DeforestationDeforestation or forest clearance is the removal of a forest or stand of trees from land that is then converted to non-forest use. Deforestation can involve conversion of forest land to farms, ranches, or urban use. The most concentrated deforestation occurs in tropical rainforests. About 31% of Earth's land surface is covered by forests at present. This is one-third less than the forest cover before the expansion of agriculture, with half of that loss occurring in the last century.
Deforestation and climate changeDeforestation is a primary contributor to climate change, and climate change affects forests. Land use changes, especially in the form of deforestation, are the second largest anthropogenic source of atmospheric carbon dioxide emissions, after fossil fuel combustion. Greenhouse gases are emitted during combustion of forest biomass and decomposition of remaining plant material and soil carbon. Global models and national greenhouse gas inventories give similar results for deforestation emissions.
Spatial analysisSpatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures.
Tropical forestTropical forests (a.k.a. jungle) are forested landscapes in tropical regions: i.e. land areas approximately bounded by the tropic of Cancer and Capricorn, but possibly affected by other factors such as prevailing winds. Some tropical forest types are difficult to categorize. While forests in temperate areas are readily categorized on the basis of tree canopy density, such schemes do not work well in tropical forests. There is no single scheme that defines what a forest is, in tropical regions or elsewhere.
Tropical rainforestTropical rainforests are rainforests that occur in areas of tropical rainforest climate in which there is no dry season – all months have an average precipitation of at least 60 mm – and may also be referred to as lowland equatorial evergreen rainforest. True rainforests are typically found between 10 degrees north and south of the equator (see map); they are a sub-set of the tropical forest biome that occurs roughly within the 28-degree latitudes (in the equatorial zone between the Tropic of Cancer and Tropic of Capricorn).
Seasonal tropical forestSeasonal tropical forest, also known as moist deciduous, semi-evergreen seasonal, tropical mixed or monsoon forest, typically contains a range of tree species: only some of which drop some or all of their leaves during the dry season. This tropical forest is classified under the Walter system as (i) tropical climate with high overall rainfall (typically in the 1000–2500 mm range; 39–98 inches) and (ii) having a very distinct wet season with (an often cooler “winter”) dry season.
Tropical and subtropical dry broadleaf forestsThe tropical and subtropical dry broadleaf forest is a habitat type defined by the World Wide Fund for Nature and is located at tropical and subtropical latitudes. Though these forests occur in climates that are warm year-round, and may receive several hundred centimeters of rain per year, they have long dry seasons that last several months and vary with geographic location. These seasonal droughts have great impact on all living things in the forest.
DataIn common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.
ForecastingForecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and resolution itself.
Tropical and subtropical moist broadleaf forestsTropical and subtropical moist broadleaf forests (TSMF), also known as tropical moist forest, is a subtropical and tropical forest habitat type defined by the World Wide Fund for Nature. TSMF is generally found in large, discontinuous patches centered on the equatorial belt and between the Tropic of Cancer and Tropic of Capricorn, TSMF are characterized by low variability in annual temperature and high levels of rainfall of more than annually. Forest composition is dominated by evergreen and semi-deciduous tree species.
Deep learningDeep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Amazon rainforestThe Amazon rainforest, also called Amazon jungle or Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome that covers most of the Amazon basin of South America. This basin encompasses , of which are covered by the rainforest. This region includes territory belonging to nine nations and 3,344 formally acknowledged indigenous territories. The majority of the forest, 60%, is in Brazil, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Bolivia, Ecuador, French Guiana, Guyana, Suriname, and Venezuela.
Weather forecastingWeather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere, land, and ocean and using meteorology to project how the atmosphere will change at a given place.
Language modelA language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.
Tropical vegetationTropical vegetation is any vegetation in tropical latitudes. Plant life that occurs in climates that are warm year-round is in general more biologically diverse that in other latitudes. Some tropical areas may receive abundant rain the whole year round, but others have long dry seasons which last several months and may vary in length and intensity with geographic location. These seasonal droughts have great impact on the vegetation, such as in the Madagascar spiny forests. Rainforest vegetation is categorized by five layers.
Ecosystem serviceEcosystem services are the many and varied benefits to humans provided by the natural environment and healthy ecosystems. Such ecosystems include, for example, agroecosystems, forest ecosystem, grassland ecosystems, and aquatic ecosystems. These ecosystems, functioning in healthy relationships, offer such things as natural pollination of crops, clean air, extreme weather mitigation, and human mental and physical well-being.
Autoregressive modelIn statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, behavior, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term); thus the model is in the form of a stochastic difference equation (or recurrence relation which should not be confused with differential equation).
Biodiversity lossBiodiversity loss includes the worldwide extinction of different species, as well as the local reduction or loss of species in a certain habitat, resulting in a loss of biological diversity. The latter phenomenon can be temporary or permanent, depending on whether the environmental degradation that leads to the loss is reversible through ecological restoration/ecological resilience or effectively permanent (e.g. through land loss).
Data scienceData science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Autoregressive integrated moving averageIn statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. To better comprehend the data or to forecast upcoming series points, both of these models are fitted to time series data. ARIMA models are applied in some cases where data show evidence of non-stationarity in the sense of mean (but not variance/autocovariance), where an initial differencing step (corresponding to the "integrated" part of the model) can be applied one or more times to eliminate the non-stationarity of the mean function (i.
