Correction: Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification.
Ontology highlight
ABSTRACT:
SUBMITTER: Ayanu Y
PROVIDER: S-EPMC4549280 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
Correction: Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification.
Ayanu Yohannes Y Conrad Christopher C Jentsch Anke A Koellner Thomas T
PloS one 20150825 8
Similar Datasets
Project description:The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.
| S-EPMC4476797 | biostudies-literature
Project description:In the longtime challenge of identifying specific, easily detectable and reliable biomarkers of IPF, BALF proteomics is providing interesting new insights into its pathogenesis. To the best of our knowledge, the present study is the first shotgun proteomic investigation of EVs isolated from BALF of IPF patients. Our main aim was to characterize the proteome of the vesicular component of BALF and to explore its individual impact on the pathogenesis of IPF. To this purpose, ultracentrifugation was chosen as the EVs isolation technique, and their purification was assessed by TEM, 2DE and LC-MS/MS. Our 2DE data and scatter plots showed considerable differences between the proteome of EVs and that of whole BALF and of its fluid component. Analysis of protein content and protein functions evidenced that EV proteins are predominantly involved in cytoskeleton remodeling, adenosine signaling, adrenergic signaling, C-peptide signaling and lipid metabolism. Our findings may suggest a wider system involvement in the disease pathogenesis and support the importance of pre-fractioning of complex samples, such as BALF, in order to let low-abundant proteins-mediated pathways emerge.
| S-EPMC8199247 | biostudies-literature
Project description:The classification of remote sensing images is inherently challenging due to the complexity, diversity, and sparsity of the data across different image samples. Existing advanced methods often require substantial modifications to model architectures to achieve optimal performance, resulting in complex frameworks that are difficult to adapt. To overcome these limitations, we propose a lightweight ensemble method, enhanced by pure data correction, called the Exceptionally Straightforward Ensemble. This approach eliminates the need for extensive structural modifications to models. A key innovation in our method is the introduction of a novel strategy, quantitative augmentation, implemented through a plug-and-play module. This strategy effectively corrects feature distributions across remote sensing data, significantly improving the performance of Convolutional Neural Networks and Vision Transformers beyond traditional data augmentation techniques. Furthermore, we propose a straightforward algorithm to generate an ensemble network composed of two components, serving as the proposed lightweight classifier. We evaluate our method on three well-known datasets, with results demonstrating that our ensemble models outperform 48 state-of-the-art methods published since 2020, excelling in accuracy, inference speed, and model compactness. Specifically, our models achieve an overall accuracy of up to 96.8%, representing a 1.1% improvement on the challenging NWPU45 dataset. Moreover, the smallest model in our ensemble reduces parameters by up to 90% and inference time by 74%. Notably, our approach significantly enhances the performance of Convolutional Neural Networks and Vision Transformers, even with limited training data, thus alleviating the performance dependence on large-scale datasets. In summary, our data-driven approach offers an efficient, accessible solution for remote sensing image classification, providing an elegant alternative for researchers in geoscience fields who may have limited time or resources for model optimization.
| S-EPMC11829047 | biostudies-literature
Project description:Climate and land use models predict that tropical deforestation and conversion to cropland will produce a large flux of soil carbon (C) to the atmosphere from accelerated decomposition of soil organic matter (SOM). However, the C flux from the deep tropical soils on which most intensive crop agriculture is now expanding remains poorly constrained. To quantify the effect of intensive agriculture on tropical soil C, we compared C stocks, radiocarbon, and stable C isotopes to 2 m depth from forests and soybean cropland created from former pasture in Mato Grosso, Brazil. We hypothesized that soil disturbance, higher soil temperatures (+2°C), and lower OM inputs from soybeans would increase soil C turnover and deplete C stocks relative to nearby forest soils. However, we found reduced C concentrations and stocks only in surface soils (0-10 cm) of soybean cropland compared with forests, and these differences could be explained by soil mixing during plowing. The amount and Δ14C of respired CO2 to 50 cm depth were significantly lower from soybean soils, yet CO2 production at 2 m deep was low in both forest and soybean soils. Mean surface soil δ13C decreased by 0.5‰ between 2009 and 2013 in soybean cropland, suggesting low OM inputs from soybeans. Together these findings suggest the following: (1) soil C is relatively resistant to changes in land use and (2) conversion to cropland caused a small, measurable reduction in the fast-cycling C pool through reduced OM inputs, mobilization of older C from soil mixing, and/or destabilization of SOM in surface soils.
| S-EPMC5993338 | biostudies-literature
Project description:Grassland is one of the most represented, while at the same time, ecologically endangered, land cover categories in the European Union. In view of the global climate change, detecting its change is growing in importance from both an environmental and a socio-economic point of view. A well-recognised tool for Land Use and Land Cover (LULC) Change Detection (CD), including grassland changes, is Remote Sensing (RS). An important aspect affecting the accuracy of change detection is finding the optimal indicators of LULC changes (i.e., variables). Inappropriately selected variables can produce inaccurate results burdened with a number of uncertainties. The aim of our study is to find the most suitable variables for the detection of grassland to cropland change, based on a pair of high resolution images acquired by the Landsat 8 satellite and from the vector database Land Parcel Identification System (LPIS). In total, 59 variables were used to create models using Generalised Linear Models (GLM), the quality of which was verified through multi-temporal object-based change detection. Satisfactory accuracy for the detection of grassland to cropland change was achieved using all of the statistically identified models. However, a three-variable model can be recommended for practical use, namely by combining the Normalised Difference Vegetation Index (NDVI), Wetness and Fifth components of Tasselled Cap. Increasing number of variables did not significantly improve the accuracy of detection, but rather complicated the interpretation of the results and was less accurate than detection based on the original Landsat 8 images. The results obtained using these three variables are applicable in landscape management, agriculture, subsidy policy, or in updating existing LULC databases. Further research implementing these variables in combination with spatial data obtained by other RS techniques is needed.
| S-EPMC6129385 | biostudies-literature
Project description:Intact forests and protected areas (PAs) are central to global biodiversity conservation and nature-based climate change mitigation. However, cropland encroachment threatens the ecological integrity and resilience of their functioning. Using satellite observations, we find that a large proportion of croplands in the remaining forests globally have been gained during 2003-2019, especially for high-integrity forests (62%) and non-forest biomes (60%) and tropical forests (47%). Cropland expansion during 2011-2019 in forests globally has even doubled (130% relative increase) than 2003-2011, with high medium-integrity (190%) and high-integrity (165%) categories and non-forest (182%) and tropical forest biomes (136%) showing higher acceleration. Unexpectedly, a quarter of croplands in PAs globally were gained during 2003-2019, again with a recent accelerated expansion (48%). These results suggest insufficient protection of these irreplaceable landscapes and a major challenge to global conservation. More effective local, national, and international coordination among sustainable development goals 15, 13, and 2 is urgently needed.
| S-EPMC10074200 | biostudies-literature
Project description:Simple and multiple linear regression analyses are statistical methods used to investigate the link between activity/property of active compounds and the structural chemical features. One assumption of the linear regression is that the errors follow a normal distribution. This paper introduced a new approach to solving the simple linear regression in which no assumptions about the distribution of the errors are made. The proposed approach maximizes the probability of observing the event according to the random error. The use of the proposed approach is illustrated in ten classes of compounds with different activities or properties. The proposed method proved reliable and was showed to fit properly the observed data compared to the convenient approach of normal distribution of the errors.
| S-EPMC4458545 | biostudies-literature
Project description:Mitochondria are one of most characterized metabolic hubs of the cell. Here, crucial biochemical reactions occur and most of the cellular adenosine triphosphate (ATP) is produced. In addition, mitochondria act as signalling platforms and communicate with the rest of the cell by modulating calcium fluxes, by producing free radicals, and by releasing bioactive proteins. It is emerging that mitochondrial metabolites can also act as second messengers and can elicit profound (epi)genetic changes. This review describes the many signalling functions of mitochondrial metabolites under normal and stress conditions, focusing on metabolites of the tricarboxylic acid cycle. We provide a new framework for understanding the role of mitochondrial metabolism in cellular pathophysiology.
| S-EPMC5311903 | biostudies-literature
Project description:Understanding COVID-19 contagion among poor populations is hampered by a paucity of data, and especially so in remote rural communities with limited access to transportation, communication, and health services. We report on the first study on COVID-19 contagion across rural communities without road access. We conducted telephone surveys with over 400 riverine communities in the Peruvian Amazon in the early phase of the pandemic. During the first wave (April-June, 2020), COVID-19 spread from cities to most communities through public and private river transportation according to their remoteness. The initial spread was delayed by transportation restrictions but at the same time was driven in unintended ways by government social assistance. During the second wave (August, 2020), although people's self-protective behaviors (promoted through communication access) helped to suppress the contagion, people responded to transportation restrictions and social assistance in distinct ways, leading to greater contagion among Indigenous communities than mestizo communities. As such, the spatial contagion during the early phase of the pandemic in tropical forests was shaped by river transportation and social behaviors. These novel findings have important implications for research and policies on pandemics in rural areas.
| S-EPMC9713114 | biostudies-literature
Project description:Background and purposeIn clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.MethodsWe present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.ResultsExperimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.
| S-EPMC4880324 | biostudies-literature