scholarly journals Assessments of Nipa Forest Using Landsat Imagery Enhanced with Unmanned Aerial Vehicle Photography

2020 ◽  
pp. 49-59
Author(s):  
Tantus Piekkoontod ◽  
Bhumiphat Pachana ◽  
Karnjana Hrimpeng ◽  
Kitsanai Charoenjit
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Mangrove Forest ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Small Scale ◽  
Ground Field ◽  
High Resolution Data ◽  
High Resolution Imagery

Nipa palms are exposed by the transformation of land use and land cover changes (LULCC) due to changes to aquaculture and orchards. Modern remote sensing for environmental monitoring of LULCC has been made easier by the use of high spatial resolution images, innovative image processing and Geographic Information Systems (GIS). The expense of high-resolution satellite imagery has resulted in investigators moving to open sources (e.g., Landsat), therefore, the interpretation of images at a medium resolution can be classified simply as LULCC classes and are constrained by the detection of small-scale disturbances. This research applied Landsat imagery with very high-resolution imagery from Unmanned Aerial Vehicles (UAVs). In order to be useful for real-world applications, the accuracy of remote sensing data must be validated using proven ground-based methods. UAVs equipped with multispectral sensors were flown over the Nipa palms at the Prasae River, Rayong Province, Thailand. The main advantage of UAV-based remote sensing is that it reduces costs and immediate availability of high-resolution data. The UAV imagery was expensed as “drone truthing data” to train image classification algorithms. These results show that UAV data can be used effectively to support and categorize similar land-cover/use classes (aquaculture vs. mangrove forest vs. nipa forest) with consistently high identification of over 87.6% on the generated thematic map, where the mangrove forest detection rate was as high as 86%. For that reason, UAVs are engaged successively in management and conservation tasks, which can be used for regional or local scale studies to compare the achieved accuracy to a general regional land cover map. This approach can be used for the variability of plants to rectify land-cover classification. Therefore, UAV images are a very useful tool to fill the gap between remote sensing information and expensive ground field campaigns.

scholarly journals Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring

2019 ◽  
Author(s):  
Xiaowei Jia ◽  
Mengdie Wang ◽  
Ankush Khandelwal ◽  
Anuj Karpatne ◽  
Vipin Kumar
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Temporal Patterns ◽  
Small Scale ◽  
High Resolution Data ◽  
Sensing Data ◽  
Coarse Resolution ◽  
Sequential Models ◽  
Resolution Data

Effective and timely monitoring of croplands is critical for managing food supply. While remote sensing data from earth-observing satellites can be used to monitor croplands over large regions, this task is challenging for small-scale croplands as they cannot be captured precisely using coarse-resolution data. On the other hand, the remote sensing data in higher resolution are collected less frequently and contain missing or disturbed data. Hence, traditional sequential models cannot be directly applied on high-resolution data to extract temporal patterns, which are essential to identify crops. In this work, we propose a generative model to combine multi-scale remote sensing data to detect croplands at high resolution. During the learning process, we leverage the temporal patterns learned from coarse-resolution data to generate missing high-resolution data. Additionally, the proposed model can track classification confidence in real time and potentially lead to an early detection. The evaluation in an intensively cultivated region demonstrates the effectiveness of the proposed method in cropland detection.

scholarly journals Ten simple rules for working with high resolution remote sensing data

2021 ◽  
Author(s):  
Maxwell Benjamin Joseph ◽  
Anna Spiers ◽  
Michael J. Koontz ◽  
Nayani Ilangakoon ◽  
Kylen Solvik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Environmental Science ◽  
Ad Hoc ◽  
Remote Sensing Data ◽  
Trial And Error ◽  
High Resolution Data ◽  
Sensing Data ◽  
Simple Rules ◽  
Resolution Data

Researchers in Earth and environmental science can extract incredible value from high resolution remote sensing data, but these data can be hard to use. Pain free use requires skills from remote sensing and the data sciences that are seldom taught together. In practice, many researchers teach themselves how to use high resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. Here we outline ten “rules” with examples from Earth and environmental science to help applied researchers work more effectively with high resolution data.

scholarly journals POTENSI PEMANFAATAN DRONE UNTUK PENYEDIAAN DATA WILAYAH PESISIR

OSEANA ◽  
2018 ◽  
Vol 43 (1) ◽  
pp. 44-52
Author(s):  
Bayu Prayudha
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Coastal Area ◽  
Service Providers ◽  
Remote Sensing Data ◽  
Unmanned Aircraft ◽  
High Resolution Imagery ◽  
Potential Use ◽  
Vast Area ◽  
Imagery Data

POTENTIAL USE OF DRONE FOR PROVIDING DATA ON COASTAL AREA. The accurate data and information are needed for the decision maker to manage coastal area. However, the data and information of the coastal area are still lack because Indonesia has vast area and some of the locations are difficult to reach. Remote sensing is a technology that can be utilized to answer those needs. Some of the remote sensing data, especially satellite imagery can be freely acquired from various service providers using online media. Nevertheless, high resolution imagery data is still not available freely because it takes high cost and not always available at any time. One of the potential vehicle to acquire high resolution imagery data of coastal area is Unmanned Aircraft Vehicle (UAV) or widely known as drone.

scholarly journals Land Cover Changes in Open-Cast Mining Complexes Based on High-Resolution Remote Sensing Data

2019 ◽  
Author(s):  
Filipe Silveira Nascimento ◽  
Markus Gastauer ◽  
Pedro Walfir M. Souza-Filho ◽  
Wilson R. Nascimento Jr. ◽  
Diogo C. Santos ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Land Cover Changes ◽  
Special Focus ◽  
Open Cast Mining ◽  
High Resolution Satellite Images ◽  
Open Cast

Remote sensing technologies may play a fundamental role in the environmental assessment of open-cast mining and the accurate quantification of mine land rehabilitation efforts. Here, we developed a systematic geographic object-based image analysis (GEOBIA) approach to map the amount of revegetated area and to quantify the land-use changes in open-cast mines in the Carajás region situated in the eastern Amazon. Based on high-resolution satellite images from 2011 to 2015 from different sensors (GeoEye, WorldView-3 and Ikonos), we quantified forests, cangas (natural metalliferous savanna ecosystems), mine land, revegetated areas and water bodies. Based on the GEOBIA approach, threshold values were established to discriminate land cover classes using spectral bands, and the NDVI and NDWI indices and LiDAR digital ground and slope models. The overall accuracy was higher than 90%, and the Kappa indices varied between 0.82 and 0.88. During the observation period, the mining complex expanded; for that, canga and forest vegetation was converted to mine land. At the same time, the amount of revegetated area increased. Thus, we conclude that our approach is capable of providing consistent information regarding land cover changes in mines, with a special focus on the amount of revegetation necessary to fulfill environmental liabilities.

scholarly journals An Improved Method for Impervious Surface Mapping Incorporating LiDAR Data and High-Resolution Imagery at Different Acquisition Times

2018 ◽  
Vol 10 (9) ◽  
pp. 1349 ◽  
Author(s):  
Hui Luo ◽  
Le Wang ◽  
Chen Wu ◽  
Lei Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Sensor Data ◽  
Lidar Data ◽  
Landscape Changes ◽  
Surface Mapping ◽  
High Resolution Imagery ◽  
High Resolution Images

Impervious surface mapping incorporating high-resolution remote sensing imagery has continued to attract increasing interest, as it can provide detailed information about urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping yields better performance than the use of high-resolution imagery alone. However, due to LiDAR data’s high cost of acquisition, it is difficult to obtain LiDAR data that was acquired at the same time as the high-resolution imagery in order to conduct impervious surface mapping by multi-sensor remote sensing data. Consequently, the occurrence of real landscape changes between multi-sensor remote sensing data sets with different acquisition times results in misclassification errors in impervious surface mapping. This issue has generally been neglected in previous works. Furthermore, observation differences that were generated from multi-sensor data—including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images—also present obstacles to achieving the final mapping result in the fusion of LiDAR data and high-resolution images. In order to resolve these issues, we propose an improved impervious surface-mapping method incorporating both LiDAR data and high-resolution imagery with different acquisition times that consider real landscape changes and observation differences. In the proposed method, multi-sensor change detection by supervised multivariate alteration detection (MAD) is employed to identify the changed areas and mis-registered areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution image are extracted via independent classification based on the corresponding single-sensor data. Finally, an object-based post-classification fusion is proposed that takes advantage of both independent classification results while using single-sensor data and the joint classification result using stacked multi-sensor data. The impervious surface map is subsequently obtained by combining the landscape classes in the accurate classification map. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and unambiguously improve the performance of impervious surface mapping.

scholarly journals Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

2019 ◽  
Vol 11 (1) ◽  
pp. 69 ◽  
Author(s):  
Zachary L. Langford ◽  
Jitendra Kumar ◽  
Forrest M. Hoffman ◽  
Amy L. Breen ◽  
Colleen M. Iversen
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Ecosystem Structure ◽  
Modeling And Analysis ◽  
High Resolution Imagery

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ∼343 km2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations.

Sign in / Sign up

Export Citation Format

Share Document