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The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations. [ABSTRACT FROM AUTHOR]

Mapping and monitoring disturbances in vegetation over large areas demand reliable approaches and accurate end-user maps. Methods and algorithms have been developed to meet satisfactory disturbance map accuracies, and the combination of multiple approaches has shown promise as a reliable alternative to any single method. However, extracting meaningful disturbance information from these combined methods is still challenging. Data variance from environmental conditions and disturbance drivers leads to spatial-temporal heterogeneity in land surfaces over large areas, which results in mapping errors. We evaluated the effectiveness of ensemble classification and data-driven regionalization for mapping vegetation disturbances at a broad scale. Using Google's Earth Engine cloud computing platform, our ensemble approach combines multispectral LandTrendr outputs reflecting preliminary disturbance information in a Random Forest model to map disturbances in Minas Gerais, Brazil. We then applied an unsupervised clustering technique to perform data-driven regionalization of our study area using several sources of environmental and anthropogenic information and analysed gains and losses in map accuracies. Our results indicated gains in accuracy by the ensemble method compared to non-ensemble methods of disturbance mapping, which ranged from 7.3 to 29.9% in overall accuracy at the 5% significance level. Data-driven regionalization addressed complexities arising from variability in vegetation types, local climate, and topography across our study area, identifying climate and seasonal metrics as important variables for reducing uncertainties in vegetation disturbance maps. The integration of these techniques has revealed significant potential for increasing map accuracy and has provided important insights into the development of disturbance mapping methods in heterogeneous environments. [ABSTRACT FROM AUTHOR]

In Brazil, approximately 30% of unscheduled interruptions of energy supply are caused by fires and vegetation interference in the energy distribution networks, resulting in great losses for companies of the electricity sector. To reduce the interruptions caused by these kinds of events, the energy distribution companies continually monitor and manage the vegetation in the vicinity of electric cables. However, due to the great extension and capillarity of the networks, it is not always possible to cover the entire network, and it is necessary to define priority segments to be managed. Taking into the account this context, the main objective of this study was to develop multi-criteria indicators to identify segments of the energy distribution networks with higher priority for management, based on vegetation attributes extracted from remote sensing images. For this purpose, we tested two artificial intelligence algorithms, support vector machine (SVM) and artificial neural networks (ANN), to automatically identify different classes of vegetation using PlanetScope images. Our results showed that the ANN algorithm presented better results for the vegetation classification when compared to the results obtained with the SVM algorithm. The application of the developed indicators showed adherent results, even in densely urbanized areas. We hope that the use of the developed indicators can help Brazilian energy distribution companies in optimizing vegetation management and consequently reducing unscheduled interruptions. [ABSTRACT FROM AUTHOR]

Native vegetation across the Brazilian Cerrado is highly heterogeneous and biodiverse and provides important ecosystem services, including carbon and water balance regulation, however, land-use changes have been extensive. Conservation and restoration of native vegetation is essential and could be facilitated by detailed landcover maps. Here, across a large case study region in Goiás State, Brazil (1.1 Mha), we produced physiognomy level maps of native vegetation (n = 8) and other landcover types (n = 5). Seven different classification schemes using different combinations of input satellite imagery were used, with a Random Forest classifier and 2-stage approach implemented within Google Earth Engine. Overall classification accuracies ranged from 88.6–92.6% for native and non-native vegetation at the formation level (stage-1), and 70.7–77.9% for native vegetation at the physiognomy level (stage-2), across the seven different classifications schemes. The differences in classification accuracy resulting from varying the input imagery combination and quality control procedures used were small. However, a combination of seasonal Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (surface reflectance) imagery resulted in the most accurate classification at a spatial resolution of 20 m. Classification accuracies when using Landsat-8 imagery were marginally lower, but still reasonable. Quality control procedures that account for vegetation burning when selecting vegetation reference data may also improve classification accuracy for some native vegetation types. Detailed landcover maps, produced using freely available satellite imagery and upscalable techniques, will be important tools for understanding vegetation functioning at the landscape scale and for implementing restoration projects. [ABSTRACT FROM AUTHOR]

Questions: Understanding spatial variation in floristic composition is crucial to quantify the extent, patchiness and connectivity of distinct habitats and their spatial relationships. Broad‐scale variation in floristic composition and the degree of uniqueness of different regions remains poorly mapped and understood in several areas across the globe. We here aim to map vegetation heterogeneity in Amazonia. Location: Middle Juruá river region, Amazonas State, Brazil. Methods: We mapped four plant groups by applying machine learning to scale up locally observed community composition and using environmental and remotely sensed variables as predictors, which were obtained as GIS layers. To quantify how reliable our predictions were, we made an assessment of model transferability and spatial applicability. We also compared our floristic composition map to the official Brazilian national‐level vegetation classification. Results: The overall performance of our floristic models was high for all four plant groups, especially ferns, and the predictions were found to be spatially congruent and highly transferable in space. For some areas, the models were assessed not to be applicable, as the field sampling did not cover the spectral or environmental characteristics of those regions. Our maps show extensive habitat heterogeneity across the region. When compared to the Brazilian vegetation classification, floristic composition was relatively homogeneous within dense forests, while floristic heterogeneity in rainforests classified as open was high. Conclusion: Our maps provide geoecological characterization of the regions and can be used to test biogeographical hypotheses, develop species distribution models and, ultimately, aid science‐based conservation and land‐use planning. [ABSTRACT FROM AUTHOR]

• The Cerrado biome is highly complex in terms of LULC patterns rendering classification a big challenge. • Time series analysis and phenological metrics arise as valuable assets to perform LULC mapping. • Contextual information is a key factor to accurately classify vegetation and deforestation in the Cerrado. • We provide frameworks to integrate machine learning and image processing to perform LULC analysis The Brazilian Savannah, or Cerrado, has gained vital importance in the discussions about sustainable land development after the conversion of half of its natural vegetation. For the last two decades, most of the agricultural expansion in Brazil has occurred in this biome. This is related to technological improvements in agriculture as well as to environmental compliance policies that have effectively reduced soybean expansion in the Brazilian Amazon biome. Therefore, remotely sensed imagery, pattern recognition and image processing techniques have been employed to analyze and monitor the land dynamics over Cerrado. In this work, we present a brief review on Land Use and Land Cover mapping (LULC) in the Cerrado biome from an application perspective: natural vegetation, pastureland, agriculture, and deforestation. In this review we selected some studies whose results could contribute to the development of more detailed and accurate LULC maps for the Cerrado biome. [ABSTRACT FROM AUTHOR]

Many classification systems were developed to help in the mission of describing climate, but none of them is able to meet all areas of human activity. A system developed by Holdridge in 1947, called "life zones", is considered the most ecological climate classification system because of the use of biotemperature. Several countries already have a life zones map and the purpose of this study is to classify each Brazilian municipalities according to this system. Temperature, precipitation, altitude and latitude data, provided by Alvares et al. (2013), were applied according to the methodology described by Holdridge (2000). The ecological life zones map was compared with the vegetation map through correlation matrices, which generated indices ranging from 0 to 100%. The results showed that Brazil has 35 life zones. The most common are tropical moist forest and tropical premontane moist forest, covering almost 50% of the country's area. After the analysis of correlations, we could see that 21 LZs have more than 80% of their surfaces covered by two or less types of vegetation. The LZs distribution was consistent to the characteristics of each region, in terms of vegetation and climate; therefore, the Holdridge system can be considered a relevant and specific option for decision making related to agricultural and forestry activities. [ABSTRACT FROM AUTHOR]

The objective of the study was to determine the percentage of green areas in the Northern, Northwestern, and Meia Ponte zones of the city of Goiânia-Goiás, Brazil. The Green Area Index (gai) was applied, in conformity with the methodological principles used by indicators that depend on demographics, expressed by the ratio of green area surfaces per inhabitant. The gai was quantified by interpreting remote sensing images to draw the vegetation cover map, and dasymetric mapping was used for demographic density. Results made it possible to evaluate the relation among spatial distribution of the gai, population quantity, and urban environmental quality. It has been observed an unbalanced correlation between the quantitative-qualitative levels of green areas, which are subordinate to its greenery maintenance and real estate speculation. [ABSTRACT FROM AUTHOR]

Brazil's Araucaria tree (Araucaria angustifolia) is an iconic living fossil and a defining element of the Atlantic Forest global biodiversity hotspot. But despite more than two millennia as a cultural icon in southern Brazil, Araucaria is on the brink of extinction, having lost 97% of its extent to 20th‐century logging. Although logging is now illegal, 21st‐century climate change constitutes a new—but so far unevaluated—threat to Araucaria's future survival. We use a robust ensemble modelling approach, using recently developed climate data, high‐resolution topography and fine‐scale vegetation maps, to predict the species' response to climate change and its implications for conservation on meso‐ and microclimate scales. We show that climate‐only models predict the total disappearance of Araucaria's most suitable habitat by 2070, but incorporating topographic effects allows potential highland microrefugia to be identified. The legacy of 20th‐century destruction is evident—more than a third of these likely holdouts have already lost their natural vegetation—and 21st‐century climate change will leave just 3.5% of remnant forest and 28.4% of highland grasslands suitable for Araucaria. Existing protected areas cover only 2.5% of the surviving microrefugia for this culturally important species, and none occur in any designated indigenous territory. Our results suggest that anthropogenic climate change is likely to commit Araucaria to a second consecutive century of significant losses, but targeted interventions could help ensure its survival in the wild. [ABSTRACT FROM AUTHOR]

Our aims were to quantify and map the plant ecoregions of the Atlantic Forest, a biodiversity hotspot that covers ca 150 million ha in eastern South America. We used a data set on the distribution of 4378 shrub and tree species across 711 localities. Plant ecoregions were identified using analyses of species turnover for both species occurrences and relative abundances. We interpolated NMDS axes of compositional variation over the entire the Atlantic Forest extent, and then classified the compositional dissimilarity according to the number of biogeographical ecoregions previously identified through K‐means analyses. We assessed the ability of environmental, historical vegetation stability and the current human footprint to explain the occurrence of the identified ecoregions through multinomial logistic regression models. We identified 21 spatially cohesive occurrence and 14 abundance ecoregions. Aridity, soil and historical biome stability were retained in the best model explaining both occurrence and abundance ecoregions. Broad compositional zones were identified through UPGMA cluster analysis of ecoregions, and formed north and south compositional blocks. Our work confirms the existence of a broad north–south divide within the Atlantic Forest, previously suggested based on climatic and amphibious data. Differences between the occurrence and abundance maps suggest the location of transition zones to neighbouring domains and endemism centres. Due to the aggregate nature of our analyses, site‐level disturbance degree was not considered, implying that human impacts could be broader then we could detect. There was limited overlap between our results and previous Atlantic Forest regionalization efforts, indicating that multi‐taxa, physiognomic and environmental regionalization schemes based on expert opinion or vegetation maps are poor proxies for compositional ecoregions. [ABSTRACT FROM AUTHOR]

Shading and N fertilization affect fruit distribution in cotton (Gossypium hirsutum L.), but there is no detailed information on earliness of crop maturity according to phenological development. The aim of this work was to evaluate the effect of shading at early flowering and N top‐dressing rates on relative cotton earliness using plant mapping. Field experiments were conducted in Itapeva (16 d of shading; and 0, 60, 120, and 180 kg N ha−1) and Chapadão do Sul (17 d of shading; 0, 80, and 160 kg N ha−1; and early‐ and full‐season cultivars), Brazil. Seed cotton yield was grouped by phenological position (PP) according to the standard phenological scale, for interpolation calculation at each 10% increment in accumulated harvestable yield (Ac), weighted average PP (PPwa) determination, and logistic‐regression analysis. Crop maturity earliness was predicted based on the reduction in PPwa and in PP. Shading increased PP up to 10 and 30% of Ac due to a decrease of 33 and 40% in the number of bolls on early fruiting sites in Itapeva and Chapadão do Sul, respectively, but did not affect PPwa. Increases in PP up to high Ac percentages and in PPwa values were observed at the two higher N rates in both experiments, mainly due to lower and higher boll number at earlier and later fruiting sites, respectively. Short‐term shading during early flowering of cotton changed yield distribution by decreasing boll number on early fruiting sites, but did not affect the earliness of crop maturity. Earliness was decreased by high N rates due to higher cumulative seed cotton yield at later fruiting sites. [ABSTRACT FROM AUTHOR]

Global change has tremendous impacts on savanna systems around the world. Processes related to climate change or agricultural expansion threaten the ecosystem’s state, function and the services it provides. A prominent example is the Brazilian Cerrado that has an extent of around 2 million km 2 and features high biodiversity with many endemic species. It is characterized by landscape patterns from open grasslands to dense forests, defining a heterogeneous gradient in vegetation structure throughout the biome. While it is undisputed that the Cerrado provides a multitude of valuable ecosystem services, it is exposed to changes, e.g. through large scale land conversions or climatic changes. Monitoring of the Cerrado is thus urgently needed to assess the state of the system as well as to analyze and further understand ecosystem responses and adaptations to ongoing changes. Therefore we explored the potential of dense Landsat time series to derive phenological information for mapping vegetation gradients in the Cerrado. Frequent data gaps, e.g. due to cloud contamination, impose a serious challenge for such time series analyses. We synthetically filled data gaps based on Radial Basis Function convolution filters to derive continuous pixel-wise temporal profiles capable of representing Land Surface Phenology (LSP). Derived phenological parameters revealed differences in the seasonal cycle between the main Cerrado physiognomies and could thus be used to calibrate a Support Vector Classification model to map their spatial distribution. Our results show that it is possible to map the main spatial patterns of the observed physiognomies based on their phenological differences, whereat inaccuracies occurred especially between similar classes and data-scarce areas. The outcome emphasizes the need for remote sensing based time series analyses at fine scales. Mapping heterogeneous ecosystems such as savannas requires spatial detail, as well as the ability to derive important phenological parameters for monitoring habitats or ecosystem responses to climate change. The open Landsat and Sentinel-2 archives provide the satellite data needed for improved analyses of savanna ecosystems globally. [ABSTRACT FROM AUTHOR]

The study examined the capability of dual-polarization SAR data for forest cover mapping and change assessment in the Brazilian Amazon Forest regions. Shuttle Imaging Radar (SIR)-C and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) data were analysed to map and quantify deforestation. The images were classified using hybrid classifier, where each land cover was grouped in various spectral sub-classes interpreted on the imagery and later merged together to generate the desired land cover classes. The classification accuracy for forest was reasonably high (>90%). The technique applied in this study can be extended for operational mapping and monitoring of deforestation in the tropics, particularly for those regions which are often covered by cloud. [ABSTRACT FROM AUTHOR]

Litter production is related to canopy processes including the timing and amount of leaf development, reproduction and net primary production. However, quantifying spatial and temporal patterns in litter production is complicated in Amazonian semi-deciduous forests because of high spatial heterogeneity and seasonal variation in rainfall. Here, we use monthly measurements of litter production and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) composites over a 6-year period to assess whether MODIS can be used to quantify litter production of tropical semi-deciduous forests. Original MODIS NDVI and EVI values were poorly related to the seasonal periodicity in litter production, but after using singular spectrum analysis (SSA) signal extraction techniques, clear relationships between litter production and the NDVI and EVI emerged. These results indicate that MODIS NDVI and EVI data are useful for detecting temporal patterns in litter production for Amazonian semi-deciduous forests if signal extraction analyses such as SSA are conducted. [ABSTRACT FROM AUTHOR]

In order to evaluate the satellite image patterns between such cool season cereals cultivated areas intended for grazing or grain production, Normalized Difference Vegetation Index (NDVI) temporal profiles were analyzed. This data was acquired from twenty nine wheat and annual ryegrass cultivated areas in the states of Rio Grande do Sul and Paraná. For each area, the cultivated species (wheat or ryegrass), as well as the respective central point coordinates, acquired via Global Positioning System (GPS) was informed. NDVI values were extracted over each monitored area from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor images, with spatial resolution of 250 meters. The NDVI temporal profiles showed that grain production areas have a typical agricultural field spectral pattern. The same pattern was not observed for the grazing pasture areas. The differences observed in these temporal patterns are defined by the changes that grazing has imposed on the phenology and morphology of these plants. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the area of use, relative abundance, spatial distribution and area size of the white-lipped peccary (Tayassu pecari Link, 1795) along the Araucaria Ecological Corridor. The research was carried out in 14 properties located on a spatial section of 659 km2 situated in the Third Plateau of the state of Paraná. The points of occurrence of peccary were mapped based on interviews with local inhabitants and on tracks, paths, and other signs of its presence in the sampling areas. The areas were monthly visited (18 months) and the points (UTM) with signs of the species were marked by GPS and plotted on the map. The relative abundance index was estimated based on all the events in which the species was seen. Considering the values of relative abundance index for both groups of peccary studied, an average of 119 individuals was estimated for an area of 232 km2. The relative density obtained was considered low, especially considering that the peccary is a social species. It is possible that hunting is the main cause of the population decline of the species in the studied region. [ABSTRACT FROM AUTHOR]

The relationships between 18 narrowband Hyperion vegetation indices and basal area and canopy cover values of eight Brazilian savannah physiognomies were evaluated. The best fitting regression relationships were used to estimate these parameters in the image, which were projected over a digital elevation model (DEM) and compared with an available vegetation map. Results showed that the gradual increase of biophysical parameters, from savannah grassland to semi-deciduous forest, produced large correlation coefficients with most of the indices, especially the near-infrared/red-based ones (e.g. NDVI and simple ratio). Larger canopy cover and basal area values were associated with tall woodland and semi-deciduous forest and smaller values with grasslands and shrub savannah. When projected over the DEM, the predominance of rupestrian fields at high altitudes and of tall woodland and semi-deciduous forest at low altitudes was observed. [ABSTRACT FROM AUTHOR]

This paper describes a participatory mapping method field tested with agro-extractive settlements in the Bolivian Amazon. A regional transition from customary to formal property rights resulting from sweeping 1996 land tenure reforms has led to confusion and conflicts over resource rights, a problem compounded by recent high market prices for Brazil nuts. In response to community requests to clarify resource rights to Brazil nut trees, CIFOR offered to train community members to map trees, trails and other key features themselves. This experience indicates that local residents can map their resources in an effective and efficient way and in the process gather necessary information to mediate competing claims, demonstrate their legitimate resource claims to external stakeholders and make management decisions. We argue that maps and properties are more likely to be seen as legitimate reflections of de facto rights if local stakeholders are involved as a group from the outset. [ABSTRACT FROM AUTHOR]

A spectral linear-mixing model using Landsat ETM+ imagery was undertaken to estimate fraction images of green vegetation, soil and shade in an indigenous land area in the state of Mato Grosso in the central-western region of Brazil. The fraction images were used to classify different types of land use and vegetation cover. The fraction images were classified by the following two methods: (a) application of a segmentation based on the region-growing technique; and (b) grouping of the regions segmented using the per-region unsupervised classifier named ISOSEG. Adopting a 75% threshold, ISOSEG generated 44 clusters that were grouped into eight land-use and vegetation-cover classes. The mapping achieved an average accuracy of 83%, showing that the methodology is efficient in mapping areas of great land-use and vegetation-cover diversity, such as that found in the Brazilian cerrado (savanna). [ABSTRACT FROM AUTHOR]

The aim of this paper is show some results of vegetation index calculated to the cities of Salgueiro, Mirandiba, Carnaubeiras da Penha and Floresta, located in the semi-arid of Pernambuco, Brazil. Was used images from sensor TM-Landsat 7, whose image is the orbit/point 216/66, date of obtaining January 23, 2003. The Normalized Difference Vegetation Index (NDVI) has been widely used in various operational applications, including mapping of vegetation, land use classification, detection and tracking of environment changes. The NDVI can be easily linked to certain parameters of vegetation such as phytomass, leaf area, productivity and photosynthetic activity, percentage of green cover, and others. This is a very simple index, resulting from the difference between the reflectance of the near infrared (NIR) and reflectance in the red (R), divided by the sum of the two reflectance respectivement. [ABSTRACT FROM AUTHOR]

Using map algebra, in the GIS (geographic information system) environment, this study integrates the B-RAMS, Brazilian Regional Atmospheric Models Software (CPTEC, 2005) climate model data with remote sensing data, intending to obtain a wildfire hazard map. The Taquari River Springs Park (TRSP) was chosen as a case study, due to the presence of springs which are considered important contributors to the Upper Paraguay River Basin, and it also contains essential remnants of the Cerrado Biome. The B-RAMS model has provided relative humidity, components of the horizontal wind and temperature. The TRSP land cover was identified by object oriented classification of a LANDSAT ETM+ image, supported by field observations. From the land cover phytophysionomic type characterization, a forest wildfire fuel map has been elaborated. The integration of the different maps has been made using a GIS, and a new map with its associated GIS database was generated showing the most vulnerable zones to wildfire hazard. [Copyright &y& Elsevier]

Applications of L-band SAR data to map deforestation are generally based on the assumption that undisturbed forests consistently exhibit higher radar backscatter than deforested areas. In this Letter we show that depending on the stage of the deforestation process (slashing, burning and terrain clearing), this assumption is not always valid, and deforested areas may display a stronger radar return backscatter than primary forest. The analysis of multitemporal SAR images, supported by several Landsat Thematic Mapper (TM) images and field knowledge, showed that wood materials left following the deforestation practices function as corner reflectors, causing an initial increase in the radar backscatter, which then subsequently decreases over time as the debris on these fields are removed. [ABSTRACT FROM AUTHOR]

Describes the protected areas program of Brazil. Brazilian strategy to select new protected areas; Focus on areas of high biological importance and prioritization of those areas under strong anthropogenic pressure; Map of biological diversity conservation priority areas; Mapping the vegetation cover for all of the biomes.

The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere-Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados' physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) has been associated with vegetation productivity and evapotranspiration in mathematical models. At a regional level LAI can be estimated with enough accuracy through spectral vegetation indices (SVIs), derived from remote sensing imagery. However, there are few studies showing LAI-SVI relationships in subtropical regions. The aim of this work was to examine the relationship between LAI and SVIs in a subtropical rural watershed (in Piracicaba, State of São Paulo, Brazil), for different land covers, and to use the best relationship to generate a LAI map for the watershed. LAI was measured with a LAI-2000 instrument in 32 plots on the field in areas of sugar cane, pasture, corn, eucalypt, and riparian forest. The SVIs studied were Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI), calculated from Landsat-7 ETM+ data. The results showed LAI values ranging from 0.47 to 4.48. LAI-SVI relationships were similar for all vegetation types, and the potential model gave the best fit. It was observed that LAI-NDVI correlation ( r 2 =0.72) was not statistically different from LAI-SR correlation ( r 2 =0.70). The worst correlation was obtained by LAI-SAVI ( r 2 =0.56). A map was generated for the study area using the LAI-NDVI relationship. This was the first LAI map for the region. [ABSTRACT FROM AUTHOR]

Attempts to map vegetation types, especially pasture, from satellite sensor data in tropical and sub-tropical regions very often have limited success. This study analyses the accuracy of two classifications of Landsat Thematic Mapper (TM), with the aim of distinguishing the pastures from other vegetation classes in a meso-scale basin (12 400 km[sup 2], Piracicaba basin, Brazil). The initial classification is based on non-supervised clustering of the images. The delimited classes are interpreted and merged by comparison with standard spectra from NASA. The second classification is a parallelepiped partition based on the merged clusters issued from the first one. The two classifications are compared by validation with 287 random field observations selected within the whole catchment. The results are discussed, analysing the spectral behaviour variability of the pasture class. [ABSTRACT FROM AUTHOR]

Discusses about fraction image of vegetation in Mato Grosso, Brazil. Characteristics of fraction image; Color composites of fraction image; Illustration of composite fraction image.

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. [ABSTRACT FROM AUTHOR]

Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there is a large range of datasets for monitoring, analyzing, and detecting disturbances, many methods have been well-studied and successfully implemented. However, factors such as the vegetation type, input data, and change detection method can significantly alter the outcomes of a disturbance-detection study. We evaluated the spatial agreement of disturbance maps provided by the Breaks For Additive Season and Trend (BFAST) algorithm, evaluating seven spectral indices in three distinct vegetation domains in Brazil: Atlantic forest, savanna, and semi-arid woodland, by assessing levels of agreement between the outputs. We computed individual map accuracies based on a reference dataset, then ranked their performance, while also observing their relationships with specific vegetation domains. Our results indicated a low rate of spatial agreement among index-based disturbance maps, which itself was minimally influenced by vegetation domain. Wetness indices produced greater detection accuracies in comparison to greenness-related indices free of saturation. The normalized difference moisture index performed best in the Atlantic forest domains, yet performed poorest in semi-arid woodland, reflecting its specific sensitivity to vegetation and its water content. The normalized difference vegetation index led to high disturbance detection accuracies in the savanna and semi-arid woodland domains. This study offered novel insight into vegetation disturbance maps, their relationship to different ecosystem types, and corresponding accuracies. Distinct input data can produce non-spatially correlated disturbance maps and reflect site-specific sensitivity. Future research should explore algorithm limitations presented in this study, as well as the expansion to other techniques and vegetation domains across the globe. [ABSTRACT FROM AUTHOR]

• Automated identification of soybean plants via orbital sensors. • EOS-MODIS, MSI and OLI multitemporal images. • Soybean discrimination by phenological phases. • Spatial distribution of soybean for public policy purposes. Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr−1 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates. [ABSTRACT FROM AUTHOR]