Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.

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Automatic cost-effective method for land cover classification (ALCC)

Computers Environment and Urban Systems ◽

10.1016/j.compenvurbsys.2019.03.001 ◽

2019 ◽

Vol 76 ◽

pp. 1-10 ◽

Cited By ~ 12

Author(s):

Mateo Gašparović ◽

Mladen Zrinjski ◽

Marina Gudelj

Keyword(s):

Land Cover ◽

Cost Effective ◽

Land Cover Classification ◽

Cost Effective Method

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Canopy Height Estimation from Single Multispectral 2D Airborne Imagery Using Texture Analysis and Machine Learning in Structurally Rich Temperate Forests

Remote Sensing ◽

10.3390/rs11232853 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2853

Author(s):

Christos Boutsoukis ◽

Ioannis Manakos ◽

Marco Heurich ◽

Anastasios Delopoulos

Keyword(s):

Land Cover ◽

Texture Analysis ◽

Spatial Scales ◽

Computational Cost ◽

Texture Feature ◽

Cost Effective ◽

Canopy Height ◽

Support Vector ◽

Structural Parameter ◽

Wide Range

Canopy height is a fundamental biophysical and structural parameter, crucial for biodiversity monitoring, forest inventory and management, and a number of ecological and environmental studies and applications. It is a determinant for linking the classification of land cover to habitat categories towards building one-to-one relationships. Light detection and ranging (LiDAR) or 3D Stereoscopy are the commonly used and most accurate remote sensing approaches to measure canopy height. However, both require significant time and budget resources. This study proposes a cost-effective methodology for canopy height approximation using texture analysis on a single 2D image. An object-oriented approach is followed using land cover (LC) map as segmentation vector layer to delineate landscape objects. Global texture feature descriptors are calculated for each land cover object and used as variables in a number of classifiers, including single and ensemble trees, and support vector machines. The aim of the analysis is the discrimination among classes in a wide range of height values used for habitat mapping (from less than 5 cm to 40 m). For that task, different spatial resolutions are tested, representing a range from airborne to spaceborne quality ones, as well as their combinations, forming a multiresolution training set. Multiple dataset alternatives are formed based on the missing data handling, outlier removal, and data normalization techniques. The approach was applied using orthomosaics from DMC II airborne images, and evaluated against a reference LiDAR-derived canopy height model (CHM). Results reached overall object-based accuracies of 67% with the percentage of total area correctly classified exceeding 88%. Sentinel-2 simulation and multiresolution analysis (MRA) experiments achieved even higher accuracies of up to 85% and 91%, respectively, at reduced computational cost, showing potential in terms of transferability of the framework to large spatial scales.

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Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

Remote Sensing ◽

10.3390/rs12071220 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1220 ◽

Cited By ~ 4

Author(s):

Thuan Sarzynski ◽

Xingli Giam ◽

Luis Carrasco ◽

Janice Ser Huay Lee

Keyword(s):

Land Cover ◽

Southeast Asia ◽

Oil Palm ◽

Elaeis Guineensis ◽

Radar Data ◽

Google Earth ◽

Visual Interpretation ◽

Google Earth Engine ◽

Radar Satellite ◽

Government Statistics

Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia.

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Landscape Influences on Stream Habitats and Biological Assemblages

Landscape Influences on Stream Habitats and Biological Assemblages ◽

10.47886/9781888569766.ch20 ◽

2006 ◽

Keyword(s):

Land Cover ◽

Fish Assemblages ◽

Spatial Scales ◽

Cumulative Effect ◽

Stream Fish ◽

Negative Effects ◽

Forest Harvest ◽

Land Cover Types ◽

Fine Sediments ◽

Habitat Variables

<em>Abstract.</em>—Forest harvests have been shown to have negative effects on stream fish and habitat; however, the relationship between these factors, and the magnitude of these effects, has received little study. We investigated the influence that various land-cover types (including recent forest harvest) have on fish assemblages at multiple spatial scales and compared these results to the influences of local instream habitat variables. Satellite land-cover data and land management harvest maps were used to characterize the land-cover types throughout the Knife River basin in northeast Minnesota. Eleven spatial scales (with 30-m and 100-m buffer widths), including site, reach, stream corridor, and catchment, were evaluated. Forward stepwise regression was used to relate land cover to coldwater index of biotic integrity scores and metrics. Land-cover relationships varied with spatial scale, but land cover at the catchment and corridor scales explained the most variation in fish and habitat variables. Generally, increases in forest cover and decreases in water/wetland were associated with higher quality fish assemblages and instream habitat. No negative effects of forest harvest were found at the site or reach scales. Forest harvest 5–8 years old was negatively related to fish assemblage quality at the stream corridor and catchment scales, possibly related to changes in temperature and substrate at the corridor scale, and increases in fine sediments and unstable banks at the catchment scale. The cumulative effect of increasing forest harvest from 0 to 8 years old throughout the catchment was associated with lower quality fish assemblages and instream habitat, indicating that large increases in similar timed forest harvests throughout a catchment (not just in the riparian zone) can have negative effects on stream fish and habitat.

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Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations

Remote Sensing ◽

10.3390/rs12040636 ◽

2020 ◽

Vol 12 (4) ◽

pp. 636

Author(s):

Xiaowei Jia ◽

Ankush Khandelwal ◽

Kimberly M. Carlson ◽

James S. Gerber ◽

Paul C. West ◽

...

Keyword(s):

Land Cover ◽

Ensemble Learning ◽

Large Scale ◽

Spatial Scales ◽

Remote Sensing Data ◽

High Temporal Resolution ◽

Visual Interpretation ◽

Study Region ◽

Small Areas ◽

Modis Data

Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user’s accuracy and producer’s accuracy of our generated map are around 85% and 80% in our study region.

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Accuracy Assessment of the FROM-GLC30 Land Cover Dataset Based on Watershed Sampling Units: A Continental-Scale Study

Sustainability ◽

10.3390/su12208435 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8435

Author(s):

Zitian Guo ◽

Chunmei Wang ◽

Xin Liu ◽

Guowei Pang ◽

Mengyang Zhu ◽

...

Keyword(s):

High Resolution ◽

Land Cover ◽

Large Scale ◽

Reference Data ◽

Accuracy Assessment ◽

Visual Interpretation ◽

Land Cover Types ◽

Practical Applications ◽

Land Type ◽

Continental Scale

Land cover information plays an essential role in the study of global surface change. Multiple land cover datasets have been produced to meet various application needs. The FROM-GLC30 (Finer Resolution Observation and Monitoring of Global Land Cover) dataset is one of the latest land cover products with a resolution of 30 m, which is a relatively high resolution among global public datasets, and the accuracy of this dataset is of great concern in many related researches. The objective of this study was to calculate the accuracy of the FROM-GLC30 2017 dataset at the continental scale and to explore the spatial variation differences of each land type accuracy in different regions. In this study, the visual interpretation land cover results at 20,936 small watershed sampling units based on high-resolution remote sensing images were used as the reference data covering 65 countries in Asia, Europe, and Africa. The reference data were verified by field survey in typical watersheds. Based on that, the accuracy assessment of the FROM-GLC30 2017 dataset was carried out. The results showed (1) the area proportion of different land cover types in the FROM-GLC30 2017 dataset was generally consistent with that of the reference data. (2) The overall accuracy of the FROM-GLC30 2017 dataset was 72.78%, and was highest in West Asia–Northeast Africa, and lowest in South Asia. (3) Among all the seven land cover types, the accuracy of bareland and forest was relatively higher than that of others, and the accuracy of shrubland was the lowest. The accuracy for each land cover type differed among regions. The results of this work can provide useful information for land cover accuracy assessment researches at a large scale and promote the further practical applications of the open-source land cover datasets.

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SAFARI 2000 ESTIMATED BVOC EMISSIONS FOR SOUTHERN AFRICAN LAND COVER TYPES

ORNL Distributed Active Archive Center Datasets ◽

10.3334/ornldaac/764 ◽

2004 ◽

Cited By ~ 1

Author(s):

L. OTTER

Keyword(s):

Land Cover ◽

Land Cover Types

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Using off – set handpumps — a cost-effective method to protect drinking water in rural villages

Waterlines ◽

10.3362/0262-8104.1984.019 ◽

1984 ◽

Vol 2 (4) ◽

pp. 25-26

Author(s):

Daniel Jenkins

Keyword(s):

Drinking Water ◽

Cost Effective ◽

Rural Villages ◽

Cost Effective Method

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CAD Of Anticorrosive Protection Of Steel Structures

Promyshlennoe i Grazhdanskoe Stroitel stvo ◽

10.33622/0869-7019.2019.09.18-23 ◽

2019 ◽

pp. 18-23

Keyword(s):

Protective Coating ◽

Computer Aided Design ◽

Steel Structures ◽

Cost Effective ◽

Operating Conditions ◽

Computational Tool ◽

Protection Method ◽

Cost Effective Method ◽

The Cost ◽

Aided Design

The choice of cost-effective method of anticorrosive protection of steel structures is an urgent and time consuming task, considering the significant number of protection ways, differing from each other in the complex of technological, physical, chemical and economic characteristics. To reduce the complexity of solving this problem, the author proposes a computational tool that can be considered as a subsystem of computer-aided design and used at the stage of variant and detailed design of steel structures. As a criterion of the effectiveness of the anti-corrosion protection method, the cost of the protective coating during the service life is accepted. The analysis of existing methods of steel protection against corrosion is performed, the possibility of their use for the protection of the most common steel structures is established, as well as the estimated period of effective operation of the coating. The developed computational tool makes it possible to choose the best method of protection of steel structures against corrosion, taking into account the operating conditions of the protected structure and the possibility of using a protective coating.

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