land covers
Recently Published Documents


TOTAL DOCUMENTS

308
(FIVE YEARS 138)

H-INDEX

26
(FIVE YEARS 7)

2022 ◽  
Vol 14 (2) ◽  
pp. 754
Author(s):  
István Valánszki ◽  
Lone Søderkvist Kristensen ◽  
Sándor Jombach ◽  
Márta Ladányi ◽  
Krisztina Filepné Kovács ◽  
...  

Despite the growing quantity of ecosystem-services-related research, there is still a lack of deeper understanding on cultural ecosystem services (CES). This is mainly due to the perception of CES, which can vary by geographic location and population. In this study, we present a Public Participation Geographic Information System (PPGIS) method in a Hungarian microregion. Our goal is to increase understanding on how cultural services are perceived in this geographical context and level, and how this relative importance is related to biophysical landscape features. We also consider the influence of accessibility on the perceived landscape and compare our findings with the results of other studies with different sociocultural backgrounds. The research consists of participatory mapping with 184 persons that were digitized and analyzed with GIS and statistical software. During the analysis, we identified CES hotspots and compared CES with landscape features, as well as CES perception with accessibility. Our results showed positive correlation of CES with land covers related to built-up areas, as well as aesthetic and recreational services with water bodies. Compared to other studies, we found different spatial relationships in the case of spiritual services, and higher importance of agricultural land covers during the CES perception, thanks to the Central-Eastern European (CEE) sociocultural background. Our study highlights the effect of accessibility on CES perception; nevertheless, these relationships varied by different infrastructural elements. We conclude by discussing the implications and limitations of our study and encouraging future landscape research to apply the PPGIS method in this geographical context.


2021 ◽  
Author(s):  
Tuvia Turkeltaub ◽  
Jiao Wang ◽  
Qinbo Cheng ◽  
Xiaoxu Jia ◽  
Yuanjun Zhu ◽  
...  

Laws ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 92
Author(s):  
Elena A. Mikhailova ◽  
Lili Lin ◽  
Zhenbang Hao ◽  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
...  

The state of Rhode Island (RI) has established its greenhouse gas (GHG) emissions reduction goals, which require rapidly acquired and updatable science-based data to make these goals enforceable and effective. The combination of remote sensing and soil information data can estimate the past and model future GHG emissions because of conversion of “low disturbance” land covers (e.g., evergreen forest, hay/pasture) to “high disturbance” land covers (e.g., low-, medium-, and high-intensity developed land). These modeled future emissions can be used as a predevelopment potential GHG emissions information disclosure. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for RI by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. Obtained results provide accurate and quantitative spatio-temporal information about likely GHG emissions and show their patterns which are often associated with existing urban developments. These remote sensing tools could be used by the state of RI to both understand the nature of land cover change and likely GHG emissions from soil and to institute mandatory or voluntary predevelopment assessments and potential GHG emissions disclosures as a part of a climate mitigation policy.


Check List ◽  
2021 ◽  
Vol 17 (6) ◽  
pp. 1639-1646
Author(s):  
Martín de Jesús Cervantes-López ◽  
Ricard Arasa-Gisbert ◽  
Omar Hernández-Ordóñez ◽  
Víctor Arroyo-Rodríguez

We document the first verifiable records of Claudius angustatus Cope, 1865 in the Selva Lacandona, Chiapas, Mexico. Three individuals were observed in different types of anthropic land covers. These records are the most recent observations of C. angustatus in the southeastern zone of its range in more than 20 years, thus representing the southernmost known occurrences of this species. With these records we confirm the long-suspected presence of C. angustatus in the region, increasing the number of reptile species in the Selva Lacandona to 91.


2021 ◽  
Author(s):  
Yan Yan ◽  
Weige Zhang ◽  
Yunfeng Hu ◽  
Huaipeng Liu ◽  
Xiaoping Zhang ◽  
...  

Abstract Precise spatiotemporal datasets of artificial impervious surfaces (AISs) are essential for evaluating urbanization processes and associated soil organic carbon (SOC) dynamics. However, spatially explicit studies on SOC stocks based on high-quality AIS data remain deficient, which affects the accuracy of urban SOC budgets. In this study, we used 30-m Landsat images and a subpixel-based model to accurately evaluate and quantify the annual AIS of Kaifeng, an ancient city in China that experienced intensive urbanization from 2000 to 2020. Soil organic carbon (SOC) dynamics were further estimated and spatially exhibited based on the SOC densities (SOCD) of different land covers observed in the field. Our results demonstrate that Kaifeng experienced drastic AIS expansion from 2000–2020, both in total area (an increase of ~154.35%) and density (described by mean AIS abundance, 0.56 vs. 0.72). Spatially, AIS mainly sprawled to the west, and infilling was observed in the old town. Moreover, the expansion of AIS in Kaifeng has resulted in a total of 0.08 Tg of SOC loss over the past 20 years, and the study area has acted as a clear carbon source. The greatest SOC losses occurred during 2010 — 2015, mainly in the west — with >30% (~0.024 Tg) of the total loss occurring between 2010 and 2015. This study provides new insights into urban growth through the mapping of growth patterns in terms of both outward sprawl and infill. We also provide a novel means of presenting the spatial patterns of urbanization-induced SOC dynamics using subpixel AIS maps.


2021 ◽  
Vol 13 (22) ◽  
pp. 4683
Author(s):  
Masoumeh Aghababaei ◽  
Ataollah Ebrahimi ◽  
Ali Asghar Naghipour ◽  
Esmaeil Asadi ◽  
Jochem Verrelst

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.


Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1324
Author(s):  
Hang Li ◽  
Ichchha Thapa ◽  
James H. Speer

Global warming and related disturbances, such as drought, water, and heat stress, are causing forest decline resulting in regime shifts. Conventional studies have combined tree-ring width (TRW) and the normalized difference vegetation index (NDVI) to reconstruct NDVI values and ignored the influences of mixed land covers. We built an integrated TRW-NDVI model and reconstructed the annual NDVI maps by using 622 Landsat satellite images and tree cores from 15 plots using point-by-point regression. Our model performed well in the study area, as demonstrated by significant reconstructions for 71.14% (p < 0.05) of the area with the exclusion of water and barren areas. The error rate between the reconstructed NDVI using the conventional approach and our approach could reach 10.36%. The 30 m resolution reconstructed NDVI images in the recent 100 years clearly displayed a decrease in vegetation density and detected decades-long regime shifts from 1906 to 2015. Our study site experienced five regime shifts, markedly the 1930s and 1950s, which were megadroughts across North America. With fine resolution maps, regime shifts could be observed annually at the centennial scale. They can also be used to understand how the Yellowstone ecosystem has gradually changed with its ecological legacies in the last century.


2021 ◽  
Author(s):  
Sarah Becker ◽  
Megan Maloney ◽  
Andrew Griffin

Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI). Previous testing focused on one study site using 2-m resolution commercial MSI from WorldView-2 and 30-m resolution imagery from Landsat-7. New testing in this work used 3-m imagery from PlanetScope and 10-m imagery from Sentinel-2 in imagery in sites across 12 biomes in South and Central America and North Korea. Overall accuracy ranged between 23% and 97% for Sentinel-2 imagery and between 51% and 98% for PlanetScope imagery. Future research will focus on automating the identification of the threshold that separates tree from other land covers, exploring use of the output for machine learning applications, and incorporating ancillary data such as digital surface models and existing tree cover maps.


Sign in / Sign up

Export Citation Format

Share Document