Photogrammetric Engineering and Remote Sensing
Overview
Photogrammetric Engineering and Remote Sensing is a renowned scientific journal that focuses on the fields of photogrammetry, remote sensing, and geospatial information science. It publishes cutting-edge research articles, technical notes, and reviews, covering topics such as image analysis, data acquisition, spatial modeling, and applications of remote sensing technologies. The journal serves as a valuable resource for professionals, researchers, and students interested in the advancements and applications of these disciplines.
Details
Details
Abbr.
Photogramm Eng Remote Sensing
Start
1975
End
Continuing
Frequency
Monthly
p-ISSN
0099-1112
Country
United States
Language
English
Metrics
Metrics
h-index / Ranks: 1564
143
SJR / Ranks: 11732
309
CiteScore / Ranks: 10255
2.20
JIF / Ranks: 6403
1.3
Recent Articles
1.
Gonsoroski E, Ahn Y, Harville E, Countess N, Lichtveld M, Pan K, et al.
Photogramm Eng Remote Sensing
. 2024 Mar;
89(7):437-443.
PMID: 38486939
Post-hurricane damage assessments are often costly and time-consuming. Remotely sensed data provides a complementary method of data collection that can be completed comparatively quickly and at relatively low cost. This...
2.
Liames J, Riegel J, Foley K, Lunetta R
Photogramm Eng Remote Sensing
. 2018 Sep;
83(4):293-306.
PMID: 30245536
This study details the development of a U.S. Commonwealth of Puerto Rico above-ground forest biomass (agb) product (baseline 2000) developed by the United States Environmental, Protection Agency (epa) that was...
3.
Lu D, Hetrick S, Moran E, Li G
Photogramm Eng Remote Sensing
. 2014 Oct;
78(7):747-755.
PMID: 25328256
A hierarchical-based classification method was designed to develop time series land-use/land-cover datasets from Landsat images between 1977 and 2008 in Lucas do Rio Verde, Mato Grosso, Brazil. A post-classification comparison...
4.
Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use
Toure S, Stow D, Weeks J, Kumar S
Photogramm Eng Remote Sensing
. 2014 Jan;
79(5).
PMID: 24403648
The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital...
5.
Omumbo J, Hay S, Goetz S, Snow R, Rogers D
Photogramm Eng Remote Sensing
. 2013 Jul;
68(2):161-166.
PMID: 23814324
Remotely sensed imagery has been used to update and improve the spatial resolution of malaria transmission intensity maps in Tanzania, Uganda, and Kenya. Discriminant analysis achieved statistically robust agreements between...
6.
Moran E
Photogramm Eng Remote Sensing
. 2011 Jun;
76(10):1159-1168.
PMID: 21643433
High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers,...
7.
Stow D, Lippitt C, Weeks J
Photogramm Eng Remote Sensing
. 2010 Aug;
76(8):907-914.
PMID: 20689664
The objective was to test GEographic Object-based Image Analysis (GEOBIA) techniques for delineating neighborhoods of Accra, Ghana using QuickBird multispectral imagery. Two approaches to aggregating census enumeration areas (EAs) based...
8.
Lu D, Batistella M, Moran E
Photogramm Eng Remote Sensing
. 2009 Oct;
74(4):421-430.
PMID: 19789721
Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic...
9.
Lu D, Batistella M, de Miranda E, Moran E
Photogramm Eng Remote Sensing
. 2009 Oct;
74(3):311-321.
PMID: 19789716
Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification...