Degradation trends based on MODIS-derived estimates of productivity and water use efficiency: A case study for the cultivated pastures in the Brazilian Cerrado

Fernandes, Fernando Hiago Souza; Sano, Edson Eyji; Ferreira, Laerte Guimarães; Baptista, Gustavo Macêdo de Mello; Victoria, D. de C.; Fassoni‐Andrade, Alice César

doi:10.1016/j.rsase.2018.04.014

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…Our results show that the average water productivity varies between 0.8 kg of carbon per m3 for the cropland and 0.5 kg of carbon per m3 for pastures. These results are similar to other studies that reported 0.7 kg per m3 for the cropland in China (Cao et al 2015) and 0.7-1 kg per m3 for pastures in Brazil (Fernandes et al 2018), assessed as water use efficiency. Although the annual water productivity is a good indicator to monitor the amount of water used to produce food, the spatial distribution of the water productivity is important to identify areas with water stress conditions.…”

Section: Crop Production Potential and Ecosystem Accountingsupporting

confidence: 91%

Using remote sensing and ecosystem accounting to assess changes in ecosystems, with an illustration for the Orinoco river basin

Vargas

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To avert decline in the stock of natural capital over time, it is essential to include ecosystems and ecosystem services, which sustain our economic development (Barbier 2016; Costanza et al. 2017; Dasgupta 2010; Ekins et al. 2003). The importance of ecosystems and ecosystems servicesnatural capital-in sustaining global economic development is also shared by international initiatives such as the Global Biodiversity Assessment (GBA), the Millennium Ecosystem Assessment (MA), The Economics of Ecosystems and Biodiversity (TEEB), and the Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES). The MA highlighted the importance of assessing changes; in the biophysical flow of ecosystem services, in the condition of ecosystems, and in well-being including changes in the total value of the benefits obtained from ecosystems. The MA recognized the importance of natural capital as a key determinant of human well-being, and as a component of the total wealth of each nation. The IPBES highlighted the importance of natural capital-nature and nature's benefits to people-as a key element that contributes to enhance our quality of life (Costanza et BOX 1.1. ECOSYSTEMS AND ECOSYSTEM SERVICES The MA defined ecosystem as "a dynamic complex of plant, animal and microorganisms communities and the non-living environment interacting as a functional unit" (MA 2003). The comprising types and abundance of the biotic and abiotic components of the ecosystem can be referred as ecosystems composition, and the distribution and arrangements of this components can be referred as ecosystems structure (Schmelzer 2015; Wallace 2007; WRI et al. 2005; WRI et al. 2015). The interaction between the biotic components, and between biotic and abiotic components of ecosystems, transfer energy and materials through the system. This transfers of energy and materials can be described as ecosystems processes, underpinned by ecosystems composition and structure (Chapin III et al. 2011; Lyons et al. 2005).

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Section: Crop Production Potential and Ecosystem Accountingsupporting

confidence: 91%

Using remote sensing and ecosystem accounting to assess changes in ecosystems, with an illustration for the Orinoco river basin

Vargas

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“…Com características monçônicas marcantes, 80% das chuvas caem de novembro a março, enquanto que de maio a setembro, o tempo é seco com poucas chuvas (Neves, 2018), sendo os meses de transição abril e outubro, respectivamente, de estação chuvosa para seca e de estação seca para chuvosa. Quanto à cobertura vegetal, a área de estudo encontra-se destituída da vegetação original em grande parte de seu território, sendo que o cerrado no Brasil central foi amplamente convertido em agricultura e pastagem, e essa conversão tem implicações importantes para as mudanças climáticas e nas mudanças nos fluxos de carbono entre a atmosfera e a superfície terrestre (Fernandes et al, 2018).…”

Section: áRea De Estudounclassified

Monitoramento da superfície do solo usando indicadores ambientais provenientes de dados de satélite em órbita geoestacionária

Silveira

Ferreira

Silva

2020

Rev. Bras. Geog. Fis.

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O satélite de órbita geoestacionária Meteosat-10 fornece indicadores ambientais com alta resolução temporal e baixa resolução espacial, permitindo o monitoramento da fenologia da paisagem. Porém, devido ao alto ângulo de visada do Meteosat-10, pouco tem sido utilizado para o Brasil. Indicadores como NDVI (Normalized Difference Vegetation Index), eficiente para o monitoramento da vegetação devido à simplicidade e estreita relação com a produtividade da vegetação, e o NDWI (Normalized Difference Water Index), que indica a umidade da superfície do solo combinando o solo e a vegetação expostos, ajudam a compreender a sazonalidade ambiental e ocupação das bacias hidrográficas. Esse artigo tem como objetivo avaliar a capacidade de indicadores biofísicos, provindos do satélite de órbita geoestacionária Meteosat-10, para o monitoramento ambiental da superfície do solo, tendo como área de estudo bacias hidrográficas, Ottobacias nível 5, do estado de Goiás, incluindo o Distrito Federal, no período 2013 a 2015. Para tanto, foram avaliados o NDVI, NDWI e a precipitação com resolução temporal de 12 dias e mensal. Fez-se o cálculo dos indicadores normalizados SPI (Standard Precipitation Index), SVI (Standard Vegetation Index) e SWI (Standard Water Index) para comparações entre os anos e obtenção da tendência de cada indicador. Como resultado pode-se identificar e compreender padrões sazonais das bacias hidrográficas em diferentes regiões e classes de uso, demonstrando o potencial de indicadores de vegetação provenientes de satélites geoestacionários, para o monitoramento da cobertura do solo em bacias hidrográficas, bem como identificar a maior sensibilidade do NDWI para o monitoramento de mudanças ocorridas na superfície. Monitoring of soil surface using environmental indicators satellite data monitor from geostationary orbit A B S T R A C TThe geostationary orbit satellite Meteosat-10, has spectral bands that allow create that provides environmental indicators with high temporal resolution and low spatial resolution, allowing the monitoring of landscape phenology. However, for Brazil little has been used due to the high viewing angle of Meteosat-10 (45 a 80º). Indicators like NDVI (Normalized Difference Vegetation Index), efficient for monitoring vegetation due to the simplicity and close relationship with the productivity of the vegetation, and the NDWI (Normalized Difference Water Index), which indicates the moisture of the soil surface combining the soil and the exposed vegetation, help to understand the environmental seasonality and occupation of watersheds. This article aims to evaluate the capacity of biophysical indicators, coming from the geostationary orbit satellite Meteosat-10, for the environmental monitoring of the soil surface, with hydrographic basins, Ottobasin level 5, from the state of Goiás, as the study area. Distrito Federal, from 2013 to 2015. For this purpose, NDVI, NDWI and precipitation with 12-day and monthly resolution were evaluated. Standardized indicators SPI (Standard Precipitation Index), SVI (Standard Vegetation Index) and SWI (Standard Water Index) were calculated for comparisons between years and obtaining the trend of each indicator. As a result, it is possible to identify and understand seasonal patterns of hydrographic basins in different regions and classes of use, demonstrating the potential of vegetation indicators from geostationary satellites, for monitoring soil cover in hydrographic basins, as well identify the highest sensitivity NDWI for monitoring surface changes.Keywords: NDVI, NDWI, Meteosat-10, environmental monitoring.

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“…A diverse range of variables and indices extracted from different sensors or satellites, such as AVHRR (Advanced Very-High-Resolution Radiometer), AVHRR-GIMMS (Global Inventory Monitoring and Modelling System), MODIS (Moderate Resolution Imaging Spectroradiometer), NOAA (National Oceanic and Atmospheric Administration) AVHRR or LANDSAT, amongst others, have been employed for assessing land degradation and desertification 11 , 21 – 24 . From these sources, large databases have been developed that relate to a plethora of vegetation and climate properties 25 , 26 , including the normalized difference vegetation index (NDVI) 26 – 28 , land cover changes 29 , leaf area index (LAI) 30 , land surface temperature (LST) 30 , multidisciplinary indices comprising LAI, albedo and evapotranspiration (ET) 30 , 31 , water use efficiency (WUE), net primary production (NPP) 32 , enhanced vegetation index (EVI) 33 , and rainfall and vegetation datasets 34 .…”

Section: Introductionmentioning

confidence: 99%

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Dameneh

Gholami

Telfer

et al. 2021

Sci Rep

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Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001–2015 via a combination of three indices for vegetation (NPP—net primary production, NDVI—normalized difference vegetation index, LAI—leaf area index) and two climate indices (LST—land surface temperature, P—precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann–Kendall non-parametric test, and the Theil–Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest–southeast belt spanning central Iran, which has experienced significant vegetation decline (2001–2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran’s major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification.

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Degradation trends based on MODIS-derived estimates of productivity and water use efficiency: A case study for the cultivated pastures in the Brazilian Cerrado

Cited by 9 publications

References 33 publications

Using remote sensing and ecosystem accounting to assess changes in ecosystems, with an illustration for the Orinoco river basin

Using remote sensing and ecosystem accounting to assess changes in ecosystems, with an illustration for the Orinoco river basin

Monitoramento da superfície do solo usando indicadores ambientais provenientes de dados de satélite em órbita geoestacionária

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

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