Dinâmica de incêndios florestais e alterações biofísicas na Amazônia e Cerrado brasileiros a partir de séries temporais de sensoriamento remoto

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2019.Os biomas brasileiros se adaptaram a diferentes padrões de presença ou ausência do fogo. Dados derivados de sensoriamento remoto têm sido uma das principais bases para a detecção de incêndios florestais e os danos na estrutura da vegetação, especialmente com o desenvolvimento de sensores com alta resolução temporal e espectral, e o estabelecimento de longas séries contínuas. Nesse sentido, esta tese buscou aprofundamento em três pontos: (1) Qual a potencialidade de produtos de sensoriamento remoto para a descrição da dinâmica do fogo no Brasil? (2) Como detectar cicatrizes de queimadas a partir de séries temporais em ambientes amazônicos?; e por fim (3) Quais os danos na vegetação resultantes da alteração do regime histórico do fogo e como podem ser quantificados por sensoriamento remoto? Para ampliar o conhecimento sobre essas questões foram utilizados diversos produtos derivados dos sensores Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) e Operational Land Imager (OLI), além de diversos dados espaciais, em três escalas: uma para todo o território nacional, uma área específica do Cerrado e duas áreas específicas da Amazônia. A metodologia básica consistiu na análise de séries temporais MODIS para detecção e quantificação dos efeitos do fogo. Os resultados permitiram concluir que: (1) Os produtos globais MODIS de detecção de cicatrizes de queimadas apresentaram altas taxas de erros de omissão no Brasil, superiores a 78% em média no território nacional, sendo seu uso recomendado apenas para análises regionais ou globais. Os produtos de queimadas apresentaram as menores acurácias nos biomas dos Pampas, Amazônia e Mata Atlântica e as maiores acurácias nos biomas do Cerrado e da Caatinga. Apesar desta limitação, o produto MCD64 permitiu descrever o regime do fogo no país, as principais regiões de ocorrência e a influência da umidade e classe de vegetação neste padrão. Foram estabelecidas como limite para a ação do fogo, as zonas sem estiagem, como o Oeste da Amazônia e litoral leste do Brasil, assim como as áreas do semiárido nordestino. (2) Dentre os métodos analisados de diferença sazonal e normalização temporal, a normalização pela média da banda espectral do Infravermelho Próximo foi responsável pela maior acurácia na detecção de cicatrizes de queimadas na Amazônia, retificando a utilização de alguns índices especializados originalmente para vegetações temperadas, como o Normalized Burn Ratio (NBR). Outros métodos analisados, como a diferença sazonal e normalização por z-score, apresentaram melhor acurácia que imagens originais, mas inferior em comparação com a normalização pela média. (3) A alteração da recorrência do fogo teve influência direta no padrão biofísico e fenológico da vegetação nas áreas de estudo na Amazônia e no Cerrado. As variáveis de produtividade primária bruta e albedo apresentaram baixa representatividade espacial. As mudanças com maior inclinação da tendência, do Enhanced Vegetation Index (EVI) e temperatura superficial, foram tanto relacionadas com a recorrência do fogo, quanto com a classe de uso da vegetação, como nas terras indígenas. A inclinação da tendência, no EVI e temperatura superficial, foi maior na área do Cerrado, reforçando a necessidade urgente de conservação deste bioma. A pesquisa atestou a importância de dados de sensoriamento remoto para avaliação da dinâmica do fogo e dos seus efeitos na vegetação. A utilização de séries temporais do sensor MODIS permitiu tanto identificar as áreas queimadas com maior acurácia que outros produtos disponíveis, quanto quantificar as fragilidades da vegetação relacionadas ao padrão de fogo atual.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Brazilian biomes have adapted to different patterns of presence or absence of fire. Data derived from remote sensing have been one of the main techniques for the detection of forest fires and damage to vegetation structure, especially with the development of high temporal and spectral resolution sensors and the establishment of long continuous series. Thus, we intend to focus on three points in this thesis: (1) What is the potential of remote sensing products for the description of fire dynamics in Brazil? (2) How to detect burn scars from remote sensing time series in Amazonian environments? And finally (3) What damages in the vegetation resulting from the alteration of the historical fire regime and how can they be quantified by remote sensing? In order to increase the knowledge about these issues, several products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) sensors were used, in addition to diverse spatial data, in three scales: one for the whole national territory, one specific area of the Cerrado and two specific areas of the Amazon. The basic methodology consisted of the analysis of MODIS time series for the detection and quantification of fire effects. The results allowed to conclude that: (1) MODIS global burned area products presented high omission errors rates in Brazil, higher than 78% on average in the national territory, and their use is recommended only for regional or global analyzes. The burned area products showed the lowest value in the biomes of the Pampas, Amazon Forest and Atlantic Forest, and the highest values in the biomes of the Cerrado and Caatinga. In spite of this limitation, the product MCD64 allowed to describe the fire regime in the country, the main regions of occurrence and the influence of moisture and vegetation class in this pattern. Were established as a limit for the action of the fire the areas without drought, such as the Western Amazon and the east coast of Brazil, as well as areas with low availability of rainfall and fuel, such as the semi-arid in the Northeast. (2) Among the analyzed methods of seasonal difference and temporal normalization, the normalization of the Near Infrared spectral band by the zero-mean, was responsible for the greater accuracy in the detection of burn scars in the Amazon region, rectifying the use of some indices originally specialized for temperate vegetation, such as the Normalized Burn Ratio (NBR). Other methods analyzed, such as the seasonal difference and z-score normalization, showed better accuracy than original images, but lower than normalization by the zero-mean. (3) The alteration of fire recurrence had a direct influence on the biophysical and phenological pattern of vegetation the study areas of Amazon and Cerrado. The variables of gross primary productivity and albedo showed low spatial representativeness. The changes with higher trend slope, of Enhanced Vegetation Index (EVI) and surface temperature, were related both to fire recurrence and to the vegetation use class, as in indigenous lands. The slope of the trend in EVI and surface temperature was higher in the Cerrado area, reinforcing the urgent need for conservation of this biome. The research attested the importance of remote sensing data for the evaluation of fire dynamics and its effects on vegetation. The use of MODIS time series allowed both identifying the burned areas with greater accuracy than other available products, and quantifying the fragilities of the vegetation related to the current fire pattern

Monitoring the impact of land cover change on surface urban heat island through google earth engine. Proposal of a global methodology, first applications and problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities

Investigation into the bio-physical constraints on farmer turn-out-date decisions using remote sensing and meteorological data.

ThesisDoctoral thesisAccepted versionGrass is the most common landcover in Ireland and covers a bigger percentage (52%) of the country than any other in Europe. Grass as fodder is Ireland’s most important crop and is the foundation of its most important indigenous industry, agriculture. Yet knowledge of its distribution, performance and yield is scant. How grass is nationally, on a farm by farm, year by year basis managed is not known. In this thesis the gaps in knowledge about grassland performance across Ireland are presented along with arguments on why these knowledge gaps should be closed. As an example the need for high spatial resolution animal stocking rate data in European temperate grassland systems is shown. The effect of high stocking density on grass management is most apparent early in the growing season, and a 250m scale characterization of early spring vegetation growth from 2003-2012, based on MODIS NDVI time series products, is constructed. The average rate of growth is determined as a simple linear model for each pixel, using only the highest quality data for the period. These decadal spring growth model coefficients, start of season cover and growth rate, are regressed against log of stocking rate (r2 19 = 0.75, p<0.001). This model stocking rate is used to create a map of grassland use intensity in Ireland, which, when tested against an independent set of stocking data, is shown to be successful with an RMSE of 0.13 Livestock Unit/ha for a range of stocking densities from 0.1 to 3.3 Livestock Unit/ha. This model provides the first validated high resolution approach to mapping stocking rates in intensively managed European grassland systems. There is a demonstrated a need for a system to estimate current growing conditions. Using the spring growth model constructed for estimating stocking density a new style of grass growth progress anomaly map in the time-domain was developed. Using the developed satellite dataset 1 and 12 years of ground climate station data in Ireland, NDVI was modelled against time as a proxy for grass growth This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of “days behind” and “days ahead” of the norm. SPAT estimates for 2012 and 2013 are compared to ground based estimates from 30 climate stations and have a correlation coefficient of 0.897 and RMSE of 15days. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. This is understood by the author to be the first validated growth anomaly service, and the first for intensive European grasslands. The decisions on when to turn out cattle (the turn out date (TOD)) from winter housing to spring grazing is an important one on Irish dairy farms which has significant impacts on operating costs on the farm. To examine the relationship of TOD to conditions, the National Farm Survey (NFS) of Ireland database was geocoded and the data on turn out dates from 199 farms across Ireland over five years was used. A fixed effects linear panel data model was employed to explore the association between TOD and conditions, as it allows for unobserved variation between farmers to be ignored in favour of modelling the variance year on year. The environmental variables used in the analysis account for 38% of the variance in the turn out dates on farms nationwide. National seasonal conditions dominate over local variation, and for every week earlier grass grows in spring, farmers gain 3.7 days in grazing season but ignore 3.3 days of growth that could have been used. Every 100mm extra rain in spring means TOD is a day later and every dry day leads to turn out being half a day earlier. A well-drained soil makes TOD 2.5 days earlier compared to a poorly drained soil and TOD gets a day later for every 16km north form the south coast. This work demonstrates that precision agriculture 1 driven by optical and radar satellite data is closer to being a reality in Europe driven by enormous amounts of free imagery from NASA and the ESA Sentinel programs coupled with open source meteorological data and models and new developments in data analytics

Overgrazing in the Montado? The need for monitoring grazing pressure at paddock scale

Montados are presently facing the threat of either abandonment or intensification, and livestock overgrazing has been suspected of contributing to reduced natural regeneration and biodiversity. However, reliable data are to our knowledge, lacking. To avoid potential risks of overgrazing, an adaptive and efficient management is essential. In the present paper we review the main sources of complexity for grazing management linked with interactions among pasture, livestock and human decisions. We describe the overgrazing risk in montados and favour grazing pressure over stocking rate, as a key indicator for monitoring changes and support management decisions. We suggest the use of presently available imaging and communication technologies for assessing pasture dynamics and livestock spatial location. This simple and effective tools used for monitoring the grazing pressure, could provide an efficient day-to-day aid for farm managers’ operational use and also for rangeland research through data collection and analysis

Mechatronics applications and prototyping sensors for the precision livestock farming

The study is subdivided into 5 chapters and comprises a review of the main components of Plf, the development of a prototype for EC monitoring in ewe milk, a prototype for monitoring animals body temperature, the optimization of collection rounds of goat milk and the development of a prototype for somatic cell count (SCC) through the measurement of Sodium ions in ewe milk.‬‬‬ The first chapter is a review of the advancements of the main components of Plf, i.e. software, hardware and data transmission, focusing on issues related to hardware modularity and differences between licensed and unlicensed software. From the review it emerges that image processing is one of the most used techniques in Plf systems, in that it allows the detection of behavioral, biological and pathological parameters without interfering with the animals routine activities. In this regard the area occupied by a lamb carcass was calculated by using an image analysis open source software, CellProfiler (Jones et al., 2008). The second chapter deals with the realization of an innovative portable tool for somatic cells count in ewe milk by measuring its electrical conductivity. There are over 15,000 dairy sheep farms in Sardinia, which represent both historically and economically the most important agricultural and livestock sector in the island. Indeed, Sardinia holds more than 40% of the national sheep population thanks to more than 3 million sheep heads that provide about 60% of the total national milk production. One of the most common problems in sheep farms is mastitis, an intramammary infection which may cause a quantitative reduction up to 50% in milk production and a qualitative drop, in particular of lactose and casein. One of the indirect methods for the assessment of somatic cell count (SCC) in ruminants’ milk is through the measurement of its electrical conductivity (EC). In small ruminants, EC has a reasonable correlation R2 = 0.35 with somatic cells but to date there is still not a portable tool that can estimate SCC based on the milk’s EC reading. The prototype was calibrated on Sarda ewe milk. The aim of Chapter 3 was to develop a system using a open source sensors, actuators and micro-controller. The system is able to monitoring the rectal temperature of the animals, sending data via Bluetooth to a smart phone. The micro-controller used was an ATmega32U4, the temperature was read using the LM35 analogic sensor and a Class 1 Bluetooth serial module was connected to Arduino creating a wireless serial link between an Android phone and the Arduino board. The application for receiving data on an android smart phone was created using App Inventor that is an innovative Android application creation software developed by Massachusetts Institute of Technology (MIT). This app is free available on Google Play Store under the name animal_temp. The costs of sheep milk collection rounds in Sardinia have been analysed in chapter fourth. The escalating costs incurred by the dairy processing industries for milk collection from individual farms have focused the attention on the rationalization of milk collection and transport systems. In this regard, the case of the Sardinian goat sector has characteristics that make it unique and not comparable to other logistics optimization realities. The problems of this sector are mainly represented by the particular conditions of the rural road network and the fragmented nature of livestock farms. The aim of the present study was to test a milk collection route optimization software, MilkTour, in the collection rounds of a sample cheese dairy. The software has been developed by the Land Engineering Section of the Agriculture Department of the University of Sassari. A total of 5 routes were analysed and optimized. The results have highlighted the importance of optimizing collection routes as they have a significant impact on business costs. A important contribution that has emerged is the strong correlation between collection density and the cost per litre of collected milk (€cent/l), which allows to detects the cost-effectiveness of a round of collection and its relative optimized around. The objective of chapter 5 was to study the relationship between the ione Na+ and the main components of sheep milk, in particular somatic cells. Moreover, a portable device for estimating SCC in sheep milk was designed. The study was conducted on over 2000 samples. The milk components examined were: fat, proteins, lactose, pH, sodium chloride, urea and the ions Na+. The correlation between Na + and SCC corresponded to R2 = 0.76 (P &lt;0.01). The prototype developed incorporates two containers which receives milk samples taken from each half udder. Each container has integrated inside two sensors, one to detect the level of Na+ in the milk and the other one to compensate the milk temperature. The mathematical model, loaded into the microcontroller by a firmware written in C / C ++, analyze the data and gives back the estimate of SCC level, so it allows farmers to monitor the ewes health status by periodically comparing the somatic cell counts of each half udder. While dealing with different topics the 5 chapters can be enclose in a big new topic, called Precision Livestock Farming (Plf). Plf is the discipline that allows to monitor in real-time the numerous biological and environmental parameters concerning each individual animal of the herd. A Plf system is always made up by three components: a physical element, i.e. the hardware; an element for data processing and presentation, known as the software; and an element for the transmission of data, i.e. the network. The hardware comprises the sensors, the computers and/or microcontrollers, the data transmission and acquisition systems and the actuators. Mathematical models for data processing and the data presentation interface are included in the software loaded into the microcontroller

Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, italy

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Triennial Report: 2006-2008

Triennial Report Purpose [Page] 2 The Geographic Information Science Center of Excellence [Page] 4 Three Years in Review [Page] 5 SDSU Faculty [Page] 6-11 EROS Faculty [Page] 12-16 Post-Doctoral Researchers [Page] 17-26 GSE Ph.D. program [Page] 27 Ph.D. Students [Page] 28-39 Center Scholars Program [Page] 40 Masters Students [Page] 41 Geospatial Analysts [Page] 42 Administrative Staff [Page] 43 Center Alumni [Page] 44 Research Funding [Page] 45-46 Ph.D. Student Scholarship Grants [Page] 47 Computing Resources [Page] 48 Looking Forward [Page] 49 Appendix I Faculty publications 2006-2008 [Page] 50-58 Appendix II Cool faculty research and locations [Page] 60-65 Appendix III GIScCE birthplace map [Page] 66 Appendix IV Telephone and email contact information [Page] 67-68 Appendix V How to get to the GIScCE [Page] 6