidade no que se refere à sua dinâmica de crescimento, fato observado sobretudo em muitos centros urbanos ao redor do mundo. Observam-se novas formas de articulação da economia brasileira com o capitalismo internacional e também distintas caracterizações das cidades médias do país. Este trabalho tem por objetivo investigar o crescimento econômico, quanto ao PIB (Produto Interno Bruto), populacional e espacial a partir da expansão urbana de Rio Grande e São José do Norte no período de 1990-2010. Foram utilizadas imagens LANDSAT 5 do Sensor Thematic Mapper (TM). Os dados foram processados nos softwares Spring e ArcGIS. Como procedimentos também foi realizada pesquisa bibliográfica, no IBGE, FEE/RS (Fundação de Economia e Estatística do Rio Grande do Sul), entrevistas e visita aos locais de expansão. Os resultados demonstram, neste período, a associação entre o fenômeno da urbanização e a atividade portuária, com o crescimento de ambas as cidades. Conclui-se que proporcionalmente São José do Norte expandiu-se mais do que Rio Grande e ressalta-se o papel do Estado nesse processo.

Space-time variability of the Roncador river basin in the change of land use and cover and its correlation with climatic variables

The objective of this study was to evaluate the space-time dynamics of the soil use and occupation of the Rio Roncador river basin between 1985 and 2010. The scenes were classified by two methods (partially unsupervised - K-Means and supervised - Maximum likelihood), the Thematic Mapper sensor products on the LANDSAT 5 orbital platform were used for both images of a 25-year time series (1985 to 2000). In order to measure the accuracy of the field the computer application Google Earth was used, in which nine classes (urban area, agricultural area, pasture, exposed soil, native forest, secondary vegetation, mangrove, altitude field and water) were collected. A multiple linear regression was performed, correlating the Normalized Difference Vegetation Index - mean NDVI (dependent variable) with the independent climatic variables (global solar radiation - MJm-2day-1, average air temperature - °C, relative humidity -%, evapotranspiration - mm d-1, and rain - mm). According to the general classification by Kappa parameter of the images for 2005 and 2010, they were identified as very good (68% and 74%). These results confirm that the Roncador River Basin is undergoing transformation in its landscape, with an average reduction of -49% in native vegetation areas due to the increase in urban areas (25%) and agriculture (31%). The statistical analysis showed that rainfall and air temperature were the only variables that presented significant sigma (0.04) and (0.02). The obtained coefficient of determination indicated that 47% of the variations of the "vegetation index" are explained by the environmental variables.&#x0D;

Dynamic Changes in the Wetland Landscape Pattern of the Yellow River Delta from 1976 to 2016 Based on Satellite Data

The Yellow River Delta wetland is the youngest wetland ecosystem in China’s warm temperate zone. To better understand how its landscape pattern has changed over time and the underlying factors responsible, this study analyzed the dynamic changes of wetlands using five Landsat series of images, namely MSS (Mulri Spectral Scanner), TM (Thematic Mapper), and OLI (Operational Land Imager) sensors in 1976, 1986, 1996, 2006, and 2016. Object-oriented classification and the combination of spatial and spectral features and both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), as well as brightness characteristic indices, were used to classify the images in eCognition software. Landscape pattern changes in the Yellow River Delta over the past 40 years were then delineated using transition matrix and landscape index methods. Results show that: 1) from 1976 to 2016, the total area of wetlands in the study area decreased from 2594.76 to 2491.79 km2, while that of natural wetlands decreased by 954.03 km2 whereas human-made wetlands increased by 851.06 km2. 2) The transformation of natural wetlands was extensive: 31.34% of those covered by Suaeda heteropteras were transformed into reservoirs and ponds, and 24.71% with Phragmites australis coverage were transformed into dry farmland. Some human-made wetlands were transformed into non-wetlands types: 1.55% of reservoirs and ponds became construction land, and likewise 21.27% were transformed into dry farmland. 3) From 1976 to 2016, as the intensity of human activities increased, the number of landscape types in the study area continuously increased. Patches were scattered and more fragmented. The whole landscape became more complex. In short, over the past 40 years, the wetlands of the Yellow River Delta have been degraded, with the area of natural wetlands substantially reduced. Human activities were the dominant forces driving these changes in the Yellow River Delta.