Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas

The Brazilian Savannas have been under increasing anthropic pressure for many years, and land-use/land-cover changes (LULCC) have been largely neglected. Remote sensing provides useful tools to detect changes, but previous studies have not attempted to separate the effects of phenology from deforestation, clearing or fires to improve the accuracy of change detection without a dense time series. The scientific questions addressed in this study were: how well can we differentiate seasonal changes from deforestation processes combining the spatial and spectral information of bi-temporal (normalized difference vegetation index) NDVI images? Which feature best contribute to increase the separability on classification assessment? We applied an object-based remote sensing method that is able to separate seasonal changes due to phenology effects from LULCC by combining spectral and the spatial context using traditional spectral features and semivariogram indices, exploring the full capability of NDVI image difference to train random forest (RF) algorithm. We found that the spatial variability of NDVI values is not affect by vegetation seasonality and, therefore, the combination of spectral features and semivariogram indices provided high global accuracy (97.73%) to separate seasonal changes and deforestation or fires. From the total of 13 features, 6 provided the best combination to increase the separability on classification assessment (4 spatial and 2 spectral features). How to accurately extract LULCC while disregarding the ones caused by phenological differences in Brazilian seasonal biomes undergoing rapid land-cover changes can be achieved by adding semivariogram indices in combination with spectral features as input data to train RF algorithm

LAND USE/ COVER (LULC) MAPPING IN BRAZILIAN CERRADO USING NEURAL NETWORK WITH SENTINEL-2 DATA

The Sentinel-2a and 2B satellites form a multispectral imaging mission for Earth observation. They have promising characteristics for the study of soils and vegetation cover, and their data can be applied for land use/cover (LULC) mapping. To this end, neural networks have shown good results in pattern recognition tasks in orbital images. In this sense, the study aimed to evaluate the use of Sentinel 2 (ESA) image for LULC mapping in the Cerrado Biome, through the application of artificial neural network methodology. Among the classes of use and occupation examined, 8 classes were selected, 4 of which were natural (water bodies, savanna, forest and field formation) and 4 anthropic (Pasture, Urban areas, Silviculture and Seasonal Crop). The classification system by artificial neural network (ANN) was considered successful, with thematic accuracy (Kappa coefficient) of 0.77. Although there are still some thematic confusions during the classification process, the classification results were considered superior when compared to the MaxVer classifier. The Sentinel-2 image, together with the use of a neural network, was shown a good input for carrying out this type of mapping.Key words: Orbital Remote Sensing System, Supervised Classification Techniques, LULC classes

Spatial distribution of wood volume in brazilian savannas

Here we model and describe the wood volume of Cerrado Sensu Stricto, a highly heterogeneous vegetation type in the Savanna biome, in the state of Minas Gerais, Brazil, integrating forest inventory data with spatial-environmental variables, multivariate regression, and regression kriging. Our study contributes to a better understanding of the factors that affect the spatial distribution of the wood volume of this vegetation type as well as allowing better representation of the spatial heterogeneity of this biome. Wood volume estimates were obtained through regression models using different environmental variables as independent variables. Using the best fitted model, spatial analysis of the residuals was carried out by selecting a semivariogram model for generating an ordinary kriging map, which in turn was used with the fitted regression model in the regression kriging technique. Seasonality of both temperature and precipitation, along with the density of deforestation, explained the variations of wood volume throughout Minas Gerais. The spatial distribution of predicted wood volume of Cerrado Sensu Stricto in Minas Gerais revealed the high variability of this variable (15.32 to 98.38 m3 ha-1) and the decreasing gradient in the southeast-northwest direction914COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçã

Evaluating Soil Carbon Efflux Responses to Soil Moisture and Temperature Variations in Brazilian Biomes

Changes in soil moisture and temperature can directly influence soil carbon emissions which can add carbon to the atmosphere and make the greenhouse effect more intense In this sense research is needed that contributes to this knowledge and that simulates future scenarios allowing actions to be taken in advance Thus an experiment was set up in which carbon dioxide efflux was collected over a period of one year in three Brazilian biomes Cerrado Pantanal and Cerrado-Amazonian Ecotone and to verify the influence of soil moisture leaf area index and litter multiple regression models were carried out Correlation analyses were performed and subsequently sensitivity analyses were conducted for possible efflux increases owing to 2 C and 10 decreases or increases in soil temperature and moisture respectively simulating possible climate change scenarios The results showed that of the three study areas the Cerrado forest was most resistant to changes in these variables and the correlation between the carbon efflux and the variables soil temperature and moisture were positive and significant for Cerrado and Pantana

Methods for pasture management identification based on satellite images

Orientador: Jansle Vieira RochaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgrícolaResumo: A população mundial, projetada para 9 bilhões de pessoas em 2050, coloca em foco a necessidade do aumento da produção de alimentos. Estudos apontam em até 65% o aumento de novas áreas agriculturáveis. O crescimento da produção agrícola, de maneira sustentável, nas áreas já destinadas a esta finalidade, pode ser a melhor solução para o conflito existente entre expansão da produção agrícola e conservação do meio ambiente. Já foi mostrado que é possível o aumento da produtividade de maneira eficiente e reduzindo a emissão gases do efeito estufa, isto levaria a diminuição da pressão para a abertura de novas áreas destinadas a agricultura. Porém isso demanda a disponibilidade de tecnologia e conhecimento para o produtor e adoção de políticas públicas. O aumento da produtividade nas áreas de pastagem pode ser um fator importante para liberação de áreas para a expansão agrícola. Ao se pensar em sistemas de produção que tenham uma maior sinergia na utilização dos recursos, o sistema de integração lavoura-pecuária se apresenta como uma alternativa que aumenta a produtividade e eficiência da produção. Apesar das inúmeras pesquisas que investigam este processo de mudança das pastagens, ainda se faz necessária uma análise mais detalhada que contemple aspectos como a distribuição geográfica, mapeamento e monitoramento das pastagens. A análise multitemporal de imagens de satélite pode constituir uma estratégia eficaz no processo de mapeamento e monitoramento desta cultura. Neste sentido, a tese teve como objetivo a caracterização e mapeamento de diferentes tipos sistemas de produção e manejos de pastagem em regiões do Brasil, utilizando séries temporais de sensoriamento remoto. Métodos de caracterização e mapeamento de diferentes tipos de manejo de pastagem e sistemas de integração lavoura-pecuária (ILP) foram desenvolvidos com a utilização do método TWDTW (Time-Weighted Dynamic Time Warping) de análise e classificação de séries temporais, em conjunto com dados temporais EVI/MODIS. Dentro das definições adotadas de manejo intensificado de pastagem (tempo após a reforma e adubação), a distinção, por meio de séries temporais de EVI, apresentou acurácia entre 73-98%, para fazendas localizadas em São Paulo, Mato Grosso e Pará. O método desenvolvido para identificação de áreas de ILP mostrou acurácia entre 75-86% na identificação de sistemas com características anuais e multianuais. Assim, foram desenvolvidas possibilidades para a sistematização do monitoramento e mapeamento de sistemas ILP em escala regional e de intensidade de manejo em escala de propriedade, através de séries temporais de imagens de sensoriamento remotoAbstract: The world's population is projected to reach 9 billion people by 2050, what brings up the need to increase food production. Studies show that the increase of new areas for agriculture will be around 65%. The increase of sustainable agricultural production in areas already designed for this purpose may be the solution for the conflict between the expansion of agricultural production and environmental conservation. It has been shown that it is possible to increase the efficiency in productivity while reducing GHG emissions, this would lead to decreased pressure to open up new areas for agriculture, but this takes the availability of technology and knowledge to the producer and adoption of public policies. Higher productivities of grazing land can be an important factor in freeing areas for agricultural expansion. Thinking about production systems that have greater synergy in use of resources, the integrated crop-livestock-forest (ICL) system is presented as an alternative to increase productivity and production efficiency. Despite numerous studies investigating this process of pasture changes, even more analysis is needed to addresses issues such as geographical distribution, mapping and monitoring of pastures. The multi-temporal analysis of satellite images may provide an effective strategy for mapping and monitoring processes, since the use of information acquired at different times allow differentiation of targets due to the variation of the spectral response. Thus, thesis aimed to characterize and map different pasture production systems and management in regions of Brazil using time series remote sensing. TWDTW (Time-Weighted Dynamic Time Warping) method of temporal series analysis and classification and EVI/MODIS temporal data were used to develop the method. Among the adopted definitions of intensified pasture management (period after pasture renew and fertilizer), the distinction, through time series of EVI, showed an accuracy of 73-98% for farms located in São Paulo, Mato Grosso and Pará. The method developed to identify areas of iCL showed accuracy between 75-86%. Thus, the use of remote sensing time series analyses to automatic monitoring and mapping iCL systems in regional scale and pasture management was createDoutoradoGestão de Sistemas na Agricultura e Desenvolvimento RuralDoutor em Engenharia Agrícola2014/26928-2FAPESPCAPE

Monitoring Islamic Archaeological Landscapes in Ethiopia Using Open Source Satellite Imagery

This is the final version. Available on open access from Maney Publishing via the DOI in this recordThe African landscape is set to change dramatically in the coming years, and will have a detrimental impact on the inherent archaeological and cultural heritage elements if not monitored adequately. This paper explores how satellite imagery, in particular open source imagery (Google Earth, multispectral satellite imagery from Landsat and Sentinel-2), can be utilized to monitor and protect sites that are already known with particular reference to Islamic archaeological sites in Ethiopia. The four sites used are in different geographic and geomorphological areas: three on the Somali Plateau (Harlaa, Harar, and Sheikh Hussein), and one on the edge of the Afar Depression (Nora), and have varied histories. The results indicate that open source satellite imagery offers a mechanism for evaluating site status and conservation over time at a large scale, and can be used on data from other areas of Africa by heritage professionals in the African continent at no cost.European Union Horizon 202

Mapping native and non-native vegetation in the Brazilian Cerrado using freely available satellite products

This is the final version. Available on open access from Nature Research via the DOI in this recordNative 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.ShellFAPESPNatural Environment Research Council (NERC

Exploring issues of balanced versus imbalanced samples in mapping grass community in the telperion reserve using high resolution images and selected machine learning algorithms

ABSTRACT Accurate vegetation mapping is essential for a number of reasons, one of which is for conservation purposes. The main objective of this research was to map different grass communities in the game reserve using RapidEye and Sentinel-2 MSI images and machine learning classifiers [support vector machine (SVM) and Random forest (RF)] to test the impacts of balanced and imbalance training data on the performance and the accuracy of Support Vector Machine and Random forest in mapping the grass communities and test the sensitivities of pixel resolution to balanced and imbalance training data in image classification. The imbalanced and balanced data sets were obtained through field data collection. The results show RF and SVM are producing a high overall accuracy for Sentinel-2 imagery for both the balanced and imbalanced data set. The RF classifier has yielded an overall accuracy of 79.45% and kappa of 74.38% and an overall accuracy of 76.19% and kappa of 73.21% using imbalanced and balanced training data respectively. The SVM classifier yielded an overall accuracy of 82.54% and kappa of 80.36% and an overall accuracy of 82.21% and a kappa of 78.33% using imbalanced and balanced training data respectively. For the RapidEye imagery, RF and SVM algorithm produced overall accuracy affected by a balanced data set leading to reduced accuracy. The RF algorithm had an overall accuracy that dropped by 6% (from 63.24% to 57.94%) while the SVM dropped by 7% (from 57.31% to 50.79%). The results thereby show that the imbalanced data set is a better option when looking at the image classification of vegetation species than the balanced data set. The study recommends the implementation of ways of handling misclassification among the different grass species to improve classification for future research. Further research can be carried out on other types of high resolution multispectral imagery using different advanced algorithms on different training size samples.EM201

Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6 and remaining surface components with MAEs between 3.0 and 11.2. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2 per decade for the entire study area and + 4.2 per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome

Análise da aplicação do método RAPELD à vegetação lenhosa do cerrado

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Florestal, 2018.O Cerrado é considerado um complexo vegetacional e, por isso, uma região peculiar e diversificada fisionomicamente. O presente estudo teve como objetivo avaliar a aplicação do método RAPELD, que possui premissa básica de homogeneização da amostragem seguindo as curvas de nível, em ambientes heterogêneos como o Cerrado. Para isso, foram realizados dois tipos de abordagens, os dados de campo (inventário da vegetação lenhosa) e análise das imagens de satélite. Além da variação entre as parcelas, foi registrada grande heterogeneidade dentro de cada unidade amostral em relação ao número de indivíduos, riqueza e área basal, o que refletiu em elevados coeficientes de variação para a maioria das parcelas. A classificação da vegetação identificou a ocorrência das três formações de Cerrado: formações campestres (Campo Limpo e Campo Sujo), Savânica (Cerrado sentido restrito) e Florestal (Mata de Galeria), sendo amostradas no Parque Nacional da Chapada dos Veadeiros, Goiás, Brasil. As parcelas analisadas situaram-se predominantemente em Cerrado sentido restrito, Campo Limpo e Campo Sujo, com algumas delas possuindo mais de um tipo fitofisionômico. A disposição das parcelas em nível altimétrico não homogeneizou a variação entre os segmentos de uma mesma parcela para os parâmetros florísticos e estruturais da vegetação lenhosa no Cerrado. Assim, quando o objetivo do estudo é a descrição da vegetação arbustivo-arbórea, a utilização do RAPELD não deve ser feita indiscriminadamente. Neste caso, a metodologia RAPELD necessita de ajustes às características de heterogeneidade fitofisionômica do Cerrado a fim de amostrar adequadamente suas particularidades.The Cerrado is considered a complex vegetation since it is a peculiar region with diversified phytophysiognomies. This study aimed to evaluate the application of the RAPELD methodology, which the basic premise is that the vegetation can be homogenize when plots follow the isocline, in heterogeneous environments as Cerrado. We carried out two approaches: the field data (forest inventory of the shrub and tree vegetation) and analysis of satellite`s image. Besides the variation between the plots, it was recorded a highly heterogeneity inside the plots in relation to the number of individual, richness and basal area of the vegetation, which reflected high coefficients of variation in most of the plots. The classification of the vegetation identified the occurrence of the three Cerrado formations: grasslands (Campo Limpo and Campo Sujo), savanna(Cerrado stricto senso) and forest (Mata de Galeria) located in the Chapada dos Veadeiros National Park. The samples were majority located in the Cerrado stricto senso, Campo Limpo e Campo Sujo, and some samples occurred in more than one type of phytophysiognomie. The disposition of the plots, following the isocline, did not homogenize the variation inside the plots segments in the floristic and structure parameters of the woody vegetation of Cerrado. Thus, if the purpose of the survey is to describe the vegetation, the use of the RAPELD method should not be done indiscriminately. In this case, the RAPELD method needs some adjustments to adequately sample the heterogenous phytophysiognomies particularities of the Cerrado