Vegetation Cover Index Research Articles

Variação espaço-temporal (1938-2013) do uso e ocupação da terra na região do Saco da Fazenda, Itajaí-SC

O objetivo deste trabalho é a análise da variação temporal da cobertura de uso e ocupação do solo de uma região estuarina no litoral centro-norte do Estado de Santa Catarina, reconstruindo as alterações na dinâmica da paisagem em um período de 75 anos (1938 - 2013), além de calcular as variações do Índice de Uso e Ocupação do Solo (IUO) e do Índice de Cobertura Vegetal (ICV). Para a classificação digital de uso e ocupação do solo foi realizada uma classificação visual, em ambiente SIG. Os resultados indicaram que nas décadas de 30, 50 e 70 ocorreram as maiores transformações na área de estudo, sendo que a variação do IUO apontou um crescimento das áreas antropizadas e o ICV demonstrou uma redução das áreas com vegetação. Estudos desta natureza constituem-se ferramentas importantes para a tomada de decisão dos gestores municipais, na busca de um crescimento mais harmônico das cidades, compatibilizando as necessidades dos moradores por moradias e serviços públicos com os remanescentes naturais, que tem igual importância para a qualidade de vida da população.

Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera

The permanent monitoring of vegetation cover is important to guarantee a sustainable management of agricultural activities, with a relevant role in the reduction of water erosion. This monitoring can be carried out through different indicators such as vegetation cover indices. In this study, the vegetation cover index was obtained using uncalibrated RGB images generated from a digital photographic camera on an unmanned aerial vehicle (UAV). In addition, a comparative study with 11 vegetation indices was carried out. The vegetation indices CIVE and EXG presented a better performance and the index WI presented the worst performance in the vegetation classification during the cycles of jack bean and millet, according to the overall accuracy and Kappa coefficient. Vegetation indices were effective tools in obtaining soil cover index when compared to the standard Stocking method, except for the index WI. Architecture and cycle of millet and jack bean influenced the behavior of the studied vegetation indices. Vegetation indices generated from RGB images obtained by UAV were more practical and efficient, allowing a more frequent monitoring and in a wider area during the crop cycle.

Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China.

With the exploitation of coalfields, the eco-environment around the coalfields can become badly damaged. To address this issue, “mine greening” has been proposed by the Ministry of Land and Resources of China. The sustainable development of mine environments has now become one of the most prominent issues in China. In this study, we aimed to make use of Landsat 7 ETM+ and Landsat 8 OLI images obtained between 2005 and 2016 to analyze the eco-environment in a coalfield. Land cover was implemented as the basic evaluation factor to establish the evaluation model for the eco-environment. Analysis and investigation of the eco-environment in the Yuxian coalfield was conducted using a novel evaluation model, based on the biological abundance index, vegetation coverage index, water density index, and natural geographical factors. The weight of each indicator was determined by an analytic hierarchy process. Meanwhile, we also used the classic ecological footprint to calculate the ecological carrying capacity in order to verify the effectiveness of the evaluation model. Results showed that the eco-environment index illustrated a slowly increasing tendency over the study period, and the ecological quality could be considered as “good”. The results of the evaluation model showed a strong correlation with the ecological carrying capacity with a correlation coefficient of 0.9734. In conclusion, the evaluation method is a supplement to the time-series quantitative evaluation of the eco-environment, and also helps us to explore the eco-environment in the mining area.

Effects of drought on plant parameters of different rangeland types in Khansar region, Iran

Vegetation changes and drought are among the most important processes affecting the formation of ecosystems. The main objective of this research was to investigate relationships of field-assessed plant parameters with remotely sensed vegetation cover and an index of drought severity. These relationships were assessed across a range of vegetation types and across seasonal and annual scales in the Khansar region of Isfahan Province, Iran. Vegetation cover was measured by the MODIS Normalized Difference Vegetation Index (NDVI) (extracted from the USGS MOD13Q1 image product), and drought severity was estimated by the Standardized Precipitation Index (SPI) (for intervals of 1, 3, 6, 9, and 12 months). Field-based plant parameters (vegetation cover, production rate, and leaf area index (LAI)) were measured for a range of vegetation types covering a range of conditions (315 sites in total, assessed in 2016), using a 4-pixel method. The correlation between NDVI, SPI, and field plant parameters was evaluated using Pearson correlation. The results indicated that the production rate and LAI were higher in rangelands with fair conditions than in those with degraded conditions and varied in each vegetation type during the year. The highest and lowest production rates were observed in Astragalus-Agropyrum (28 g m−2 year−1) and Artemisia sieberi-Scariola (9 g m−2 year−1), respectively. The correlation between NDVI and plant production varied across plant communities, from 0.552 in Artemisia sieberi-Scariola to 0.865 in Astragalus-Agropyrum. The correlation between NDVI and SPI was highest for 1-month intervals and varied by plant community between 0.622 (p = 0.006) for Scariola-Cousinia and 0.840 (p < 0.001) Astragalus-Agropyrum. In general, the observations revealed that vegetation type, vegetation form, rangeland condition, and the stratification of the region have key roles in drought monitoring.

Influence of Urbanization on the Dynamics of the Urban Vegetation Coverage Index (VCI) in Erechim (RS)

ABSTRACT Vegetation fragments located in urban perimeters provide better environmental conditions for biological diversity and human well-being. The aim of this study was to evaluate the Vegetation Coverage Index (VCI) of the urban perimeter of Erechim, RS, between years 2010 and 2015. Mapping was carried out using World View 2 and Pleiades satellites images with spatial resolution of 0.5 m. Significant difference was found between VCI values of 2010 and 2015, as well as a decrease in the recommended rate in the range of 30% per neighborhood. Due to the influence of increased urbanization, there was reduction of 229.98 hectares in the vegetation coverage area of the urban perimeter during the study period. The expansion of urban areas reduced vegetation fragments and has contributed in an effective manner to the disordered occupation of the city, generating negative impacts to the environment and to the quality of life of the local population.

Water erosion prediction at a national scale for South Africa

Erosion is a major soil degradation problem in South Africa, confronting both land and water resource management throughout the country. Given the increasing threat of soil erosion, a need to improve techniques of estimating the soil-erosion risk at a national scale was identified by the National Department of Agriculture and forms the basic premise of this study. Principles and components of the Revised Universal Soil Loss Equation are applied here since the model combines sufficient simplicity for application on a national scale with a comprehensive incorporation of the main soil-erosion factors. Indicators of erosion susceptibility of the physical environment, including climate erosivity, soil erodibility and topography were improved over earlier assessments by feeding current available data into advanced algorithms. Two maps are presented: an actual erosion-risk distribution, and a potential erosion-risk map that excludes the vegetation cover factor. Actual soil-erosion risk, which relates to the current risk of erosion under contemporary vegetation and land-use conditions, was accounted for by regression equations between vegetation cover and MODIS-derived spectral index. The area of land with a moderate to severe potential risk is found to total approximately 61 m. ha (50%). Although more than 91 m. (75%) are classified as having only a very low to low actual risk, approximately 26 m. ha (20%) of land is eroded at a rate greater than a soil-loss tolerance of 10 t/ha·yr, showing the potential to target erosion control to problem areas. The Eastern Cape, Limpopo and KwaZulu-Natal Provinces have the highest erosion potential. Comparison of potential and actual erosion risk indicates that over 26 m. ha (>30% of national land) could be subject to high erosion risk without maintenance or careful management of the current vegetation cover and land use. Although the distribution of the actual erosion risk broadly follows that outlined previously, this study provides an advance on previous assessments of erosion; results are validated more comprehensively than before, and show an overall accuracy of 77%. The paper also describes many of the limitations inherent in regional erosion studies.

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

Carbon exchange in a caatinga area during an unusually drought year

The objective of this study was to analyze the daily and seasonal variation of the carbon balance components in a caatinga area during an unusually drought year. Data were collected from a turbulent vortices covariance system installed at an area in Caatinga , in the region of Petrolina Municipality, State of Pernambuco, Brazil. Carbon dioxide flux data were collected in the year 2012, and the partitioning between gross primary productivity (GPP) and ecosystem respiration (Re). The vegetation cover index, weather elements and the soil water content were monitored. It was observed that the daily emissions of CO 2 flux was dependent upon photosynthetically active radiation in the first days after rainfall events, being influenced by the water vapor pressure deficit with the reduction in the soil water content. The occurrence of up to 2 mm rainfall promoted peaks of Re after long periods of drought. The measured NEP was equal to 468.18 gC m -2 year -1 . So, it is concluded that in years with severe drought, Caatinga may act as a source of carbon to the atmosphere.

The effects of restoration on vegetation trends: spatiotemporal variability and influencing factors

ABSTRACTSince the ‘Grain to Green Program' was launched in 1999 in the Loess Plateau, China, the processes and patterns of ecological change have become important. Through the use of the fractional vegetation cover (FVC) index, this study examines the spatial distribution and temporal change of vegetation cover in the Loess Plateau during 2000–2014. Over this period more than 60% of the Loess Plateau has remained with little vegetation cover (FVC &lt;30%). The spatial distribution pattern shows an overall increase from the NW to SE. Temporally, the vegetation cover exhibits a general trend of improvement. In 2000–2005, 2000–2010 and 2000–2014, the percentages of restored vegetation (vegetation with significantly increased FVC, P&lt;0.05) were 2, 21 and 52%, respectively. The rate of vegetation cover restoration was highly variable among different bioclimatic zones. The expansion of restored vegetation was greater in the N of the Loess Plateau than in the south. Both human restoration activities and climatic fluctuation influenced the vegetation cover change. The ‘Grain to Green Program' emphasised vegetation restoration. Regional precipitation also had clear effects on vegetation cover. The results of this study reveal that vegetation change shows a non-linear process in response to climate and ecological restoration measures, and that the change gradually emerges over time. This study highlights the importance of considering the spatiotemporal variability in vegetation cover during the implementation of restoration programs, which could aid decision-making for the effective and sustainable management of large-scale restoration programs.

Dynamic changes of wetland resources based on MODIS and Landsat image data fusion

Given the seasonal dynamics of wetland ecosystem, limited available data, and technological method of wetland investigation, wetland evaluation cannot be accurately accessed. Although the remote sensing technology has been widely employed on wetland investigation and identification, changeable weather conditions especially cloud interference are the main barrier to acquire clear remote sensing image for wetland identification and information extraction. The combination and precision evaluation of remote sensing data with high temporal-spatial resolution ratio from moderate-resolution imaging spectroradiometer (MODIS) and Landsat were conducted using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM): a comprehensively temporal-spatial reflectance model was built; the high-resolution image in the time series and Modified Normalized Difference Water Index were obtained. The main data obstacles in wetland resources monitoring were invalid. The typical wetland areas in Liaoning province of China were evaluated using combination algorithm and Landsat (Thematic Mapper) images. The results show that the MODIS and Landsat data can be combined well with high correlations in different wave ranges. The maximum Normalized Difference Water Index (NDWI) is 0.9678, followed by green wave (0.9630), near-infrared wave (0.9345), and blue wave (0.9018).The wetland seasonal change of Panjin was examined using the data combination method. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and vegetation coverage index were extracted from combined images of Panjin from June 2016 to August 2016 and analyzed. Results showed that the NDVI was high in partial water area during the studied period indicating high chlorophyll contents.

EVALUASI LAHAN UNTUK MENILAI KINERJA SUB DAERAH ALIRAN SUNGAI RAWAKAWUK (Land evaluation to access performance of Rawakawuk Sub Watershed)

Land is an important resource in a watershed because of its position as a processor. The condition of a watershed depends on the land management and its compliance with its performance. The purpose of the research is to evaluate the performance of Rawakawuk Sub Watershed from the land aspects. Guidelines of Watershed Evaluation and Monitoring, i.e. the Regulation of Director General of Land Rehabilitation and Social Forestry or PerDirjen 04/V-Set/2009 was used to analyze parameters of Vegetation Cover Index (VCI), Land Use Suitability Index (LUSI), and Erosion Index (EI). The results showed that the study area has 53% of pine and teak monoculture, and 20,7 % of agroforestry private forest, therefore the VCI is 73,2%. The LUSI is 93,7% which implies that 93,7% of the area is suitable to its land capability. In addition, 36,1% of the area is exposed to very severe erosion. Since the solum depth is relatively deep (>90 cm), the tolerable soil erosion is high and EI tends to be small. Around 60,3% of the area has an average EI of 71,1 which is classified as moderate. Based on the cumulative value of VCI, LUSI and EI, Rawakawuk Sub Watershed can be classified as slightly good. However, conservation practices should be applied to the very severe erosion areas.

Controlling factors of ‘Caatinga’ and sugarcane evapotranspiration in the Sub-middle São Francisco Valley

ABSTRACT In the Sub-middle São Francisco Valley, the main controlling factors of ‘Caatinga’ and irrigated sugarcane ETr were investigated in this study. Between 2015 and 2016, environmental variables were measured by sensors coupled to two micrometeorological towers, one in the preserved ‘Caatinga’ and the other in an irrigated sugarcane crop. Soil moisture and vegetation cover index were also evaluated and biometric data were obtained only in the sugarcane. Actual evapotranspiration was determined based on the latent heat flux, by the energy balance method. Therefore, the ratio between actual and reference evapotranspiration was calculated. Response variables were formed by the actual evapotranspiration and by its ratio with the reference evapotranspiration. Explanatory variables included growth and environmental data. Multicollinearity, canonical and track analyses were applied. It was verified that only the environmental variables exhibited correlation with the actual evapotranspiration and its ratio with the reference evapotranspiration. In the ‘Caatinga’, soil moisture directly and indirectly influenced ETr, as the global solar radiation altered the response of actual evapotranspiration to the atmospheric demand. In sugarcane, ETr was directly and indirectly controlled by the intercepted photosynthetically active radiation and soil temperature, and the latter one also affected the ratio between actual and reference evapotranspiration.

MAPPING AND VEGETATION COVER INDEX FROM CÁCERES CITY, MATO GROSSO STATE (MT), BRAZIL

Analyzing the urban space related to its own growth allows to identify the identification of the dynamics of the natural elements and the way the intensified anthropic action shapes and at the same time degrades the landscape, which in the present study is the Pantanal biome. The objective of this study is to use high resolution images and vegetation cover indexes to analyze the urban expansion of Cáceres/MT, and to generate financial support for municipal planning and management. For the execution of this research, remote sensing images and a Geographic Information System (GIS) were used, as well as demographic census data. The urban expansion contributed to the removal of 19.62% of the vegetation and to the increase of 15.28% of anthropic use. The Caceres Vegetation Cover Index is high, with vegetation percentages above 30% occurring in 74.42% of the neighborhoods. From the date of the study on the Index of Vegetation Cover for Inhabitant (ICVH) decreased by 37.20%, remained at 32.55% and increased by 30.25%. It was concluded that an increase in the use of urban space contributed to the reduction of vegetation, as well as the decrease of the population associated to the vegetation of the neighborhoods influenced on the decrease of the ICVH

Chlorophyll Content Detection of Field Maize Using RGB-NIR Camera

In order to rapidly detect the chlorophyll content in the field, the RGB-NIR camera was used to collect the multi-spectral image of maize canopy. The SPAD value was measured at the same time to indicate the chlorophyll content. The multi-spectral images were processed. Considering the features of multi-spectral images, after image median filtering and segmentation to remove the background effect by field soil, dry straw and weeds, 15 image parameters were extracted including the average grey value of each channel (AR, AG, AB and ANIR), the vegetation indices (ANDVI, ANDGI, ARVI, ADVI), vegetation coverage index (VCI), hue average(AH) and image texture parameters (energy (AASM), moment of inertia (ACON), correlation (ACOR), entropy (AEN) and inverse difference moment (AL)). The sensitive parameters were selected by correlation coefficient analysis and RF method. There were 4 and 8 parameters, respectively. After modeling, the LS-SVM model built by RF selected parameters showed better detection accuracy with Rc2=0.87 and Rv2=0.74. It could be used for rapid and non-destructive detection of the chlorophyll content for field maize.

Dynamic Assessment on the Landscape Patterns and Spatio-temporal Change in the mainstream of Tarim River

The Tarim River (TR), as the longest inland river at an arid area in China, is a typical regions of vegetation variation research and plays a crucial role in the sustainable development of regional ecological environment. In this paper, the newest dataset of MODND1M NDVI, at a resolution of 500m, were applied to calculate vegetation index in growing season during the period 2000-2015. Using a vegetation coverage index, a trend line analysis, and the local spatial autocorrelation analysis, this paper investigated the landscape patterns and spatio-temporal variation of vegetation coverage at regional and pixel scales over mainstream of the Tarim River, Xinjiang. The results showed that (1) The bare land area on both sides of Tarim River appeared to have a fluctuated downward trend and there were two obvious valley values in 2005 and 2012. (2) Spatially, the vegetation coverage improved areas is mostly distributed in upstream and the degraded areas is mainly distributed in the left bank of midstream and the end of Tarim River during 2000-2005. (3) The local spatial auto-correlation analysis revealed that vegetation coverage was spatially positive autocorrelated and spatial concentrated. The high-high self-related areas are mainly distributed in upstream, where vegetation cover are relatively good, and the low-low self-related areas are mostly with lower vegetation cover in the lower reaches of Tarim River.

Utilizations of Drone in Pasture: Seasonal Changes of Vegetation Cover and Vegetation Index

Utilizations of Drone in Pasture: Seasonal Changes of Vegetation Cover and Vegetation Index

CoastalDEM: A global coastal digital elevation model improved from SRTM using a neural network

Positive vertical bias in elevation data derived from NASA's Shuttle Radar Topography Mission (SRTM) is known to cause substantial underestimation of coastal flood risks and exposure. Previous attempts to correct SRTM elevations have used regression to predict vertical error from a small number of auxiliary data products, but these efforts have been focused on reducing error introduced solely by vegetative land cover. Here, we employ a multilayer perceptron artificial neural network to perform a 23-dimensional vertical error regression analyses, where in addition to vegetation cover indices, we use variables including neighborhood elevation values, population density, land slope, and local SRTM deviations from ICESat altitude observations. Using lidar data as ground truth, we train the neural network on samples of US data from 1–20m of elevation according to SRTM, and assess outputs with extensive testing sets in the US and Australia. Our adjustment system reduces mean vertical bias in the coastal US from 3.67m to less than 0.01m, and in Australia from 2.49m to 0.11m. RMSE is cut by roughly one-half at both locations, from 5.36m to 2.39m in the US, and from 4.15m to 2.46 in Australia. Using ICESat data as a reference, we estimate that global bias falls from 1.88m to −0.29m, and RMSE from 4.28m and 3.08m. The methods presented here are flexible and effective, and can be effectively applied to land cover of all types, including dense urban development. The resulting enhanced global coastal DEM (CoastalDEM) promises to greatly improve the accuracy of sea level rise and coastal flood analyses worldwide.

Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.

Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.

Identification of dominant interactions between climatic seasonality, catchment characteristics and agricultural activities on Budyko-type equation parameter estimation

Quantifying precipitation (P) partition into evapotranspiration (E) and runoff (Q) is of great importance for global and regional water availability assessment. Budyko framework serves as a powerful tool to make simple and transparent estimation for the partition, using a single parameter, to characterize the shape of the Budyko curve for a “specific basin”, where the single parameter reflects the overall effect by not only climatic seasonality, catchment characteristics (e.g., soil, topography and vegetation) but also agricultural activities (e.g., cultivation and irrigation). At the regional scale, these influencing factors are interconnected, and the interactions between them can also affect the single parameter of Budyko-type equations’ estimating. Here we employ the multivariate adaptive regression splines (MARS) model to estimate the Budyko curve shape parameter (n in the Choudhury’s equation, one form of the Budyko framework) of the selected 96 catchments across China using a data set of long-term averages for climatic seasonality, catchment characteristics and agricultural activities. Results show average storm depth (ASD), vegetation coverage (M), and seasonality index of precipitation (SI) are three statistically significant factors affecting the Budyko parameter. More importantly, four pairs of interactions are recognized by the MARS model as: The interaction between CA (percentage of cultivated land area to total catchment area) and ASD shows that the cultivation can weaken the reducing effect of high ASD (>46.78 mm) on the Budyko parameter estimating. Drought (represented by the value of Palmer drought severity index < -0.74) and uneven distribution of annual rainfall (represented by the value of coefficient of variation of precipitation > 0.23) tend to enhance the Budyko parameter reduction by large SI (>0.797). Low vegetation coverage (34.56%) is likely to intensify the rising effect on evapotranspiration ratio by IA (percentage of irrigation area to total catchment area). The Budyko n values estimated by the MARS model reproduce the calculated ones by the observation well for the selected 96 catchments (with R = 0.817, MAE = 4.09). Compared to the multiple stepwise regression model estimating the parameter n taken the influencing factors as independent inputs, the MARS model enhances the capability of the Budyko framework for assessing water availability at regional scale using readily available data.

ECONOMIC AND ENVIRONMENTAL SUSTAINABILITY OF AGRICULTURAL HOLDINGS WITHOUT ANIMAL PRODUCTION

This article undertakes to evaluate the economic and environmental sustainability of agricultural holdings not engaged in animal production. Studies covered agricultural holdings in the Podlaskie voivodeship included in the FADN system in 2014. The evaluation accounts for selected agri-ecological indicators, supplemented by material pressure indexes and economic indexes (profitability of land and labour). Obtained results indicate that environmental sustainability conditions were met only with regard to the vegetation cover index but were not met with regard to the remaining indexes (share of permanent grasslands, crop structure, balance of organic substances). Holdings without animals exerted lesser pressure on the environment than holdings engaged in animal production from the perspective of consumption of means of production. Both groups of holdings did not achieve economic sustainability. Income parity in 2014 amounted to 0.73 for holdings without animals and 0.84 for holdings with animal productions.