Improving Agricultural Management in a Large-Scale Paddy Field by Using Remotely Sensed Data in the Ceres-Rice Model

2016 ◽  
Vol 65 (2) ◽  
pp. 224-228
Author(s):  
Mojtaba Rezaei ◽  
Ali Shahnazari ◽  
Mahmoud Raeini Sarjaz ◽  
Majid Vazifedoust
Keyword(s):  
Paddy Field ◽  
Large Scale ◽  
Remotely Sensed ◽  
Agricultural Management ◽  
Remotely Sensed Data

The delineation of agricultural management zones with high resolution remotely sensed data

2009 ◽  
Vol 10 (6) ◽  
pp. 471-487 ◽  
Author(s):  
Xiaoyu Song ◽  
Jihua Wang ◽  
Wenjiang Huang ◽  
Liangyun Liu ◽  
Guangjian Yan ◽  
...  
Keyword(s):  
High Resolution ◽  
Remotely Sensed ◽  
Agricultural Management ◽  
Remotely Sensed Data ◽  
Management Zones

scholarly journals Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3166
Author(s):  
Jash R. Parekh ◽  
Ate Poortinga ◽  
Biplov Bhandari ◽  
Timothy Mayer ◽  
David Saah ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Remotely Sensed Data ◽  
Statistical Indices ◽  
Deep Learning Neural Network

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.

scholarly journals Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science

2019 ◽  
Vol 11 (4) ◽  
pp. 463 ◽  
Author(s):  
Céline Boisvenue ◽  
Joanne White
Keyword(s):  
Remote Sensing ◽  
Information Needs ◽  
Large Scale ◽  
Remotely Sensed ◽  
Forest Carbon ◽  
Carbon Accumulation ◽  
Accurate Estimation ◽  
Next Generation ◽  
Remotely Sensed Data ◽  
Large Area

Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.

Prediction of spatial ET-fluxes using remote sensing and field data of selected areas in the Eastern Part of Abu Dhabi, United Arab Emirates

Soil Research ◽  
2006 ◽  
Vol 44 (8) ◽  
pp. 759
Author(s):  
Fares M. Howari ◽  
Ahmed Murad ◽  
Hassan Garamoon
Keyword(s):  
Remote Sensing ◽  
United Arab Emirates ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hydrological Cycle ◽  
Remotely Sensed ◽  
Arid Environments ◽  
Remotely Sensed Data ◽  
Considerable Work

Evapotranspiration (ET) is a major source of water depletion in arid and semi-arid environments; and it is a poorly quantified variable in the hydrological cycle. Remote sensing has the potential application to quantify this variable especially at large scale. The present study reports methodology useful to determine whether derived variables from remotely sensed data, such as vegetation and soil brightness indices, could be used to predict ET. To achieve this goal, various regression analyses were conducted between data derived from satellites, field meteorological stations, and ET values. Selected sub-scenes of Landsat Enhanced Thematic Mapper images free of cloud were used to derive Normalized Difference Vegetation Index (NDVI) and Soil Brightness Index using ER-Mapper and JMP software packages. From the obtained relationship between NDVI and ET, it was observed that ET increases sharply with increase in NDVI. The predicted ET results obtained from the multiple regression functions of field ET, NDVI, solar radiation, wind velocity, and/or temperature are comparable with the ET values obtained by Penman-Monteith method. The results showed that a remotely sensed vegetation index could be used, indirectly, to determine ET values. However, there is still considerable work to be done before simple and full automated extraction of ET from the reported methods can be achieved for large-scale applications.

scholarly journals Modelling and Mapping Above Ground Biomass Using Sentinel 2 and Planet Scope Remotely Sensed Data in West Usambara Tropical Rainforests, Tanzania

2021 ◽  
Author(s):  
Ernest William Mauya ◽  
Sami Madundo
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Tropical Rainforests ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Remotely Sensed Data ◽  
Tropical Regions ◽  
Sentinel 2

Abstract Forest biomass estimation using field -based inventories at a large scale is challenging and generally entails large uncertainty in tropical regions. With their wall-to-wall coverage ability, optical remote sensing signals had gained a wide acceptance for larger scale estimation of AGB at different spatial scales, ranging from local to global. However, their applicability in tropical forests is still limited. In this study, we investigated the performance of Sentinel 2 and Planet Scope remotely sensed data for AGB modelling, predicting and mapping in the tropical rainforest of Tanzania. A total of 296 field inventory plots were measured across the west Usambara mountain forests. AGB values were computed for each of the field plot in Mg/ha, and related with remotely sensed predictor variables using parametric and non- parametric statistical methods. Band values, vegetation indices and texture based variables were derived from each of the remotely sensed data. The AGB models were developed and validated using k-fold cross validation and their relative root mean square error (cvRMSEr%) were used to judge their accuracies. Relative efficiency (RE) of each dataset as compared to pure field inventory was also computed. The results showed that, Sentinel 2 based model fitted using generalized linear models (RMSEr = 67.00 % and pseudo-R2= 20%) had better performance as compared to Planet Scope based models (cvRMSEr = 72.1 % and pseudo-R2= 5.2%). Overall GLMs resulted into a models with less prediction error as compared to random forest when using Sentinel 2 data. However, for the Planet Scope, there was marginal improvement of using random forest (cvRMSEr = 72.0%) as compared to GLMs. Models, that in cooperated texture variables resulted into better prediction accuracy as compared to those with band values and indices only. The R.E values for Sentinel2 and Planet Scope were 1.2 and 1.1 respectively. Our study had demonstrated that Sentinel 2 and Planet Scope remotely sensed data can be used to develop cost-effective method for AGB estimation within the context of tropical rainforests of Tanzania. Further studies are however encouraged to look more on the best way of optimizing the efficiency of the two data sources in AGB estimations.

scholarly journals A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation

2014 ◽  
Vol 70 ◽  
pp. 110-119 ◽  
Author(s):  
Jiyuan Li ◽  
Lingkui Meng ◽  
Frank Z. Wang ◽  
Wen Zhang ◽  
Yang Cai
Keyword(s):  
Data Aggregation ◽  
Large Scale ◽  
Remotely Sensed ◽  
Map Reduce ◽  
Remotely Sensed Data
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