Remote sensing applications for mapping large wildfires based on machine learning and time series in Northwestern Portugal

Mapping large wildfires (LW) is essential for environmental applications and enhances the understanding of the dynamics of affected areas. Remote sensing techniques supported by machine learning and time series have been increasingly used in studies addressing this issue and have shown potential for this type of analysis. The main aim of this article is to develop a methodology for mapping LW in northwestern Portugal using a machine learning algorithm and time series from Landsat images. For the burnt area classification, we initially used the Fourier harmonic model to define outliers in the time series that represented pixels of possible burnt areas and, then, we applied the random forest classifier for the LW classification. The results indicate that the harmonic analysis provided estimates with the actual observed values of the NBR index; thus, the pixels classified by random forest were only those that were masked, collaborated in the processing, and reduced possible spectral confusion between targets with similar behaviour. The burnt area maps revealed that ~23.5% of the territory was burnt at least once from 2001 to 2020. The temporal variability of the burnt area indicated that, on average, 6.504 hectares were affected by LW within the 20 years. The annual burnt area varied over the years, with the minimum annual area detected in 2014 (679.5 hectares) and the maximum mapped area detected in 2005 (73,025.1 hectares). We concluded that the process of defining the mask with the outliers considerably reduced the universe of pixels to be classified within each image, which leaves the training of the classifier focused on separating the set of pixels into two groups with very similar spectral characteristics, thus contributing so that the separation of groups with similar spectral behaviour was performed automatically and without great sampling effort. The method showed satisfactory accuracy results with little omission for burnt areas.This research was funded by Portuguese funds through Fundação para a Ciência e a Tecnologia, I.P., within the scope of the research project “EcoFire—O valor económico dos incêndios florestais como suporte ao comportamento preventivo”, reference PCIF/AGT/0153/2018

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Monitoring deforestation and forest degradation linking high-resolution satellite data and field data in the context of REDD+. A case of Tanzania

El principal objetivo de este doctorado es apoyar el desarrollo de un sistema nacional de monitoreo forestal en Tanzania para informar sobre las emisiones actuales e históricas derivadas de la deforestación y la degradación forestal. El marco de la tesis se centra específicamente en el emergente contexto internacional de la iniciativa REDD + (Reducción de Emisiones por Deforestación y Degradación) de las Naciones Unidas, bajo la cual los países pueden obtener subsidios financieros para demostrar que están reduciendo sus emisiones de carbono de tierras forestales con respecto a su práctica histórica reciente. La investigación se centró en cinco áreas de investigación: La parte (1) revisa los antecedentes políticos de REDD +. En él se describen las normas y las opciones a ser abordadas por los países participantes y se demuestran algunos de los problemas técnicos y las opciones que pueden enfrentar y adoptar en la tecnología de teledetección. La parte (2) presenta los resultados del trabajo de campo en Tanzania. Esto incluye la creación de una recopilación rápida de datos sobre el terreno y directrices sobre protocolos para vincular los datos de campo con los datos de teledetección, con el fin de producir mapas de cobertura vegetal y biomasa aérea utilizando imágenes de muy alta resolución. La parte (3) demuestra la mejora en el mapeo de los bosques con una fina resolución espacial y alta frecuencia de adquisiciones con la llegada de los nuevos satélites Sentinel-2. Este potencial se ha probado en un área de bosque seco en el centro de Tanzania. En la parte (4) se evalúa una estimación a gran escala de la biomasa terrestre para toda Tanzania, utilizando una combinación de datos de teledetección y de campo. La capacidad predictiva se investigó comparando los resultados con las mediciones en tierra realizadas por el inventario nacional. La parte (5) investiga la dinámica de la deforestación alrededor de Dar es Salaam, junto con un modelo para inferir la probabilidad futura de deforestación a nivel nacional. La capacidad del modelo de replicar los patrones espaciales de deforestación se evaluó a través de datos del terreno. Entre los principales resultados de este doctorado están que las estimaciones de cambios de cobertura forestal de diferentes fuentes tienen una amplia varianza a nivel nacional y que las estimaciones de emisiones para el proceso REDD + siguen siendo poco fiables. Hay un gran número de opciones a las que se enfrenta un sistema de monitoreo forestal, en términos de definiciones y métodos, que tienen un impacto en la factibilidad de implementación y en los resultados. Se ha demostrado la dificultad de vincular los datos de teledetección con los parámetros forestales de los estudios nacionales, con recomendaciones para mejorar la futura recopilación de datos sobre el terreno. Sin embargo, el uso sinérgico de la teledetección y los datos del estudio sobre el terreno pueden reducir efectivamente los costes de cartografía y monitoreo de los cambios y la degradación de los bosques. Para ello, se encontró que el uso de índices de textura y segmentación de imágenes de satélite de alta resolución espacial (5m) era útil en la producción de mapas de biomasa forestal. Además, la llegada de Sentinel-2 ofrece la oportunidad de analizar datos de media resolución espacial (<20m) en series temporales, especialmente útiles para áreas secas.The main objective of this PhD is to support the development of a national forest monitoring system in Tanzania so as to report on current and historical emissions which derive from deforestation and forest degradation. The framework of the thesis is specifically focused on the emerging international context of the REDD+ (Reducing Emissions from Deforestation and Degradation) initiative from the United Nations, under which countries may obtain financial grants for demonstrating that they are reducing their carbon emissions from forest lands with respect to their recent historical practice. The research focused on five focal areas of research: Part (1) reviews the policy background to REDD+. It outlines the rules and choices to be addressed by participatory countries and demonstrates some of the technical problems and options that they can face and adopt in the remote sensing technology. Part (2) presents the results from the PhD field work in Tanzania. This included the set-up of rapid field data collection and guidelines on protocols to link the field data to the remote sensing data, so as to produce maps of vegetation cover and above ground biomass using very high resolution images. Part (3) demonstrates the improvement to map forests at a fine spatial resolution and high frequency of acquisitions with the arrival of the new Sentinel-2 satellites. This potential has been tested on an area of dry forest in Central Tanzania. Part (4) tests a full scale estimate of above ground biomass for the whole of Tanzania, using a combination of remote sensing and field data. The predictive capability was investigated by comparing the results against ground measurements undertaken by the national inventory. Part (5) investigates the dynamics of deforestation around Dar es Salaam, along with a model to infer future probability of deforestation at the national level. The ability of the model to replicate spatial patterns of deforestation was assessed through ground truth data. Among the main outcome of this PhD is that estimates of forest change from different sources have wide variance at national level and emissions estimates for the REDD+ process remain unreliable. There are a large number of choices facing a forest monitoring system, in terms of forest definitions and methods, which have an impact on the feasibility of implementation and results. The difficulty of linking remote sensing data to the forest parameter from national surveys has been shown, with recommendations to improve future field data collection. However the synergistic use of remote sensing and field survey data can effectively reduce the costs for mapping and monitoring forest changes and forest degradation. For this, the use of high spatial resolution (5m) satellite image segmentation and texture indices was found to be useful in the production of forest biomass maps. Additionally, the arrival of Sentinel-2 data provides the opportunity to analyse medium high spatial resolution data (<20m) in time series, especially useful for dry areas

Mapping intra- and inter-annual dynamics in wetlands with multispectral, thermal and SAR time series

Kartierung der intra- und interannuellen Dynamik von Feuchtgebieten mit multispektralen, thermischen und SAR-Zeitreihen Die Analyse der aktuellen räumlichen Verbreitung und der zeitlichen Entwicklung von Feuchtgebieten stellt eine äußerst komplexe Aufgabe dar, welche durch die Saisonalität, die schwierige Zugänglichkeit und die besonderen Eigenschaften als Ökoton bedingt ist. Erdbeobachtungssysteme sind somit das am besten geeignete Werkzeug, um zeitliche und räumliche Muster von Feuchtgebieten auf globaler Ebene zu beobachten (saisonale Veränderungen und Langzeit-Trends) und um den Einfluss der menschlichen Aktivitäten auf ihre physischen und biologischen Eigenschaften zu untersuchen. Zur Kartierung von raum-zeitlichen Mustern wurden Zeitreihen von Radar- (Sentinel-1), Multispektral- (Sentinel-2) und Thermal-Satellitendaten (MODIS) in fünf Untersuchungsgebieten, mit für Feuchtgebiete unterschiedlichen typischen Charakteristika, untersucht. In Kapitel 1 werden die Problematik in Bezug auf die Definition von Feuchtgebieten erläutert und allgemeine Degradations-Trends beschrieben. Die Kapitel 2 und 3 behandeln einen Algorithmus, der Veränderungen mithilfe von SAR-Zeitreihen feststellt, sowie die Vorteile des Cloud-Computings für das operationelle Monitoring saisonaler Muster und die Erkennung kurzfristig auftretender Veränderungen. In den Kapiteln 4 und 5 werden die zwei Hauptursachen für den Verlust von Feuchtgebieten betrachtet: der Staudammbau und die Ausdehnung landwirtschaftlicher Flächen. In Kapitel 4 werden dichte Zeitreihen multispektraler (Sentinel-2) und SAR-Daten (Sentinel-1) verwendet, um die Feuchtgebiete Albaniens – eines Landes in dem konträre Pläne zum Ausbau seines Wasserkraftpotentials und dem Schutz intakter Flussökosysteme zu Spannungen führen – landesweit zu kartieren. Die synergetischen Vorteile, die sich durch die Fusionierung von multispektralen und SAR-Daten für die Klassifikation ergeben, werden dabei herausgestellt. Kapitel 5 veranschaulicht, dass die Kilombero-Überschwemmungsebene in Tansania ein großes und bedeutendes Feuchtgebiet ist, das in den vergangenen Jahren infolge der weitgehend unkontrollierten Ausbreitung landwirtschaftlicher Flächen in seiner Ausdehnung und seiner Ökologie stark beeinträchtigt wurde. Um die Auswirkungen der Landnutzungsänderungen des Feuchtgebietes während der vergangenen 18 Jahre zu analysieren, wurden eine Zeitreihe (2000 bis 2017) thermaler Daten (MODIS) analysiert. Die drei für die Zeitreihenanalyse angewandten Modelle zeigen, wie landwirtschaftliche Praktiken die Landoberflächentemperatur in den landwirtschaftlich genutzten Gebieten sowie in den angrenzenden natürlichen Feuchtgebieten erhöht haben.Due to wetlands’ seasonality, their difficult access and ecotone character, determining their actual extension and trends over time is a complex task. Earth Observation systems are the most appropriate tool to monitor their spatio-temporal patterns (seasonal changes and long term trends) at global scales, and to study the effects that human activities have in their physical and biological properties. In this work I use time series of radar (Sentinel-1), multispectral (Sentinel-2) and thermal (MODIS) imagery to map the spatio-temporal patterns in 5 wetlands of different characteristics. First, I introduce in chapter 1 the problematic of wetlands’ definitions and their degradation trends. I continue with a brief introduction on remote sensing, time series analysis, and their applications on wetlands’ research and management. In chapters 2 and 3 I implement an algorithm for change detection of time series of Sentinel-1 images and demonstrate the advantages of cloud computation for operational monitoring. In chapters 4 and 5 I address two of the main causes of wetland degradation: dam building and agricultural expansion. In chapter 4 I use dense time series of Sentinel-1 and Sentinel-2 images map all the wetlands of Albania; a country struggling between developing its large hydropower potential or preserving its intact and valuable river ecosystems. I evaluate the synergic advantages of fusing multispectral and radar imagery in combination with knowledge-based rules to produce classification of higher thematic and spatial resolutions. In chapter 5 I present how the Kilombero Floodplain, in Tanzania, has been degraded during the last years due to uncontrolled farmland expansion. I use a time series of thermal imagery (MODIS) from 2000 until 2017 to analyze the effect of land use changes on the wetland. I compare three models for time series analysis and reveal how farming practices have increased the surface temperature of the farmed area, as well as in adjacent natural wetlands.Mapeo de las dinámicas inter- e intra-anuales en humedales con series temporales de imágenes multiespectrales, termales y de radar Debido a la estacionalidad de los humedales, su difícil acceso y sus características de ecotono, determinar su actual extensión y sus tendencias a lo largo del tiempo es una tarea compleja. Los sistemas de observación terrestres son la herramienta más apropiada para monitorear sus patrones espacio-temporales (estacionalidad y tendencias a largo plazo) a escalas globales, y para estudiar los efectos que las actividades humanas causan en sus propiedades físicas y biológicas. En esta tesis uso series temporales de imágenes radar (Sentinel-1), multiespectrales (Sentinel-2) y termales (MODIS) para mapear los patrones espacio-temporales de 5 humedales de diferentes características. En el capítulo 1 describo los retos que derivan de las diferentes definiciones que existen de los humedales. También presento las tendencias globales de degradación que la mayoría de los humedales continúan experimentando en los últimos años. Continúo con una breve introducción de los sistemas de teledetección remota, análisis de series temporales, y sus aplicaciones a la investigación y gestión de los humedales. En los capítulos 2 y 3 implemento un algoritmo de detección de cambios para series temporales de imágenes radar, y muestro las ventajas de usar sistemas de computación en la nube para monitorear cambios en la cobertura del suelo a corto plazo. En los capítulos 4 y 5 trato con dos de las causas más comunes de degradación de humedales: la construcción de presas y la expansión de la agricultura. En el capítulo 4 uso series temporales de imágenes multiespectrales (Sentinel-2) y radar (Sentinel-1) para mapear todos los humedales Albania; un país que se debate entre desarrollar su potencial hidroenergético o preservar sus valiosos e intactos ecosistemas de rivera. Mediante la fusión de imágenes radar y multiespectrales y el uso de reglas de decisión genero un mapa de suficiente resolución espacial y temática para que pueda ser usado por sectores interesados y gestores. En el capítulo 5 presento como las llanuras inundables de Kilombero, en Tanzania, han sido degradadas durante los últimos años debido a la expansión incontrolada de la agricultura. Usando series temporales de imágenes termales (MODIS) desde 2000 hasta 2017 y mapas de cambios de usos del suelo, determino los efectos que estos cambios han tenido en el humedal. Comparo 3 modelos diferentes de análisis de series temporales y muestro cómo la expansión de la agricultura ha incrementado la temperatura superficial terrestre, no solo de la zona cultivada, sino también de zonas adyacentes aún naturales

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Os grandes incêndios florestais no Noroeste de Portugal continental (2001 – 2020) - A deteção remota como ferramenta de apoio ao seu estudo

Tese de doutoramento em Geografia (especialização em Geografia Física e Estudos Ambientais)Atualmente, é crescente a preocupação por parte da sociedade sobre os impactes provocados pela ação do fogo nos diferentes ambientes. O fogo pode ser entendido como um fator ecológico natural ou não que influencia na estrutura e funcionamento de diversos ecossistemas, podendo, quando se perde o seu controle, passar a ser considerado como incêndio, sendo potencialmente capaz de provocar grandes prejuízos, e se tornar um grande incêndio florestal. Esta preocupação tem como consequência benéfica o desenvolvimento de ferramentas que ajudam a compreender esse problema. Em todo o mundo, incêndios florestais sempre ocorreram em diversas regiões, mesmo em áreas com condições climáticas menos propensas, resultando em impactos ambientais em vários níveis. Nas últimas décadas, a Europa tem enfrentado um aumento significativo no número de incêndios e na área devastada pelo fogo, com padrões espaciais e temporais variados. Essas tendências são resultado das transformações na disponibilidade de materiais combustíveis e das mudanças climáticas. Diante disso, Portugal destaca-se como um dos países mais afetados pelos incêndios florestais no sul do continente europeu, com eventos recorrentes e frequentes impactes, sendo responsáveis por perdas no ambiente, na economia e de vidas humanas. Os incêndios florestais não estão distribuídos uniformemente pelo território de Portugal, sendo o Noroeste a região que apresenta a maior incidência. Nessa investigação buscamos conhecer e compreender a repartição espacial e evolução temporal dos Grandes Incêndios Florestais (GIF) no Noroeste de Portugal continental, com recurso a técnicas de deteção remota, no período de 2001 a 2020. Para isso, foi realizada uma extensa revisão bibliográfica para fundamentar esta tese de doutoramento, além de uma bibliométrica para analisar as publicações científicas sobre o tema estudado entre os anos de 1991 a 2020. Com o uso de técnicas de detenção remota desenvolvemos uma metodologia para cartografar os GIFs, usando algoritmo de Machine Learnig e séries temporais por meio da averiguação de 20 anos de imagens Landsat. Também realizamos estudo de caso no município de Baião com a análise de um GIF que ocorreu em 2019, e realizamos a cartografia das áreas de interface urbano florestais (IUF) para esse município. Os Incêndios de Interface Urbano-Florestal (IUFs) desempenham um papel crucial nesse contexto, uma vez que as mudanças demográficas e sociais ocorridas nas áreas rurais têm levado ao abandono das terras nas últimas décadas., o que, por sua vez, influencia no ordenamento florestal e nas áreas onde as casas ou áreas urbanas se encontram ou se misturam com a vegetação selvagem ou áreas rurais, as quais são muito vulneráveis aos incêndios florestais. Diante disso, consideramos que com base em dados de deteção remota podemos aprofundar o conhecimento sobre os GIFs no Noroeste de Portugal continental, principalmente pela possibilidade de realizar análise de séries temporais, que contribuem no entendimento dos eventos já ocorridos. O desenvolvimento de estudos, como este, aborda questões ambientais, sociais e económicas, uma vez que favorece o desenvolvimento de campanhas de sensibilização e prevenção, atividades de gestão florestal e do risco de incêndio, bem como a a monitorização de possíveis atividades potencialmente facilitadoras de incêndios.Nowadays, there has been a growing concern of society about the impacts caused by the action of fire in different environments. Fire can be understood as a natural or non-natural ecological factor that influences the structure and functioning of several ecosystems, and when it loses control, it can be considered as a wildfire, being potentially capable of causing great damage, turning into a large wildfire. This concern has as a beneficial consequence the development of more efficient tools that contribute to the understanding of this problem. Worldwide, wildfires have always occured in multiple regions, even in climatically less prone areas, and with environmental impacts at various levels. In recent decades, Europe has registered a high number of fires and an extensive burnt area, with different spatial and temporal trends, as a result of transformations in the availability of fuel material and climate change. In view of this, Portugal stands out as one of the countries most affected by wildfires in the south of the European continent, with recurrent events and frequent impacts, being responsible for losses in the environment, economy, and human lives. Wildfires are not evenly distributed across the territory of Portugal, with the Northwest being the region with the highest incidence. In this investigation, we seek to know and understand the spatial distribution and temporal evolution of the Large Wildfires (LWF) in the Northwest of mainland Portugal, using remote sensing techniques within a period from 2001 to 2020. In doing so, we performed an extensive bibliographic review to support this doctoral thesis, in addition to a bibliometric analyse to exam scientific publications on the subject studied between 1991 and 2020. With the use of remote sensing techniques, we developed a methodology to map the LWFs, using Machine Learning algorithm and time series through the investigation of 20 years of Landsat images. Furthermore, we developed a case study in the municipality of Baião by analysing the LWF that occurred in 2019 and mapped its wildland-urban interface areas (WUI). The WUIs have gained importance in this matter, since the demographic and social changes that have occurred in rural areas have driven land abandonment in recent decades, which, in turn, influences forest planning and the areas where houses or urban areas are located or intermingled with wild vegetation or rural areas, which are very vulnerable to wildfires. On account of it, we consider that based on remote sensing data we can deepen the knowledge of LWFs in the Northwest of mainland Portugal, mainly due to the possibility of performing time series analysis, which contribute to the understanding of events that have already occurred. The development of studies, such as this one, addresses environmental, social, and economic issues, as it fosters awareness and prevention campaigns, forestry, and fire risk management activities, as well as the monitoring of possible activities that potentially can lead to wildfires.À FCT, Fundação para a Ciência e a Tecnologia, pelas bolsas de investigação nos projetos “Eco.Fire – O valor económico dos incêndios florestais como suporte ao comportamento preventivo” e “O3F - Um Framework de Optimização para reduzir os Incêndios Florestais”

Forest Biomass and Land Cover Change Assessment of the Margalla Hills National Park in Pakistan Using a Remote Sensing Based Approach

Climate change is one of the greatest threats recently, of which the developing countries are facing most of the brunt. In the fight against climate change, forests can play an important role, since they hold a substantial amount of terrestrial carbon and can therefore affect the global carbon cycle. Forests are also an essential source of livelihood for a remarkably high proportion of people worldwide and a harbor for rich global biodiversity. Forests are however facing high deforestation rates. Deforestation is regarded as the most widespread process of land cover change (LCC), which is the conversion of one land cover type to the other land cover type. Most of this deforestation occurs in developing countries. Agricultural expansion has been reported as the most significant widespread driver of deforestation in Asia, Africa, and Latin America. This deforestation is altering the balance of forest carbon stocks and threatening biodiversity. Pakistan is also a low forest cover country and faces high deforestation rates at the same time, due to the high reliance of local communities on forests. Moreover, it is also the most adversely affected by climate change. Agricultural expansion and population growth have been regarded as the most common drivers of deforestation in Pakistan. Financial incentives such as ‘Reducing Emissions from Deforestation and Forest Degradation, and the Role of Conservation of Forest Carbon, Sustainable Management of Forests and Enhancement of Forest Carbon Stocks’ (REDD+) offer hope for developing countries for not only halting deforestation but also alleviating poverty. However, such initiatives require the estimation of biomass and carbon stocks of the forest ecosystems. Therefore, it becomes necessary that the biomass and carbon potentials of the forests are explored, as well as the LCCs are investigated for identifying the deforestation and forest degradation hit areas. Based on the aforementioned, the following research objectives/sub-objectives were investigated in the MHNP, which is adjoined with the capital city of Pakistan, Islamabad; A) Forest Biomass and Carbon Stock Assessment of Margalla Hills National Park (MHNP) A.1) Aboveground Biomass (AGB) and Aboveground Carbon (AGC) assessment of the Subtropical Chir Pine Forest (SCPF) and Subtropical Broadleaved Evergreen Forest (SBEF) using Field Inventorying Techniques A.2) Exploring linear regression relationship between Sentinel-1 (S1) and Sentinel-2 (S2) satellite data with the AGB of SCPF and SBEF A.3) AGB estimation combining remote sensing and machine learning approach B) LC Classification and Land Cover Change Detection (LCCD) of MHNP for the time-period between 1999 and 2019 B.1) LC Classification for the years 1999, 2009 and 2019 using Machine Learning Algorithm B.2) LCCD of MHNP between 1999 to 2019