Luis Marcelo Tavares de Carvalho

Application of LIDAR to forest inventory for tree count in stands of Eucalyptus sp

Light Detection and Ranging, or LIDAR, has become an effective ancillary tool to extract forest inventory data and for use in other forest studies. This work was aimed at establishing an effective methodology for using LIDAR for tree count in a stand of Eucalyptus sp. located in southern Bahia state. Information provided includes in-flight gross data processing to final tree count. Intermediate processing steps are of critical importance to the quality of results and include the following stages: organizing point clouds, creating a canopy surface model (CSM) through TIN and IDW interpolation and final automated tree count with a local maximum algorithm with 5 x 5 and 3 x 3 windows. Results were checked against manual tree count using Quickbird images, for verification of accuracy. Tree count using IDW interpolation with a 5x5 window for the count algorithm was found to be accurate to 97.36%. This result demonstrates the effectiveness of the methodology and its use potential for future applications.

Análise espaço-temporal da evapotranspiração de referência para Minas Gerais

Objetivou-se com esta pesquisa, analisar a demanda hidrica em Minas Gerais, representada pela evapotranspiracao de referencia (ET0), durante o ano. Os valores de ET0 foram estimados pelo metodo de Penman-Monteith-FAO a partir de dados diarios originados de registros de 42 estacoes climatologicas do Instituto Nacional de Meteorologia (INMET) referentes a um periodo de 17 anos (1961 a 1978). No geral, os resultados mostraram que a ET0 e bastante variavel em Minas Gerais, chegando a apresentar valores medios de 914 ate valores de 1.677 mm ano-1. As maiores variacoes, tanto espaciais como temporais, sao registradas no norte do Estado, onde tambem ocorrem os maiores valores de ET0. O Estado de Minas Gerais apresenta um deficit hidrico anual em aproximadamente 50% de sua area total. Os meses que apresentaram as maiores e menores demandas hidricas no Estado foram janeiro e junho, respectivamente. Em funcao da nitida distincao que apresentaram os dados de ET0 geoespacializados nas regioes do Estado de Minas Gerais, o conhecimento do correto valor da ET0 em cada localidade trara beneficios aos produtores no manejo da irrigacao.

Levantamento de reconhecimento com apoio digital dos solos do município de Nazareno-MG

This research was carried out in Nazareno County, State of Minas Gerais, which has been subjected to serious environmental problems due to inadequate soil use. Information about soils is necessary for the appropriate monitoring when implementing measures to mitigate or correct the problem. The objective of this work was to produce a soil recognition map. It is intended to subsidize future and more detailed mapping of the area. The choice of the work area was due to the high degree that the erosive processes reached in Nazareno. Geographic Information Systems (GIS) aided the mapping process and the investigation, minimizing costs and increasing the quality and precision of the results. Five soil mapping units were defined: LVAd (58%) was predominant, followed by CXbd2 (16%), and CXbd1 (15%). The unit LVdf (7%) occurs in the north of the municipal district and the unit GXbd occupies the smallest area (2%).

Sustainable Forest Management of Native Vegetation Remnants in Brazil

A region’s species diversity is an important factor, resulting as a component of social and economical development when used wisely. The correct commercialization of a region’s natural resources guaranties the preservation of local culture and habitat maintenance by means of the obtained income. Hence, the idea of sustainability arises, a widespread theoretical theme which is beginning to gain force in Brazil’s consumer market. The principal conceptual shift was the erroneous notion that timber resources from forests are inexhaustible, since the processes of recomposition/restoration naturally occur after exploration. Indeed a system is capable of regeneration, but this is tied to a series of factors that are usually not respected in areas illegally explored. According to a conference realized in Melbourne by Raison et al. (2001), the concept of sustainability must encompass social and economic conditions such as: respect the forest growth rate; legislation based control; productive capacity; ecosystem’s health and vitality; soil and water resource protection; carbon balance and preservation of biological diversity. Under this scenario, Brazil presents great potential for the use of its natural resources. This is due to the country’s vast territorial extension (8.5 million km2) and high diversity of recurrent vegetation physiognomies. The country possesses about 5.2 million km2 of forest land (60% of its territory), of this total, 98.7% consists of natural forest formation and 1.3% of planted forests. The forest types found in Brazil can be classified as Cerrado (Brazilian savanna), Amazonia (tropical rainforest), Mata Atlântica (Atlantic rainforest), Pantanal (wetlands) and Caatinga (semi-arid forest) as well of transition areas which promotes a mixture of habitats. In many cases, the deforestation of these environments is associated with illegal logging practices coupled with agriculture and cattle-raising. The damage caused by this include modifications of the carbon cycle and consequential rise of CO2 emissions; forest fragmentation; alteration of the hydraulic cycle; species extinction; rural exodus and loss of local fauna and flora diversity. Possibly the most logical use of these forests is the application of sustainable forest management for wood production destined for fire wood, charcoal and logs for industrial purposes. The motives for this strategy are evident, involving aspects attached to the reduction

Object-based land-cover change detection applied to Brazilian seasonal savannahs using geostatistical features

ABSTRACT A new method for remote-sensing land-use/land-cover (LULC) change detection is proposed to eliminate the effects of forest phenology on classification results. This method is insensitive to spectral changes caused by vegetation seasonality and uses an object-based approach to extract geostatistical features from bitemporal Landsat TM (Thematic Mapper) images. We first create image objects by multiresolution segmentation to extract geostatistical features (semivariogram parameters and indices) and spectral information (average values) from NDVI (normalized difference vegetation index), acquired in the wet and dry seasons, as input data to train a Support Vector Machine algorithm. We also used the image difference traditional change-detection method to validate the effectiveness of the proposed method. We used two classes: (1) LULC change class and (2) seasonal change class. Using the most geostatistical features, the change detection results are considerably improved compared with the spectral features and image differencing technique. The highest accuracy was achieved by the sill (σ2 overall variability) semivariogram parameter (95%) and the AFM (area first lag–first maximum) semivariogram index (88.33%), which were not affected by vegetation seasonality. The results indicate that the geostatistical context makes possible the use of bitemporal NDVI images to address the challenge of accurately detecting LULC changes in Brazilian seasonal savannahs, disregarding changes caused by phenological differences, without using a dense time series of remote-sensing images. The challenge of extracting accurate semivariogram curves from objects of long and narrow shapes requires further study, along with the relationship between the scale of segmentation and image spatial resolution, including the type of change and the initial land-cover class.

Modelagem preditiva de distribuição de espécies pioneiras no estado de Minas Gerais

The objective of this work was to determine the potential distribution of 23 pioneer species in the state of Minas Gerais, Brazil, as well as to identify the environmental variables that influence their distributions. The Maxent algorithm was chosen to associate the occurrence of species with the following bioclimatic variables: diurnal temperature variation, isothermality, temperature seasonality, driest month precipitation, precipitation seasonality (coefficient of variation), and actual evapotranspiration. The normalized difference vegetation index (NDVI), flora conservation status, and the spatial heterogeneity of vegetation types were also evaluated, besides erodibility (susceptibility of soil to erosion), groundwater availability, soil texture, organic matter content, mineral occurrence (existing mineral species by lithological unit), pedological simplified map, slope and altitude. The species Anadenanthera colubrina was the most suitable for the Caatinga biome, followed by Casearia sylvestris and Plathymenia reticulate , indicated for the Atlantic Forest and the Cerrado biomes, respectively. The use of Maxent is recommended as a tool to guide conservation plans that require the indication of species, aiming to recover degraded or deforested vegetation areas.

Determinação do volume de madeira em povoamento de eucalipto por escâner a laser aerotransportado

The objective of this work was to evaluate the possibility of estimating the diameter at breast height (DBH) with tree height and number data derived from airborne laser scanning (LiDAR, light detection and ranging) dataset, and to determine the timber volume of an Eucalyptus sp. stand from these variables. The total number of detected trees was obtained using a local maxima filtering. Plant height estimated by LiDAR showed a nonsignificant tendency to underestimation. The estimate for DBH was coherent with the results found in the forest inventory; however, it also showed a tendency towards underestimation due to the observed behavior for height. The variable number of stems showed values close to the ones observed in the inventory plots. LiDAR underestimated the total timber volume in the stand in 11.4%, compared to the total volume delivered to the industry. The underestimation tendency of tree height (5% mean value) impacted the individual tree volume estimate and, consequently, the stand volume estimate. However, it is possible to obtain regression equations that estimate DBH with good precision, from the LiDAR plant height derived data. The parabolic model is the one that provides the best estimates for timber volumetric yield of eucalyptus stands.

ANÁLISE DA FRAGMENTAÇÃO FLORESTAL DA ÁREA DE PROTEÇÃO AMBIENTAL COQUEIRAL, COQUEIRAL – MG

This study analysed the forest fragmentation of Coqueiral Environmental Protected Area (APA Coqueiral), located in Coqueiral, South region of Minas Gerais state, Brazil. The objective was to evaluate the forest fragmentation of Coqueiral APA, using landscape metrics, as well as, elaborating landscape simulation models to provide future scenarios of ecological restoration, and then, to compare these simulations to the current landscape. Land use analyses were carried out through Geographic Information Systems and Remote Sensing techniques, using a SPOT 5 satellite image (SPOTMAP). Forest fragmentation was analysed by FRAGSTATS software for calculating landscape metrics such as: area, perimeter, shape, and, patch connectivity. We performed 1 and 5 m buffers maps, and a virtual restoration of the Permanent Protected Areas (APP) for landscape simulations. Landscape fragmentation analyses showed that natural vegetation is distributed within 360 patches, being 137 of them smaller than 1 ha. Landscape simulation models revealed that natural vegetation has increased from 1943.13 ha to 2299.02 ha in the restored APP simulation (natural vegetation/restored APPs = VA). The average patch size increased from 7.66 m to 15.75 m in the same simulation in comparison to the current landscape. VA showed a smaller shape value (1.93), indicating that patch shape is simpler in this simulation. This is an important result from the conservation point of view, because as simpler a shape of a patch is, smaller the edge effect is. Isolation values were not statistically different in the simulations: 38.9 m (VN); 40.64 m (VB1); 42.89 m (VB5) e 39.75 m (VA), what indicates low isolation between patches, even in the current landscape. Connectivity index was high (99%) for all simulations, indicating that landscapes have high structural connectivity. These data are relevant inputs to decision makers and to a better planning and management of the APA Coqueiral, allowing us to indicate the priority areas for conservation in this natural reserve.

Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification. Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.

MODELING ECOLOGICAL NICHE OF TREE SPECIES IN BRAZILIAN TROPICAL AREA

Modeling of the ecological niche of vegetal species is useful for understanding the species-environment relationship, for prediction of responses to climate changes and for correct reforestation programs and establishment of plantation’s recommendation. The objective of this work was to establish a model for the distribution of four tree species (Casearia sylvestris, Copaifera langsdorffii, Croton floribundus and Tapirira guianensis), widely used in reforestation projects in the state of Minas Gerais, Brazil. In addition, we analyzed the relationship between environmental characteristics and the occurrence of species and tested the performance of Random Forest and Artificial Neural Networks as modeling methods. These methods were evaluated by their overall accuracy, sensitivity, specificity, Kappa, true skill statistic and the area under the receiver operating curve. The results showed the species Casearia sylvestris, Copaifera langsdorffii and Tapirira guianensis widely occurring in the state of Minas Gerais, including a broad range of environmental variables. Croton floribundus had restricted occurrence in the southern state, showing narrow environmental variation. The resulting algorithms demonstrated greater performance when modeling restricted geographic and environmental species, as well as species occurring with high prevalence in data. The algorithm Random Forest performed better for distribution modeling of all species, although the results varied for each metric and species. The maps generated had acceptable metrics and are supported by and ecological information obtained from other sources, constituting a useful tool to understand the ecology and biogeography of the target species.