Above ground biomass functions with vegetation indices for multiple use systems of two evergreen oaks

Remote sensing is a promising approach for above ground biomass estimation, as forest parameters can be obtained indirectly. The analysis in space and time is quite straight forward due to the flexibility of the method to determine forest crown parameters with remote sensing. It can be used to evaluate and monitoring for example the development of a forest area in time and the impact of disturbances, such as silvicultural practices or deforestation. The vegetation indices, which condense data in a quantitative numeric manner, have been used to estimate several forest parameters, such as the volume, basal area and above ground biomass. The objective of this study was the development of allometric functions to estimate above ground biomass using vegetation indices as independent variables. The vegetation indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Soil-Adjusted Vegetation Index (SAVI). QuickBird satellite data, with 0.70 m of spatial resolution, was orthorectified, geometrically and atmospheric corrected, and the digital number were converted to top of atmosphere reflectance (ToA). Forest inventory data and published allometric functions at tree level were used to estimate above ground biomass per plot. Linear functions were fitted for the monospecies and multispecies stands of two evergreen oaks (Quercus suber and Quercus rotundifolia) in multiple use systems, montados. The allometric above ground biomass functions were fitted considering the mean and the median of each vegetation index per grid as independent variable. Species composition as a dummy variable was also considered as an independent variable. The linear functions with better performance are those with mean NDVI or mean SR as independent variable. Noteworthy is that the two better functions for monospecies cork oak stands have median NDVI or median SR as independent variable. When species composition dummy variables are included in the function (with stepwise regression) the best model has median NDVI as independent variable. The vegetation indices with the worse model performance were EVI and SAVI.

Monitoring pasture variability: optical OptRx® crop sensor versus Grassmaster II capacitance probe

Estimation of pasture productivity is an important step for the farmer in terms of planning animal stocking, organizing animal lots, and determining supplementary feeding needs throughout the year. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Two types of sensors were evaluated: an active optical sensor(OptRx®, which measures the NDVI, Normalized Difference Vegetation Index) and a capacitance probe (GrassMaster II which estimates plant mass). The results showed the potential of NDVI for monitoring the evolution of spatial and temporal patterns of vegetative growth of biodiverse pasture. Higher NDVI values were registered as pasture approached its greatest vegetative vigor, with a significant fall in the measured NDVI at the end of Spring, when the pasture began to dry due to the combination of higher temperatures and lower soil moisture content. This index was also effective for identifying different plant species (grasses/legumes) and variability in pasture yield. Furthermore, it was possible to develop calibration equations between the capacitance and the NDVI (R2 = 0.757; p < 0.01), between capacitance and GM (R2 = 0.799; p<0.01), between capacitance and DM (R2 = 0.630; p<0.01), between NDVI and GM (R2=0.745; p < 0.01), and between capacitance and DM (R2=0.524; p<0.01). Finally, a direct relationship was obtained between NDVI and pasture moisture content (PMC, in %) and between capacitance and PMC (respectively, R2 = 0.615; p<0.01 and R2=0.561; p <0.01) in Alentejo dryland farming systems.

Avaliação do sensor óptico de vegetação OptRx® na monitorização da variabilidade espacial e temporal de pastagens

estimativa da produtividade das pastagens constitui uma etapa fundamental para o gestor agrícola em termos de planeamento do encabeçamento animal, organização dos lotes de animais e avaliação das necessidades de suplementação alimentar ao longo do ano. O objectivo principal deste trabalho consistiu na avaliação de um sensor óptico activo (“OptRx”, que mede o índice NDVI, “Normalised Difference Vegetation Index”) para monitorizar de forma expedita aspectos relacionados com a variabilidade da pastagem e apoiar a tomada de decisão do gestor agrícola. Os resultados obtidos demonstraram o potencial que apresenta o índice NDVI para monitorizar a evolução do padrão espacial e temporal do estado vegetativo de uma pastagem biodiversa. Índices mais elevados foram registados à medida que a pastagem se aproximava do seu maior vigor vegetativo, notando-se uma quebra significativa destes índices no final da Primavera, quando a pastagem começou a secar em virtude da conjugação de temperaturas mais elevadas com a redução dos teores de humidade no solo. Este índice foi também efectivo na identificação de diferentes famílias botânicas (gramíneas/leguminosas) e diferentes produtividades na pastagem. Por outro lado, foi possível desenvolver equações de calibração do NDVI com a produção de matéria verde e de matéria seca (em kg/ha), tendo sido evidenciada uma relação inversa deste índice com o teor de matéria seca (em %) de pastagens de sequeiro do Alentejo.

Evaluation of DUALEM, OptRx and Grassmaster II sensors for monitoring the spatial variability and the temporal stability of a Mediterranean pasture

Site-specific management (SSM) is a form of precision agriculture whereby decisions on resource application and agronomic practices are improved to better match soil and crop requirements as they vary in the field. SSM enables the identification of regions (homogeneous management zones) within the area delimited by field boundaries. These subfield regions constitute areas that have similar permanent characteristics. Traditional soil and pasture sampling and the necessary laboratory analysis are time-consuming, labour-intensive and cost prohibitive, not viable from a SSM perspective because it needs a large number of soil and pasture samples in order to achieve a good representation of soil properties, nutrient levels and pasture quality and productivity. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of soil nutrients and pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Three types of sensors were evaluated in a 7ha pasture experimental field: an electromagnetic induction sensor (“DUALEM 1S”, which measures the soil apparent electrical conductivity, ECa), an active optical sensor ("OptRx®", which measures the NDVI, “Normalized Difference Vegetation Index”) and a capacitance probe ("GrassMaster II" which estimates plant mass). The results indicate the possibility of using a soil electrical conductivity probe as, probably, the best tool for monitoring not only some of the characteristics of the soil, but also those of the pasture, which could represent an important help in simplifying the process of sampling and support SSM decision making, in precision agriculture projects. On the other hand, the significant and very strong correlations obtained between capacitance and NDVI and between any of these parameters and the pasture productivity shows the potential of these tools for monitoring the evolution of spatial and temporal patterns of the vegetative growth of biodiverse pasture, for identifying different plant species and variability in pasture yield in Alentejo dry-land farming systems. These results are relevant for the selection of an adequate sensing system for a particular application and open new perspectives for other works that would allow the testing, calibration and validation of the sensors in a wider range of pasture production conditions, namely the extraordinary diversity of botanical species that are characteristic of the Mediterranean region at the different periods of the year.

Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

Modifications in vegetation cover can have an impact on the climate through changes in biogeochemical and biogeophysical processes. In this paper, the tree canopy cover percentage of a savannah-like ecosystem (montado/dehesa) was estimated at Landsat pixel level for 2011, and the role of different canopy cover percentages on land surface albedo (LSA) and land surface temperature (LST) were analysed. A modelling procedure using a SGB machine-learning algorithm and Landsat 5-TM spectral bands and derived vegetation indices as explanatory variables, showed that the estimation of montado canopy cover was obtained with good agreement (R2 = 78.4%). Overall, montado canopy cover estimations showed that low canopy cover class (MT_1) is the most representative with 50.63% of total montado area. MODIS LSA and LST products were used to investigate the magnitude of differences in mean annual LSA and LST values between contrasting montado canopy cover percentages. As a result, it was found a significant statistical relationship between montado canopy cover percentage and mean annual surface albedo (R2 = 0.866, p < 0.001) and surface temperature (R2 = 0.942, p < 0.001). The comparisons between the four contrasting montado canopy cover classes showed marked differences in LSA (χ2 = 192.17, df = 3, p < 0.001) and LST (χ2 = 318.18, df = 3, p < 0.001). The highest montado canopy cover percentage (MT_4) generally had lower albedo than lowest canopy cover class, presenting a difference of −11.2% in mean annual albedo values. It was also showed that MT_4 and MT_3 are the cooler canopy cover classes, and MT_2 and MT_1 the warmer, where MT_1 class had a difference of 3.42 °C compared with MT_4 class. Overall, this research highlighted the role that potential changes in montado canopy cover may play in local land surface albedo and temperature variations, as an increase in these two biogeophysical parameters may potentially bring about, in the long term, local/regional climatic changes moving towards greater aridity.

Effects of clouds on the surface shortwave radiation at a rural inland mid-latitude site

Seven years (2003–2010) of measured shortwave (SW) irradiances were used to obtain estimates of the 10 min averaged effective cloud optical thickness (ECOT) and of the shortwave cloud radiative effect (CRESW) at the surface in a mid-latitude site (Évora — south of Portugal), and its seasonal variability is presented. The ECOT, obtained using transmittance measurements at 415 nm, was compared with the correspondent MODIS cloud optical thickness (MODIS COT) for non-precipitating water clouds and cloud fractions higher than 0.25. This comparison showed that the ECOT represents well the cloud optical thickness over the study area. The CRESW, determined for two SW broadband ranges (300–1100 nm; 285–2800 nm), was normalized (NCRESW) and related with the obtained ECOT. A logarithmic relation between NCRESW and ECOT was found for both SW ranges, presenting lower dispersion for overcast-sky situations than for partially cloudy-sky situations. The NCRESW efficiency (NCRESW per unit of ECOT) was also related with the ECOT for overcast-sky conditions. The relation found is parameterized by a power law function showing that NCRESW efficiency decreases as the ECOT increases, approaching one for ECOT values higher than about 50.

Sugarcane Water Productivity Assessments in the São Paulo state, Brazil.

São Paulo state, Brazil, has been highlighted by the sugarcane crop expansion. The actual scenario of climate and land use changes, bring attention for the large-scale water productivity (WP) analyses. MODIS images were used together with gridded weather data for these analyses. A generalized sugarcane growing cycle inside a crop land mask, from September 2011 to October 2012, was considered in the main growing regions of the state. Actual evapotranspiration (ET) is quantified by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm, the biomass production (BIO) by the RUE (Radiation Use Efficiency) Monteith?s model and WP is considered as the ratio of BIO to ET. During the four generalized sugarcane crop phases, the mean ET values ranged from 0.6 to 4.0 mm day-1; BIO rates were between 20 and 200 kg ha-1 day-1, resulting in WP ranging from 2.8 to 6.0 kg m-3. Soil moisture indicators are applied, indicating benefits from supplementary irrigation during the grand growth phase, wherever there is water availability for this practice. The quantification of the large-scale water variables may subsidize the rational water resources management under the sugarcane expansion and water scarcity scenarios.

Dinâmica espaço-temporal da temperatura de superfície, da bacia do rio Corumbataí, SP utilizando imagens do sensor TM/Landsat.

Neste estudo foram empregados sistemas de informações geográficas e técnicas de sensoriamento remoto para investigar o efeito do uso e cobertura do solo sobre a temperatura da superfície do solo (TSS) nos anos de 1985, 1990, 1995, 1999, 2002 e 2011, na bacia do Rio Corumbataí (BRC), SP. O padrão espacial da TSS foi derivado de imagens do satélite Landsat, por meio da banda termal do sensor TM. Estudou-se a relação entre os índices NDVI, NDBI, MI e da altitude com a TSS. A TSS da BRC foi crescente até o ano de 1999 e reduziu até o ano de 2011. O padrão espacial da TSS foi influenciado pelo uso e cobertura do solo. Houve correlação negativa da TSS com o NDVI e MI e positiva com o NDBI. Áreas de pastagem e áreas de cana-de-açúcar colhidas podem atingir TSS superiores às áreas urbanas, dependendo das condições de umidade do ambiente. Conclui-se que os dados do TM/Landsat associados aos dados coletados em campo, apresentam potencial para identificação de padrões térmico-hídricos em estudos de bacias hidrográficas.

Avaliação da dinâmica do carbono na fitomassa de cana-de-açúcar e pastagem.

A avaliação na alteração dos estoques de carbono na fitomassa agrícola ocorreu em uma área de 51.650 km2, compreendendo 125 municípios das regiões, central, norte e nordeste do Estado de São Paulo. Essas regiões possuem as cadeias de produção especializadas da cana-de-açúcar e das pastagens que estão presentes em praticamente quase todos os municípios da região e competem por área. Por meio da investigação do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) e da interpretação de imagens do sensor Thematic Mapper (TM), avaliou-se a mudança de uso e cobertura da terra nos anos de 1988 e 2015. A expansão a área de cana-de-açúcar acelerou-se significativamente em toda a região e nos últimos 27 anos a área cultivada passou de 1.085.900 ha (21% da área de estudo) para 1.966.445 ha (38% da área de estudo). As áreas de pastagens reduziram-se de 1.397.724 ha (26% da área de estudo) para 684.323 ha (13% da área de estudo). A análise dos dados revelou que a cana-de-açúcar é capaz de acumular 107,2 t.ha.-1.ano-1 de carbono na fitomassa, enquanto as pastagens cultivadas somente 11,7 t.ha.-1.ano-1 de carbono. Em 1988 toda a área de cana-de-açúcar retinha na fitomassa 116 milhões de toneladas de CO2 e em 27 anos esse acúmulo passou para 211 milhões de toneladas de CO2 .ano-1. Constata-se com isso que o carbono pode, ao menos em parte, ser recomposto pelos agroecossistemas durante o subsequente uso do solo. Dos 125 municípios avaliados, 118 deles apresentaram elevação do carbono acumulado na fitomassa devido a incorporação de áreas de pastagens por cana-de-açúcar, num total de 592 mil ha. Somente nas áreas de pastagens que foram substituídas por cana-de-açúcar nesses 27 anos, promoveu-se a remoção de 54 milhões de toneladas de CO2 da atmosfera.

Mapeamento do risco de incêndio na bacia do Alto Paraguai utilizando dados AVHRR-NOAA.

Na Bacia do Alto Paraguai o fogo é muito utilizado para manejo de pastagens, principalmente durante a estação seca. A determinação do risco de incêndio em áreas de vegetação é uma informação importante par auxiliar as práticas de manejo adequado ao uso do fogo. O objetivo deste trabalho foi realizar o mapeamento do risco de incêndio na Bacia do Alto Paraguai utilizando dados AVHRR-NOAA. A análise dos perfis temporais da Banda 1 e do NDVI nos meses de agosto, setembro e outubro de 2004 a 2008, em conjunto com os focos de calor detectados nas imagens NOAA, permitiu caracterizar o decréscimo da umidade da vegetação que proporciona a condição para ocorrência de incêndios. Os resultados mostraram que valores do fator de refletância da Banda 1 maiores que 5% e valores do NDVI menores que 0,40, podem estimar alto grau de risco de incêndio. O mapeamento do risco de incêndio utilizando dados AVHRR-NOAA demonstrou ter forte correlação com os focos de calor detectados nas imagens NOAA. O método mostrou ser viável e pode ser refinado para integrar os sistemas de prevenção de incêndio para alerta de queimadas e para tomadas de decisão para controle do fogo.

Supporting land use change modelling with automated monitoring of land use dynamics in the La Plata River Basin.

2008

Assessment of sugarcane harvesting residue effects on soil spectral behavior

When the harvesting of sugarcane involves a mechanized process, plant residues remain on the soil surface, which makes proximal and remote sensing difficult to monitor. This study aimed to evaluate, under laboratory conditions, differences in the soil spectral behavior of surface layers Quartzipsamment and Hapludox soil classes due to increasing levels of sugarcane?s dry (DL) and green (GL) leaf cover on the soil. Soil cover was quantified by supervised classification of the digital images (photography) taken of the treatments. The spectral reflectance of the samples was obtained using the FieldSpec Pro (350 to 2500 nm). TM-Landsat bands were simulated and the Normalized Difference Vegetation Index (NDVI) and soil line were also determined. Soil cover ranged from 0 to 89 % for DL and 0 to 80 % for GL. Dry leaf covering affected the features of the following soil constituents: iron oxides (480, 530 and 900 nm) and kaolinite (2200 nm). Water absorption (1400 and 1900 nm) and chlorophyll (670 nm) were determinant in differentiating between bare soil and GL covering. Bands 3 and 4 and NDVI showed pronounced variations as regards differences in soil cover percentage for both DL and GL. The soil line allowed for discrimination of the bare soil from the covered soil (DL and GL). High resolution sensors from about 50 % of the DL or GL covering are expected to reveal differences in soil spectral behavior. Above this coverage percentage, soil assessment by remote sensing is impaired.

Predição da fertilidade do solo do polo agrícola do Rio de Janeiro por meio de modelagem solo x paisagem.

O objetivo deste trabalho foi predizer a fertilidade do solo no polo agrícola do Estado do Rio de Janeiro, por meio da modelagem solo x paisagem. A área de estudo compreendeu as regiões mais produtivas do Estado do Rio de Janeiro: Norte, Noroeste e Serrana. Características químicas do solo ? pH em H2O e capacidade de troca catiônica (CTC) ? e ambientais ? elevação, plano de curvatura, perfil de curvatura, índice de umidade, aspecto e declividade do terreno, além de tipos de solos, índice de vegetação normalizada (NDVI), imagens Landsat 7 e litologia ? foram utilizadas como variáveis preditoras. A análise exploratória dos dados identificou valores extremos, os quais foram expurgados, na preparação para a análise por regressão linear múltipla (RLM). Aos resultados da RLM, foram adicionados os resultados de krigagem dos resíduos da regressão, com uma técnica de mapeamento digital de solos (MDS) denominada regressão-krigagem. Na região Serrana, as variáveis ambientais explicaram as variáveis químicas. A variável NDVI foi importante nas três regiões, o que evidencia a importância da cobertura vegetal para a predição da fertilidade do solo. Em geral, os solos analisados apresentaram baixo pH. Os valores de CTC, nas regiões estudadas, estão dentro do intervalo considerado bom para a fertilidade do solo.

Biophysical indicators based on satellite images in an irrigated area at the São Francisco River Basin, Brazil.

The Jaíba Irrigated Perimeter is a large irrigated agriculturearea, located in the region Forest Jaíba between the SãoFrancisco and Verde Grande rivers, in the Brazilian semi-arid region. In 2014, irrigators thisthe region face losses in theinterruption of new plantings in irrigated areas due to water scarcity. The objective ofthis study is combine the modelto estimate the Monteith BIO with the SAFER algorithm in the case of obtaining ET, to analyze the dynamics of naturalvegetation and irrigated crops in water scarcity period. For application of the model are necessary data frommeteorological stations and satellite images. Were used 23satellite images of MODIS withspatial resolution of 250mand temporal 16 days, of 2014 year. For analyze the results,we used central pivots irrigation mask of Minas Geraisstate, Brazil. In areas with irrigated agriculture with central pivot, the mean values of BIO over the year 2014 were88.96 kg.ha-1.d-1. The highest values occurred between April 23 and May 8, with BIO 139 kg.ha-1.d-1. For areas withnatural vegetation, the average BIO was 88.34 kg.ha-1.d-1with lower values in September. Estimates of ET varied withthe lowest values of ET observedin natural vegetation 1,91±1,22 mm.d-1and the highest values in irrigated area isobserved 3,51±0,97 mm.d-1. Results of this study can assist in monitoring of river basins, contributing to themanagement irrigated agriculture, with the trend of scarcity of water resources and increasing conflicts for the wateruse.

Energy balances in sugar cane, coffee and natural vegetation in the northeastern side of the São Paulo state, Brazil

Under land and climate change scenarios, agriculture has experienced water competitions among other sectors in the São Paulo state, Brazil. On the one hand, in several occasions, in the northeastern side of this state, nowadays sugar-cane is expanding, while coffee plantations are losing space. On the other hand, both crops have replaced the natural vegetation composed by Savannah and Atlantic Coastal Forest species. Under this dynamic situation, geosciences are valuable tools for evaluating the large-scale energy and mass exchanges between these diffe rent agro-ecosystems and the lower atmosphere. For quantification of the energy balance components in these mixed agro-ecosystems, the bands 1 and 2 from the MODIS product MOD13Q1 we re used throughout SA FER (Surface Algorithm for Evapotranspiration Retrieving) algorithm, which was applied together with a net of 12 automatic weather stations, during the year 2015 in the main sugar cane and coffee growing regions, located at the no rtheastern side of the state. The fraction of the global solar radiation (R G ) transformed into net radiation (Rn) was 52% for sugar cane and 53% for both, coffee and natural vegetation. The respective annual fractions of Rn used as λ E were 0.68, 0.87 and 0.77, while for the sensible heat (H) fluxes they were 0.27, 0.07 and 0.16. From April to July, heat advection raised λ E values above Rn promoting negative H, however these effects were much and less strong in coffee and sugar cane crop s, respectively. The smallest daily Rn fraction for all agro-ecosystems was for the soil heat flux (G), with averages of 5%, 6% and 7% in sugar cane, coffee and natural vegetation. From the energy balance analyses, we could conclude that, sugar-cane crop presented lower annual water consumption than that for coffee crop , what can be seen as an advantage in situations of water scarcity. However, the replacement of natural vegetation by su gar cane can contribute for warming th e environment, while when this occur with coffee crop there was noticed co oling conditions. The large scale modeling satisfactory results confirm the suitability of using MODIS products togeth er with weather stations to study the energy balance components in mixed agro-ecosystems under land-use and climate change conditions.