NDVI Spatial Temporal Vegetation

This article uses methods for predicting plant performance indicators in Kazakhstan. In the work, using deep learning, visualization of predicted indicators (indicators and others), statistics from predicted values and identified changes, time series have been developed. Sentinel satellite data and statistical indicators for the last few years for the agricultural territories of Kazakhstan are used as primary data. It is found that the upward trend in wheat quality, however, increases the size of fertilizers, variables based on the NDVI also significantly contribute to the forecasting model. It has been shown that the amount of applied fertilizer has stabilized in the past few years due to economic and environmental constraints, so NDVI-based models will become increasingly important for enhancing forecasting models. Four machine learning algorithms have been evaluated and compared, namely boosted regression trees (BRT) and support vector machine (SVM), to map and predict the field yield of the Experimental Oil Farm in East Kazakhstan using readily available additional data. Based on the results of the work, a forecast of crop yields and general statistical recommendations for increasing yields were obtained.

RESUMEN Se evaluaron los conflictos de uso que se presentan dentro del Parque Nacional Cerro Saroche, se analizó su vegetación a través del Índice Diferencial Normalizado de Vegetación (NDVI), mediante imágenes satelitales, para cuantificar el impacto ambiental de la pérdida de cobertura vegetal por conflictos de uso. Posteriormente, se procedió a analizarlos a través de un árbol de problemas y un árbol de objetivos de donde surgen lineamientos estratégicos para la gestión del Parque. El estudio concluyó con la evaluación de los conflictos de uso dentro del Parque Nacional, que en 2016 ocupaban una superficie de 718,59 ha, mientras que en el año 1991 era de 124,30 ha. Para el año 2016 el impacto ambiental por pérdida de la cobertura vegetal fue negativo y de nivel moderado, razón por la cual, como resultado de la investigación se propusieron cinco lineamientos estratégicos para mejorar la gestión institucional integral del Parque Nacional. ABSTRACT Se evaluaron los conflictos de uso que se presentan dentro del Parque Nacional Cerro Saroche, se analizó su vegetación a través del Índice Diferencial Normalizado de Vegetación (NDVI), mediante imágenes satelitales, para cuantificar el impacto ambiental de la pérdida de cobertura vegetal por conflictos de uso. Posteriormente, se procedió a analizarlos a través de un árbol de problemas y un árbol de objetivos de donde surgen lineamientos estratégicos para la gestión del Parque. El estudio concluyó con la evaluación de los conflictos de uso dentro del Parque Nacional, que en 2016 ocupaban una superficie de 718,59 ha, mientras que en el año 1991 era de 124,30 ha. Para el año 2016 el impacto ambiental por pérdida de la cobertura vegetal fue negativo y de nivel moderado, razón por la cual, como resultado de la investigación se propusieron cinco lineamientos estratégicos para mejorar la gestión institucional integral del Parque Nacional.

There is an algorithm presented that uses multispectral images as a result of the processing of images and the MATLAB software. These multispectral images could be applied on cacao and coffee crops which not only are the most representative type of the region of Santander, but also the ones with most economic growth on the last two decades. The strategy of deploying these images, also help to demarcate the dried lands from the better hydrated ones, which is crucial to develop Precision Farming since it makes it more productive, with less losses and with better benefits. The graphical user interface (GUI) was developed in order to facilitate the interaction between the software and the user. On this graphical interface, it’s possible to identify the varied applications that could be run and recognized. It is worth mentioning that this process has several stages. it begins from the analysis of a satellite images data base, which stablish the characteristics and condition of the crops; from it, it’s possible to obtain a more accurate and detailed radiogram that illustrates the reality of each crops.

A munkám során Szabadka vegetációját térképezem műholdas távérzékelés eszközeivel. Ezen a vizsgálat alatt arra kerestem a választ, milyen mértékben változott a vegetáció 1985-től 2018-ig és mekkora ennek a mértéke városrészenként

Három különböző típusú műholdas felvételt használtam, ezek a: Landsat 5, Landsat 8 és a Sentinel 2. Segítségükkel térképeket készítettem, amelyek kizárólag Szabadka belterületére vonatkoznak. Normalizált vegetációs index használtam, mely segítségével detektálták a növényzetet és így következtettem a beépítettség mértékére és zöld övezetek nagyságára. Ezután az értékeket elemeztem és a változásokat térben és időben ábrázoltam. Fontos megjegyzés, hogy ez a tanulmány nem tartalmaz olyan más jellegű számítást, mint például SAVI (talaj vegetációs index), WUDAPT (egy nemzetközi közösség által generált városi lombkorona-információs és modellező infrastruktúra), Tasseled cap transzormáció, amely segítségével tovább pontosítható az adatok pontosságát.

Következtetésként megállapítottam, hogy a teljes területhez képest (amely 17 km2) a zöldfelületek nagyságát 12 km2, tovább kiszámoltam a belvárosi helyi közösségek zöldfelületi nagyságát is. A zöldfelületek kiterjedésével következtethető az egy főre eső zöldfelületek, ahol a legutolsó, azaz 2010-es lakossági statisztikát használtam. Az egy főre eső zöldfelületek 2018-ban 201 fa/m2. Ugyanazzal az eljárással a helyi közösségeknek definiáltam az egy főre eső zöldfelületeket, illetve konkluzióként megállapítható az egészséges élethez szükséges zöldfelületeket is. Végén a zöldövezet fejlesztésére tettem javaslatokat.

Kulcsszavak: városi vegetáció, vegetációs index, NDVI, egy főre eső zöldfelület, Szabadka

To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R 2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings-providing a tool for tracking and monitoring nutrient management effects-not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

Water birds reflect the habitat integrity of a wetland. Birds tend to stay longer in areas that sustain food, shelter, and other life-support systems. In Candaba Swamp, migratory birds dwindle over the last ten years. Only 1,449 were recorded in 2018, the lowest since 2010 with 13,160 birds. This is critical because the swamp serves as a major refuge habitat during Siberian cold winds for many species of migratory and local birds. Land use change and water loss worsen the swamp's diminishing habitat integrity. Moreover, difficulty in monitoring due to the wetland's vastness and inaccessibility remains a challenge. However, satellite-derived information like Landsat provides a cheaper and easier method in studying the vegetation dynamics and seasonal water occurrence of wetlands. This research attempts to improve the habitat integrity of Candaba Swamp by identifying key conservation and preservation areas in the bird sanctuary. By doing so, landscape architecture intervention and spatial analysis advance. Using Google Earth Engine and Landsat data, the vegetation dynamics and seasonal water occurrence were studied. Specifically, Normalized Difference Vegetation Index (NDVI) dataset from 2013-2019, and Global Surface Water (GSW) information from 1984-2015 were utilized to perform the task. A total of 38 potential habitat zones were explored by overlaying the existing habitat patches over the collected multi-spectral vegetation profile (NDVI) and seasonal water occurrence dataset (GSW). Analyses suggested the implementation of more strict preservation measures in the existing bird sanctuary (patch 5, 4, 3). The GSW dataset also revealed that patch 33,34,35, and 36 hold water resources that may serve as new nesting and breeding grounds for birds. However, these patches also record high water loss over the years. Lastly, a stronger observance of land use change particularly in lieu of agricultural farms and residential areas expansions were noted in patch 38, 29, 22, 20, 34, 36. This study advances the convergence of satellite-derived information as cutting-edge tool in monitoring ecological functions. The process reveals the use of multi-spectral dataset in Google Earth Engine in informing new policies and improving ecological functions.

The study aimed at elucidating the possible relationship  between the complex, cyclical variations in solar/sunspot activity (external forcing) and its effect on the global
terrestrial vegetation behavior, as shown in the multitemporal (1982-1999) National Oceanic and Atmospheric Administration - Advanced Very High Resolution Radiometer (NOAA-AVHRR) normalized difference vegetation index (NDVI) data. It also sought to reveal the solar signal that is postulated to be latent in multitemporal NDVI data.
Further, the research also aimed at establishing the relationship between internal forcings, mainly multitemporal climatic phenomena such as the El Niño-Southern Oscillation
(ENSO), the Pacific Decadal Oscillation (PDO) and  multitemporal NDVI.
Pre-processing techniques for noise removal in satellite data were applied. These consisted of the one-dimensional Polynomial Least Squares Operation (PoLeS) for the
reconstruction and estimation of annual NDVI phenological curve; the Two-dimensional Polynomial Least Squares Operation (PoLeS) for noise removal in satellite data; and the application of Singular Spectrum Analyses (SSA) also for the reconstruction and estimation of the annual NDVI  phenological curve. Advanced and novel time-series spectral analysis techniques (Power Spectrum) namely, the Blackman-Tukey (BT), Maximum Entropy Spectral Analysis (MESA), were applied on time series NDVI, climatic, and solar data, to extract latent information and characterize the data by their temporal power spectral curves. Multitemporal NOAA-AVHRR NDVI consisting of continental datasets of
Africa, South America, Australia and northern boreal datasets, were pre-processed using the Two-dimensional PoLeS, and analyzed using power spectrum techniques.
Temporal spectral analyses on and correlations between NDVI and several multitemporal climatic/oceanographic and extra-terrestrial (solar) phenomena were conducted, namely, the PDO, the ENSO, Zurich Sunspot counts, and reconstructed, top of the atmosphere phototosynthetically active radiation (PAR). Low frequency, decadal oscillations were observed in the NDVI datasets and in a variety of climatic phenomena, hinting both internal and possible external modulation or forcing. Significant decadal-scale, low frequency coherency between multitemporal NDVI
(termed in this context as, “Terrestrial Decadal Oscillations” or the TDO which has never been described in scientific literature) and in all climatic, as well as solar phenomena have been observed. These coherent signals are centered at frequencies of around ~9.259 years (~0.009 cycles/month) which may be a possible evidence for strong insolation (changes in the duration and intensity of solar radiation) of the global climate.

The peculiar and seemingly ubiquitous decadal periodicity can be seen in other environmental proxies, and all seem to point to a common solar forcing origin. Further, the results of this study corroborates earlier findings of linked, internal
modulation of terrestrial vegetation behavior by various oceanographic and climatic  phenomena, as well as externally and possibly, by some form of solar forcing.

NDVI adalah nilai Normalized Diffrerence Vegetation Index, NIR adalah band 5 citra Landasat 8 dan Red adalah band 4 dari citra Landsat 8. Untuk menentukan nilai kerapatan tajuk vegetasi menggunakan hasil dari perhitungan NDVI, kemudian nilai kelas NDVI tersebut diklasifikasi ulang (reclass) menjadi tiga kelas, yaitu kerapatan jarang, sedang dan rapat.

Mangrove vegetation is one of the forest ecosystems that offers a potential of substantial greenhouse gases (GHG) emission mitigation, due to its ability to sink the amount of CO2 in the atmosphere through the photosynthesis process. Mangroves have been providing multiple benefits either as the source of food, the habitat of wildlife, the coastline protectors as well as the CO2 absorber, higher than other forest types. To explore the role of mangrove vegetation in sequestering the carbon stock, the study on the use of remotely sensed data in estimating carbon stock was applied. This paper describes an examination of the use of remote sensing data particularly Landsatdata with the main objective to estimate carbon stock of mangrove vegetation in Perancak Estuary, Jembrana, Bali. The carbon stock was estimated by analyzing the relationship between NDVI, Above Ground Biomass (AGB) and Below Ground Biomass (BGB). The total carbon stock was obtained by multiplying the total biomass with the carbon organic value of 0.47. The study results show that the total accumulated biomass obtained from remote sensing data in Perancak Estuary in 2015 is about 47.20±25.03 ton ha-1 with total carbon stock of about 22.18±11.76 tonC ha-1and CO2 sequestration 81.41±43.18 tonC ha-1.

Keywords: Perancak Estuary, carbon stock estimation, mangrove, CO2 sequestration, NDVI

The Science, progress, the technique and technology can (and must) be put at the service of Humanity, of the improvement of its conditions of existence. In the face of the environmental problems, many seek to find technological solutions capable of transcending the limits of our current model. The environmental concerns and environmental issues are increasingly strong, and today it is consensus that our current model of development must be transformed if it wishes to avoid the multiplication of environmental problems. But the technology, can it truly respond to the ecological issues? , How this question structure-t-it the ecological debate? In this second edition of the international symposium "Science and Technology at the service of the Environment" (STE'18), which will be held on 23rd and 24th June 2018 at the Faculty of Sciences of Tetouan, Morocco, you will have the opportunity to disseminate your research and to share experiences with other research experts in all areas in relationship with the environment. This international symposium will address a range of important themes in all major areas of activity. The subjects of the International Symposium STE’18 cover a wide spectrum of the environment and its application in all areas of science, namely: - Biomolecules and Environment - Biotechnology and Sustainable Development - Climate Change and Challenges for the Environment of Tomorrow - Conservation, Valorization and Exploitation of Natural Resources - Smart City and Objects Conn - Technological innovation at the service of the Environment.

The Mimbres region of southwest New Mexico is ideal for investigating the coevolutionary relationships among human impact, ecosystem processes, and settlement stability during the development of sedentism and agriculture. A resilience framework guides interpretations of nuanced relations among land use strategies and impacts through an examination of ecosystem processes around Mimbres pithouse sites. Analytical methods include multitemporal analysis of satellite imagery to measure vegetation response to precipitation variability and GIS-based spatial analyses to examine differences in ecosystem response for prehistoric sites and adjacent unoccupied locations in similar ecological settings. Palynological and architectural analyses provide independent means of assessing prehistoric impact and settlement choices (respectively).

To evaluate the relative contribution of prehistoric selection and impact on occupation length, models are developed for “choice” – ecosystem characteristics that attracted initial settlement, and “impact” – modification to ecosystems resulting from prehistoric land use. Choices reflecting productivity maximization find support; prehistoric impact played a smaller role. Pollen data suggest even small prehistoric occupations shifted plant communities towards r- strategist taxa, but impacts were not long-lived, and legacy effects of Mimbres landuse on modern ecosystem processes may be minor.

Some Mimbres settlements had large, long occupations (villages), but most settlements were small and only briefly occupied (farmsteads). Greater architectural investment in early houses at villages than at farmsteads suggests occupation length was planned during initial construction. Satellite data indicate locations for each type of settlement were selected based on the way vegetation responds to precipitation variability. Farmsteads were constructed where pluvial effects last longer, but the effects of drought are more severe. Shorter use of these settlements does not reflect failed agricultural strategies or deleterious impacts. In contrast, villages were built where drought effects are less severe, but pluvial benefits more brief, reflecting risk-minimizing strategies.

Mimbres settlement strategies were partly based on sophisticated ecological knowledge. Sustainable occupation at a regional scale, reflected in the continued use of the landscape, was derived from local decisions about the kinds of settlements to construct in specific places. Their settlement system reflects strategies of land use targeted to specific types of ecosystems and climate regimes.

Estimativas de áreas agrícolas precisam de ser acertadas e objectivas e em tempo hábil de modo a serem usadas adequadamente pelos tomadores de decisão do sector agrário. Apesar da agricultura ser a principal actividade em Moçambique a estimativa dessas áreas é comummente baseada em métodos subjectivos. O principal objectivo desse estudo é de propor um método para estimar áreas agrícolas usando séries temporais dos índices de vegetação EVI (Índice de Vegetação Realçado) e NDVI (Índice de Vegetação por diferença Normalizada) do sensor MODIS a bordo do satélite Terra.
O estudo foi realizado no distrito de Moamba para um período de  11 anos agrícolas (2001/02 a 2011/12), as imagens dos índices de vegetação foram suavizadas com sucesso pelo filtro de média móvel, e os pontos extremos para a criação dos mapas de áreas agrícolas foram encontrados a partir da análise derivativa. O EVI teve melhor performance que o NDVI, a área estimada através do EVI foi comparada com o mapa temático oficial produzido por MINAG (2007) usando a curva de características de operação do receptor (ROC). A análise ROC e os resultados estatísticos são encorajadores. O EVI/MODIS estimou uma área de aproximadamente 77% em relação aos dados oficiais, Entretanto este método mostrou ter grande potencial para estimar áreas agrícolas permanentes com a vantagem de utilizar imagens disponibilizadas gratuitamente. Recomenda-se a aplicação deste método em diferentes áreas e com dados de campo para maior acurácia.

Lineaments have a genetic relationship with the formation of various topographic features, such as cliffs, slope breaks, narrow linear ridges, gorges and stream valley segments. The present study investigates the linkage between lineament, vegetation and groundwater. It explains an approach for lineaments extraction using shaded relief images from CartoDEM. The study area is Ranchi District, Jharkhand (India). Based on azimuth angle of the light source two shaded relief images (Lineament Analysis A with azimuth angles 0°, 45°, 90°, 135° and Lineament Analysis B with azimuth angles 180°, 225°, 270°, 315°) are created. Mann-Kendall (MK) method was used for positive and negative trend analysis in weather data over the study area. The proposed methodology is focused mainly on relationships between the extracted lineaments and multiple vegetation indices. Multiple vegetation indices on include the following: Difference Vegetation Index (DVI), Green Difference Vegetation Index (GDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI) and Green Ratio Vegetation Index (GRVI), are generated from LANDSAT 5 Thematic Mapper (TM) satellite image. The NDVI and RVI maps had moderate correlation (R2) with lineament analysis A and B extracted from multi-directional images. Finally, results were validated for the study area by using Gravity Recovery and Climate Experiment (GRACE), and groundwater level data. Groundwater data shows indicative correlation with lineament maps (A & B). This analysis demonstrates the potential applicability of this methodology.

Konya  Kapalı  Havzasındaki  doğal  kaynakların  etkin  ve  sürdürülebilir  bir  şekildekullanılmasınısağlamak,  tarım  faaliyetlerinde sürdürülebilirliği sağlamak amacıyla bu çalışmakapsamında; Landsat 5-TM görüntüleri yardımıyla 28 yıllık bir periyotta  bölgenin yüzey sıcaklık,  kuraklık haritaları oluşturulmuş. Proje kapsamında 1984, 1989, 1998, 2003, 2007 ve 2011 yıllarına ait  29 adet Landsat-5 TM uydu  görüntüsü temel veri kaynağı olarak kullanılmıştır. Bu uydu görüntüleri öncelikle geometrik ve radyometrik olarak düzeltilmiştir. Daha sonra, düzeltilmiş uydu görüntüleri ile 1984, 1989, 1998, 2003, 2007 ve 2011 yıllarına ait 5 tam çerçeve TM görüntüleri kullanılarak 6 yıla ait mozaik uydu görüntüleri elde edilmiştir. Elde edilen  bu görüntülerden indeks görüntülerinin oluşturulması  aşamasına  geçilmiştir.Oluşturulan Arazi  yüzeyi sıcaklığı(AYS), Bitki  Durum  İndeksi  (BDİ), Sıcaklık –Bitki İndeksleri(SBİ)değerlendirildiğinde  sonuç  olarak  Konya  kapalı  Havzasında  1984  ve  2011  yılları  arasında  yüzey  sıcaklık değerlerinin 2.00-3.00 °C arasında yükseldiği saptanmış. BDİ’ ne göre belli bölgelerde yaz kuraklığı etkisinde artış olduğu, son yıllardaki  yağışlardaki  artış  bölgenin  nefes  almasını  sağlamış  olsa  da  yeni  bir  kurak  dönemin  gelmesi  çok  muhtemel  olarak gözüktüğü söylenebilir.

The main purpose of this paper is to investigate multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8-OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8-OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.

Mean monthly Normalized Difference Vegetation Indices (NDVI) from 1982 to 1999 were derived from NOAAAVHRR  data and compared with coincidental multi-temporal mean monthly Pacific Decadal Oscillation (PDO) datasets. Power spectra for both datasets were derived using the Maximum Entropy Spectral Analysis (MESA) technique. Three  continental test datasets were investigated namely, continental Africa, South America, and Australia. Initial results indicate high degree of coherency between NDVI and PDO power spectra. Low frequency coherency in the order of tri-decadal (30-year), 15-year, decadal and sub-decadal nature have been observed in all datasets. These Terrestrial Decadal Oscillations (TDO) seem to indicate to be modulated by the PDO based on the behaviour of the multi-year NDVI, as well as the former and the latter, by some form of solar forcing. Given these initial results of spectral coherency between NDVI and PDO, the former could be used as an environmental proxy for detection of climatic oscillations particularly that of the PDO.

Um sítio urbano tem peculiaridades devido ao uso da terra, a intensa urbanização,
vegetação, e dentre outras variáveis naturais. Com o uso do sensoriamento,
multitemporal, é possível analisar como certas variáveis influenciam no Clima Urbano
de Ponta Grossa-PR. Obtendo o NDVI, índice de vegetação normalizado e a TST,
temperatura da superfície terrestre, então entendida como balanço de energia e então
correlaciona-las.

Variable rate technology offers a sustainable, efficient, and cost-effective solution for fertilizer application. A study was conducted to design and develop a variable rate fertilizer applicator to detect real-time deficiency of N within the field and apply it per requirement of the crop. The microcontroller system was designed to receive a signal from the N sensor and send a signal to the pulse-width-modulation valve to vary the rotational speed of the hydraulic motor resulting in variation in the rotation of the metering mechanism drive shaft based on the recommended amount of fertilizer. During the field study, three replications were conducted, each of which was divided into four plots. The response time between the N sensing and fertilizer discharging fell within the range of 3.49 to 4.90 s. Fertilizer applied using the developed variable rate applicator indicated that when the fertilizer rate is increased from N1 to N4 (kg ha−1), NDVI increased from 0.56 to 0.78 and drive sha...

tAn agronomic research was conducted in Tuscany (Central Italy) to evaluate the effects of an advancedirrigation system on the water use efficiency (WUE) of a tomato crop and to investigate the ability of soiland vegetation spectroradiometry to detect and map WUE. Irrigation was applied following an innovativeapproach based on CropSense system. Soil water content was monitored at four soil depths (10, 20,30 and 50 cm) by a probe. Rainfall during the crop cycle reached 162 mm and irrigation water appliedwith a drip system amounted to 207 mm, distributed with 16 irrigation events. Tomato yield variedfrom 7.10 to 14.4 kg m−2, with a WUE ranging from 19.1 to 38.9 kg m−3. The irrigation system alloweda high yield levels and a low depth of water applied, as compared to seasonal ET crop estimated withHargraves’ formula and with the literature data on irrigated tomato. Measurements were carried outon geo-referenced points to gather information on crop (crop yield, eighteen Vegetation indices, leafarea index) and on soil (spectroradiometric and traditional analysis). Eight VIs, out of nineteen onesanalyzed, showed a significant relationship with georeferenced yield data; PVI maps seemed able toreturn the best response, before harvesting, to improve the knowledge of the area of cultivation andirrigation system. CropSense irrigation system reduced seasonal irrigation volumes. Some vegetationindexes were significantly correlated to tomato yield and well identify, a posteriori, crop area with lowWUE; spectroradiometry can be a valuable tool to improve irrigated tomato field management.

Resumo: Processos erosivos são eventos naturais, porém podem ser acelerados pelo uso incorreto do solo na produção agrícola. Nessa perspectiva, a utilização de modelos preditivos e georreferenciados para quantificar e classificar a suscetibilidade erosiva vem se tornando uma importante ferramenta na compreensão dos processos e, consequentemente, no planejamento do uso adequado dos solos. A região norte paranaense é caracterizada pela grande predisposição ao cultivo de lavouras temporárias, destacando-se o município de Bela Vista do Paraíso que apresenta como substratos geológicos predominantes rochas eruptivas basálticas, pertencentes à Formação Serra Geral (JKsg) e rochas areníticas da Formação Adamantina (Ka), sendo estas a gênese de material para os solos encontrados no município. Desta forma a presente tese demonstra em três estudos, onde a erosividade dos solos de diferentes litologias é avaliada, a utilização do NDVI (Índice de Vegetação por Diferença Normalizada) mensal e posterior conversão para o fator Cr e a utilização do modelo preditivo da Equação Universal de Perda de Solos Revisada (RUSLE – Revised Universal Soil Loss Equation), em análises mensais, para a estimativa de perda e degradação dos solos na área de estudo. No primeiro estudo estimou-se o fator de erodibilidade dos solos (fator K) a partir da metodologia de Boyoucos (1935), identificando que solos formados em rochas areníticas se encontram em classes alta a muito alta de erodibilidade, enquanto os formados em rochas basálticas têm valores menores devido ao incremento substancial de argila nos horizontes superficiais. Também se constatou que a metodologia poderia apresentar uma subestimação dos valores, acarretando uma análise inconsistente das classes de erodibilidade. No segundo estudo objetivou-se estabelecer as classes de uso e manejo dos solos e práticas conservacionistas de caráter mensal, utilizando-se o cálculo NDVI, proposto por Rouse et al. (1973), com imagens do satélite Sentinel-2. Posteriormente, houve uma conversão para o fator Cr, proposto por Durigon et al. (2014), para que não apresentassem valores negativos e apontassem valores mais próximos da realidade da cobertura vegetal da região dos trópicos. Foram identificadas três fases distintas quanto aos seus usos: sendo uma fase de pousio, em que o solo se encontra com baixo índice de cobertura vegetal ou até mesmo nula; uma fase intermediária, com propriedades já em cultivo e outras ainda em pousio; e uma fase de cultivo intensivo das lavouras temporárias, demonstrando, assim, que a técnica responde positivamente para a análise do calendário agrícola. Como última etapa da presente tese, para consecução da RUSLE mensal para o município, empregou-se o plugin GISus-M, no software ArcGIS. Nesta etapa, o fator Cr foi utilizado para ponderar os fatores C e P, contribuindo assim para a compreensão do modelo com as três fases distintas de usos, além de indicar a associação do fator LS com a erodibilidade dos solos. Sendo que no mês de janeiro de 2018 apresentou a maior perda de solos com 194,201 ton/ha/mês, porém em uma área de somente 0,022 km² e o menor valor de perda ocorreu no mês de agosto de 2018 com o valor de 36,081 ton/ha/mês. Com isso, o trabalho demonstrou-se relevante para a compreensão da dinâmica das lavouras temporárias para a modelagem preditiva da RUSLE, além de que a utilização do NDVI auxiliou no mapeamento de forma ágil com respostas precisas em um período mais curto de análise.

Abstract: Erosive processes are natural events, but they can be accelerated by incorrect use of the soil in agricultural production. From this perspective, the use of predictive and georeferenced models to quantify and classify erosive susceptibility has become an important tool in understanding the processes and, consequently, in planning the proper use of land. The northern region of Paraná is characterized by the great predisposition to the cultivation of temporary crops, highlighting the municipality of Bela Vista do Paraíso, which presents as predominant geological substrates basaltic eruptive rocks, belonging to the Serra Geral Formation (JKsg) and sandstone rocks from the Adamantina Formation (Ka), these being the genesis of material for the soils found in the municipality. Thus, this thesis demonstrates in three studies, where the erosivity of soils from different lithologies is evaluated, the use of the monthly NDVI (Normalized Difference Vegetation Index) and subsequent conversion to the Cr factor and the use of the predictive model of the Universal Equation of Revised Universal Soil Loss Equation (RUSLE), in monthly analyses, to estimate the loss and degradation of soils in the study area. In the first study, the soil erodibility factor (K factor) was estimated using the methodology of Boyoucos (1935), identifying that soils formed in sandstone rocks are in high to very high erodibility classes, while those formed in basaltic rocks have lower values due to the substantial increment of clay in the superficial horizons. It was also found that the methodology could present an underestimation of values, leading to an inconsistent analysis of the erodibility classes. The second study aimed to establish the classes of use and management of soils and monthly conservation practices, using the NDVI calculation, proposed by Rouse et al. (1973), with images from the Sentinel-2 satellite. Subsequently, there was a conversion to the Cr factor, proposed by Durigon et al. (2014), so that they did not present negative values and pointed out values closer to the reality of vegetation cover in the region of the tropics. Three distinct phases were identified regarding their uses: being a fallow phase, in which the soil has a low level of vegetation cover or even zero; an intermediate phase, with properties already under cultivation and others still fallow; and a phase of intensive cultivation of temporary crops, thus demonstrating that the technique responds positively to the analysis of the agricultural calendar. As the last step of this thesis, to achieve the monthly RUSLE for the city, the GISus-M plugin was used, in ArcGIS software. At this stage, the Cr factor was used to weight the C and P factors, thus contributing to the understanding of the model with the three distinct phases of uses, in addition to indicating the association of the LS factor with soil erodibility. In January 2018 it had the greatest loss of soil with 194,201 ton/ha/month, but in an area of only 0.022 km² and the lowest loss value occurred in August 2018 with the value of 36.081 ton/ ha/month. Thus, the work proved to be relevant for understanding the dynamics of temporary crops for predictive modeling of RUSLE, in addition to the use of NDVI assisted in mapping quickly with accurate responses in a shorter period of analysis.

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensor...

The vegetation indices (VIs) derived from the hyperspectral reflectance of vegetation are presented in this study for monitoring live green vegetation in the northern ecosystems of Iceland, along the fjords of Eyjafjörður and the Skagafjörður. The comparative analysis of the following VIs was performed: the NDVI, RVI, NRVI, TVI, CTVI, TTVI and SAVI. The methodology is based on the raster calculator band in a QGIS. The dataset includes a Landsat TM scene of 2013, UTM Zone 53, WGS84 captured from the GloVis. The computed bands include the NIR and R spectral bands and their combinations according to the algorithms of each of the seven VIs. The hyperspectral reflectance and crop canopy computations were applied to generate various scales of VIs and demonstrated following data range: NDVI: -0.91 to 0.65, RVI: 0.22 to 19.65, NRVI: 0.63 to 0.90, TVI: 0 to 1.12, CTVI: -0.64 to 1.07, TTVI: 0.70 to 1.18 and SAVI: -1.36 to 0.99 (roughly to 1.00). Of these, the RVI, NRVI, TVI and TTVI are adjusted to the positive values while the NDVI, CTVI and SAVI do include the negative diapason in the dataset due to the computing algorithm. The algorithms of the seven VIs are described and visualized in form of maps based on the multispectral remote sensing Landsat TM imagery identifying vegetated areas, their health condition and distribution of green areas against the bare soils, rocks, ocean water, lakes and ice-covered glaciers. The paper contributes both to the technical presentation of the QGIS functionality for the Landsat TM data processing by a raster calculator, and to the regional geographic studies of Iceland and Arctic ecosystems.

Global climate changes have been identified in the recent times and the focus thus has been on the impacts generated due to it. Some of the impacts identified in the Indian subcontinent are of temporary while some of these are permanent in nature. It is necessary to record and validate the impacts on the water resources related themes. Spatial technology has been accepted as one of the reliable option for quick monitoring for understanding the spatial impacts and to take appropriate faster decisions. The present study involves monitoring the changes of Land use Land cover and vegetation indices during last 16 Years. Vegetation index generated from the satellite data derived from remote sensing provides information on not only the green mass variation in time series but also it reflects the changes in water resources status within a region. Nagpur district has been selected as the study area and changes happened within this area during 2000-2016 have been presented. For simulating changes, LISS-3 data for LU/LC and 250m multi-temporal MODIS NDVI 16-day composite has been used which correlates with temperature, precipitation and deforestation. This analysis gives basic change information that is beneficial for future replanning of natural resources utilization. In this study maximum likelihood algorithm is used for supervised classification. This study gives ideas to study the crop behaviour changes and how we can improve our agriculture area and utilize available water resources within Nagpur district. The results of the study include the rapid changes of built-up, wasteland, open forest, agricultural land and reduction of the dense forest area and the water bodies. This study performed using remote sensing and GIS techniques helps monitoring rapid changes due to global climate change.

An agronomic research was conducted to evaluate the spatial variability of an onion crop, with the aim to
test Vegetation indices (VIs) as a tool to detect different yield areas.
Eleven VIs were derived from geo-referred hyperspectral readings taken at bulbification stage. Eight VIs
showed significant regressions with yield, and grouped in four clusters according to statistical analysis
(H = high; Ms and Mi as medium superior and inferior; L = low). Maps were elaborated with ordinary
Kriging. At a visual assessment, many VIs appeared similar to yield map. The surface analysis revealed
that all VIs accurately detected an L area (top of maps) characterized by heavy soil constrains, and the
H area on the left side of the map (button and upper part). The best estimation of the total field yield
was obtained by the so-called Soil-line vegetation indices and in particular by TSAVI. This study reinforces
the possibility of assessing onion yield by spectroradiometric measurements at field scale.

The main purpose of this paper is to investigate multitemporal land surface temperature (LST) changes by using satellite remote
sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt
Lake, Turkey. Normalized vegetation index (NDVI), vegetation condition index (VCI), and temperature vegetation index (TVX)
were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and
radiometric correction procedures were conducted to make satellite remote sensing data comparable with in situ measurements
carried out using thermal infrared thermometer supported by hand-held GPS. Theresults showed that real-time ground and satellite
remote sensing data were in good agreement with correlation coefficient (𝑅
2
) values of 0.90. Theremotely sensed and treated satellite
images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about 2
C) in the
Salt Lake Basin area during the 28-year period (1984–2011). Analysis of air temperature data also showed increases at a rate of 1.5–
2
∘
C during the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of
water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

The present study assesses the impact of oil spillage in the Southwestern Niger Delta of Nigeria over the past fifty (50) years. It further sought to find out the driving forces and implications of oil spillage on vegetation, livelihoods and other key parameters. The study employed geospatial techniques and a secondary source of data to achieve the objectives set out in this study. The Global Moran I statistical tool was used to determine the spatial autocorrelation based on feature locations and attribute values. We observed built-up areas, bare land, and less dense vegetation had an overall increment of 1975.98 km2, 1370 km2 and 23805 km2, respectively. Dense vegetation had declension of 22058.33 km2 over the past five decades. Findings depict a declining trend in Normalized Difference Vegetation Index, attributed to oil spillage as the key contributory factor. Occasioned by anthropogenic activities, the driving forces were traced to attacks on oil pipelines during conflicts and i...