Mapeamento da fitomassa da caatinga do seridó pelos índices de área de planta e de vegetação da diferença normalizada

Phytomass is a critical information for economic and environmental activities like the establishment of policies for timber resources, forest management, studies of plant nutrient cycling, CO2 sink, among other. The phytomass of a Caatinga area was obtained by an empirical method using normalized difference vegetation index (NDVI) of Landsat images, the plant area index (PAI) and the phytomass inventory. At a first stage, linear, logarithmic and non-linear models were developed and tested. Bush and tree specimens were considered in the study, so that most of the individuals that contribute to the spectral answer detected by satellite images were included. At a second stage, the orbital parameter NDVI was used to map the PAI, which was used to map the phytomass, based on the relationship of this phytomass as a function of PAI. The residues between measurements and estimates based on NDVI varied from 0 to 84%, while the residues of total dry weight of phytomass per ha obtained by mapping and by dendrometrical equations varied from 5 to 104%, with a large trend of 166 and 448% in open Caatinga areas, due to the contribution of the herbaceous stratum to NDVI.A fitomassa, principalmente arbórea, é informação necessária em atividades econômicas e ambientais, como políticas de uso do recurso madeireiro, manejo florestal, estudos de ciclagem de nutrientes, absorção de CO2, entre outros. A finalidade deste estudo foi a verificação de um método empírico para o mapeamento da fitomassa da Caatinga do Seridó, integrando-se um inventário de fitomassa, o índice de área de planta (IAP) e o índice de vegetação da diferença normalizada (NDVI), por meio de imagens Landsat TM. Na primeira etapa foram desenvolvidos e testados modelos lineares, logarítmicos e não lineares. A abordagem de tamanho foi arbustiva e arbórea, incluindo-se a maior parte dos indivíduos que contribuem na resposta espectral mensurada por imagens de satélite. Em uma segunda etapa utilizamos o parâmetro orbital, NDVI, para o mapeamento do IAP, que por sua vez, foi utilizado para mapear a fitomassa. Os desvios entre mensurações de IAP e estimativas a partir do NDVI, variaram de 0 a 84%, enquanto que os desvios entre Peso Seco Total de Fitomassa por ha obtidos pelo mapeamento e por equações dendrométricas, variaram de 5 a 104%, com grandes tendências de 166 e 448% para áreas de caatinga aberta, provocada pela contribuição do estrato herbáceo no NDVI

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Characterization of drainage patterns from remote sensing techniques for use in reconnaissance surveys (high intensity) of soils.

O aumento considerável da importância dos levantamentos pedoiógicos em nível mais detalhatí ) tem ievado os pesquisadores a agilizarem seus trabalhos, utilizando produtos de sensoriamento remoto. O presente estudo, realizado com o auxílio de imagens TM/LANDSAT-5, foi desenvolvido na Região do Aito Rio Sucuru, no SemiÁrido Paraibano, com o propósito de avaliar a pedologia da área, com base em dados geológicos, pedoiógicos e de ocupação da terra, e verificar suas implicações sobre a composição e as características da rede de drenagem das Unidades de Mapeamento, obtidas no Levantamento de Reconhecimento (Alta Intensidade) de Soios, na escala de 1:100.000. Para tanto, aplicou-se uma sistemática de interpretação de dados de sensores de baixa resolução espacial, associada a um controle de campo, visando a identificação das unidades de mapeamento, bem como, a aquisição de informações complementares às análises visuais, feitas sobre as imagens orbitais utilizadas. A partir do mapa pedoíógico elaborado, estudou-se a composição e as características da rede de drenagem obtida das imagens TM/LANDSAT-5, utilizando-se a metodologia das Unidades de Mapeamento. Posteriormente testou-se esta metodologia em cartas topográficas da SUDENE e comparou-se os seus resultados com aqueles obtidos a partir do Método das Amostras Circulares No estudo pedoíógico da área foram identificadas 73 manchas de solo, agrupadas em 16 Unidades de Mapeamento. O estudo da drenagem nestas Unidades de Mapeamento revelou resultados satisfatórios no tocante à diferenciação entre elas, como por exemplo, entre as unidades constituídas por solos PODZÓLiCOS VERMELHO AMARELO EUTRÓFICOS e REGOSSOLOS EUTRÓFICOS e aquelas constituídas por BRUNOS NÃO CÁLCICOS e LITÓLICOS EUTRÓFICOS Com base no mapa de drenagem foram verificados resultados positivos para os parâmetros quantitativos da drenagem, com destaque para a densidade de drenagem e a freqüência de rios. Como resultado final do trabalho conclui-se que, embora o Método das Amostras Circulares seja eficiente na distinção dos solos, a metodologia proposta neste trabalho, utilizando as Unidades de Mapeamento na caracterização da rede de drenagem permitiu resultados mais satisfatórios, posto que possibilitou a análise das Unidades de Mapeamento independente da sua morfologia, estrutura e formato.Detailed soil surveys have attached growing importance in recent years. In this context, remote sensing products and techniques have been used successfully for a number of researchers to speed up analysis and mapping processes. The present study was based on 1984-TM/LANDSAT-5 imagery of the region of Alto Rio Sucuru - Semi-arid Zone of the State of Paraíba. It reports a pedological analysis of this area, presents geological, pedological and land use information and studies their influence on the composition and the characteristics of the drainage pattens of the soil units presented on the High-Intensity Reconnaissance Survey made of on a scale of 1:100.000. An interpretation methodology of low spatial resolution data as well as a ground work approach were both applied. Using the method of the mapping unit, the composition and the characteristics of the drainage pattern mapped from the TM/LANDSAT-5 imagery were studied. This methodology was applied later on topographical maps elaborated by SUDENE and compared to the method of circular samples. A 73 soil spots set, grouped into 16 mapping units, was identified. The study of the drainage pattern of these units revealed a good unit differentiation results, e.g. between the units composed by PODZÓLICOS VERMELHO AMARELO EUTRÓFICOS (Eutrophic Red Yellow Podzolic) and REGOSSOLCS EUTRÓFiCOS (Eutrophic Regosois) and those composed by BRUNOS NÃO CÁLCICOS (Non Calcic Brown Soils) and LITÓLSCOS EUTRÓFICOS (Eutrophic Lithosols). Satisfactory results for the quantitative drainage parameters, specially for the drainage density and river frequency, were remarked from the drainage map Although the efficiency of the method of circular samples on soil differentiation was confirmed, the methodology proposed here presented more satisfactory results, by allowing a better analysis of mapping units irregardless their morphology, structure and shape

Mapeamento da fitomassa da caatinga do núcleo de desertificação do Seridó, pelo Índice de Área de Planta (IAP) e o Índice de Vegetação da Diferença Normalizada (NDVI), obtido com dados do sensor Landsat 7 TM

The phytomass of an area under desertification process, as well as the elaboration of a phytomass inventory, the plant area index (PAI)and the normalised difference vegetation index (NDVI), were obtained by an empirical method using Landsat images. In the first stage, linear, logarithmic and non-linear models were developed and tested. Bush and tree specimens were considered in the study, so that most of the individuals that contribute to the spectral answer detected by satellite images were included. In a second stage, orbital parameters (NDVI)were used to map the PAI, which, by its turn, was used to map the phytomass, based on the relationship of this phytomass in function of PAI. The residues between measurements and estimates based on NDVI varied from 1 to 45, while the residues of total dry weight of phytomass per ha obtained by mapping and by dendrometrical equations varied from 4 to 78, with a large tendency of 257 in area of open Caatinga, due to the contribution of the herbaceous stratum in NDVI.Pages: 1563-157

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data