*
 

iForest - Biogeosciences and Forestry

*

Remote sensing of selective logging in tropical forests: current state and future directions

Colbert M Jackson   , Elhadi Adam

iForest - Biogeosciences and Forestry, Volume 13, Issue 4, Pages 286-300 (2020)
doi: https://doi.org/10.3832/ifor3301-013
Published: Jul 10, 2020 - Copyright © 2020 SISEF

Review Papers


This paper reviews and discusses the status of remote sensing techniques applied in detecting and monitoring selective logging disturbance in tropical forests. The analyses concentrated on the period 1992-2019. Accurate and precise detection of selectively logged sites in a forest is crucial for analyzing the spatial distribution of forest disturbances and degradation. Remote sensing can be used to monitor selective logging activities and associated forest fires over tropical forests, which otherwise requires labor-intensive and time-consuming field surveys, that are costly and difficult to undertake. The number of studies on remote sensing for selective logging has grown steadily over the years, thus, the need for their review so as to guide forest management practices and current research. A variety of peer reviewed articles are discussed so as to evaluate the applicability and accuracy of different methods in different circumstances. Major challenges with existing approaches are singled out and future needs are discussed.

  Keywords


Tropical Forest Disturbance, Selective Logging, Forest Degradation, Forest Canopy Gaps, Disturbance Mapping, Remote Sensing, Forest Monitoring

Authors’ address

(1)
Colbert M Jackson
Elhadi Adam 0000-0003-3626-5839
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, Private Bag 3 Wits, 2050 (South Africa)

Corresponding author

 
Colbert M Jackson
mutisojackson@yahoo.com

Citation

Jackson CM, Adam E (2020). Remote sensing of selective logging in tropical forests: current state and future directions. iForest 13: 286-300. - doi: 10.3832/ifor3301-013

Academic Editor

Emanuele Lingua

Paper history

Received: Nov 25, 2019
Accepted: May 11, 2020

First online: Jul 10, 2020
Publication Date: Aug 31, 2020
Publication Time: 2.00 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 41106
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 33012
Abstract Page Views: 4011
PDF Downloads: 3599
Citation/Reference Downloads: 14
XML Downloads: 470

Web Metrics
Days since publication: 1613
Overall contacts: 41106
Avg. contacts per week: 178.39

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

Total number of cites (since 2020): 5
Average cites per year: 1.25

 

Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Andersen HE, Reutebuch SE, McGaughey RJ, D’Oliveira MV, Keller M (2014)
Monitoring selective logging in western Amazonia with repeat lidar flights. Remote Sensing of Environment 151: 157-165.
CrossRef | Gscholar
(2)
Antropov O, Rauste Y, Seifert FM, Häme T (2015)
Selective logging of tropical forests observed using L- and C-band SAR satellite data. In: Proceedings of the “IEEE International Geoscience and Remote Sensing Symposium (IGARSS)”. Milan (Italy) 26-31 July 2015. IEEE Institute of Electrical and Electronics Engineers, Piscataway, NJ, USA, pp. 3870-3873.
CrossRef | Gscholar
(3)
Anwar S, Stein A (2012)
Detection and spatial analysis of selective logging with geometrically corrected Landsat images. International Journal of Remote Sensing 33: 7820-7843.
CrossRef | Gscholar
(4)
Arroyo-Mora P, Kalacska M, Chazdon R (2009)
Spectral unmixing of forest canopy recovery in selectively logged units in a tropical lowland forest, Costa Rica. In: Anais “XIV Simpósio Brasileiro de Sensoriamento Remoto” [Annals XIV Brazilian Symposium on Remote Sensing]. Natal (Brasil) 25-30 Apr 2009. INPE, São José dos Campos, São Paulo, Brazil, pp. 2539-2546.
Gscholar
(5)
Asner GP, Keller M, Pereira R, Zweede JC (2002)
Remote sensing of selective logging in Amazonia: assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis. Remote Sensing of Environment 80: 483-496.
CrossRef | Gscholar
(6)
Asner GP, Keller M, Pereira R, Zweede JC, Silva J (2004)
Canopy damage and recovery after selective logging in Amazonia: field and satellite studies. Ecological Applications 14: 280-298.
CrossRef | Gscholar
(7)
Asner GP, Knapp DE, Broadbent EN, Oliveira PJC, Keller M, Silva JN (2005)
Selective logging in the Brazilian Amazon. Science 310: 480-482.
CrossRef | Gscholar
(8)
Asner GP, Broadbent EN, Oliveira PJC, Keller M, Knapp DE, Silva JNM (2006)
Condition and fate of logged forests in the Brazilian Amazon. Proceedings of the National Academy of Sciences USA 103: 12947-12950.
CrossRef | Gscholar
(9)
Asner GP, Hughes RF, Vitousek PM, Knapp DE, Kennedy-Bowdoin T, Boardman J (2008)
Invasive plants transform the three-dimensional structure of rain forests. Proceedings of the National Academy of Sciences USA 105: 4519-4523.
CrossRef | Gscholar
(10)
Baldauf T (2013)
Monitoring reduced emissions from deforestation and forest degradation (REDD+): capabilities of high-resolution active remote sensing. PhD thesis, Biology Department, University of Hamburg, Hamburg, Germany, pp. 94.
Gscholar
(11)
Banskota A, Kayastha N, Falkowski MJ, Wulder MA, Froese R, White JC (2014)
Forest monitoring using Landsat time-series data - A review. Canadian Journal of Remote Sensing 40: 362-384.
CrossRef | Gscholar
(12)
Broadbent EN, Zarin DJ, Asner GP, Peña-Claros M, Cooper A, Littell R (2006)
Recovery of forest structure and spectral properties after selective logging in lowland Bolivia. Ecological Applications 16 (3): 1148-1163.
CrossRef | Gscholar
(13)
Broadbent EN, Asner GP, Keller M, Knapp DE, Oliveira PJC, Silva JN (2008)
Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biological Conservation 141: 1745-1757.
CrossRef | Gscholar
(14)
Burivalova Z, Bauert MR, Hassold S, Fatroandrianjafinonjasolomiovazo NT, Koh LP (2015)
Relevance of global forest change data set to local conservation: case study of forest degradation in Masoala National Park, Madagascar. Biotropica 47: 267-274.
CrossRef | Gscholar
(15)
Chia EL, Hubert D, Enongene K, Tegegne YT (2020)
An AHP assessment of barriers in adopting sustainable forest management practices in the context of carbon emission reductions in Cameroon. Journal of Sustainable Forestry 39: 379-391.
CrossRef | Gscholar
(16)
Condé TM, Higuchi N, Lima AJN (2019)
Illegal selective logging and forest fires in the northern Brazilian Amazon. Forests 10: 61.
CrossRef | Gscholar
(17)
Costa OB, Matricardi EAT, Pedlowski MA, Miguel EP, Gaspar RO (2019)
Selective logging detection in the Brazilian Amazon. Floresta e Ambiente 26: e20170634.
CrossRef | Gscholar
(18)
Da Ponte E, Fleckenstein M, Leinenkugel P, Parker A, Oppelt N, Kuenzer C (2015)
Tropical forest cover dynamics for Latin America using Earth observation data: a review covering the continental, regional, and local scale. International Journal of Remote Sensing 36: 3196-3242.
CrossRef | Gscholar
(19)
Dalagnol R, Phillips OL, Gloor E, Galvão LS, Wagner FH, Locks CJ, Luiz EOC, Aragão LE (2019)
Quantifying canopy tree loss and gap recovery in tropical forests under low-intensity logging using VHR satellite imagery and airborne lidar. Remote Sensing 11: 817.
CrossRef | Gscholar
(20)
De Grandi EC, Mitchard E, Woodhouse IH, De Grandi GD (2015)
Spatial wavelet statistics of SAR backscatter for characterizing degraded forest: a case study from Cameroon. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8: 3572-3584.
CrossRef | Gscholar
(21)
De Wasseige C, Defourny P (2004)
Remote sensing of selective logging impact for tropical forest management. Forest Ecology and Management 188: 161-173.
CrossRef | Gscholar
(22)
Descals A, Szantoi Z, Beck P, Brink A, Strobl P (2017)
Automated detection of selective logging using Smallsat imagery. IEEE Geoscience and Remote Sensing Letters pp. 1-5.
CrossRef | Gscholar
(23)
Deutscher J, Perko R, Gutjahr K, Hirschmugl M, Schardt M (2013)
Mapping tropical rainforest canopy disturbances in 3D by COSMO-SkyMed spotlight InSAR-Stereo data to detect areas of forest degradation. Remote Sensing 5: 648-663.
CrossRef | Gscholar
(24)
D’Oliveira MVN, Reutebuch SE, McGaughey RJ, Andersen H-E (2012)
Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, western Brazilian Amazon. Remote Sensing of Environment 124: 479-491.
CrossRef | Gscholar
(25)
Edwards DP, Tobias J, Sheil D, Meijaard E, Laurance WF (2014)
Maintaining ecosystem function and services in logged tropical forests. Trends in Ecology and Evolution 29: 511-520.
CrossRef | Gscholar
(26)
Ellis P, Griscom B, Walker W, Gonçalves F, Cormier T (2016)
Mapping selective logging impacts in Borneo with GPS and airborne lidar. Forest Ecology and Management 365: 184-196.
CrossRef | Gscholar
(27)
Englhart S, Jubanski J, Siegert F (2013)
Quantifying dynamics in tropical peat swamp forest biomass with multi-temporal lidar datasets. Remote Sensing 5: 2368-2388.
CrossRef | Gscholar
(28)
Franke J, Navratil P, Keuck V, Peterson K, Siegert F (2012)
Monitoring fire and selective logging activities in tropical peat swamp forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5: 1811-1820.
CrossRef | Gscholar
(29)
Furusawa T, Pahari K, Umezaki M, Ohtsuka R (2004)
Impacts of selective logging on New Georgia Island, Solomon Islands evaluated using very-high-resolution satellite (IKONOS) data. Environmental Conservation 31: 349-355.
CrossRef | Gscholar
(30)
Gibson L, Lee TM, Koh LP, Brooks BW, Gardner TA, Barlow J, Peres CA, Bradshaw CJA, Laurance WF, Lovejoy TE, Sodhi NS (2011)
Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478: 378-381.
CrossRef | Gscholar
(31)
Graça PMLA, Maldonado FD, Santos JR, Keizer EWH (2015)
Multi-temporal analysis of radiometric changes in satellite images of forest edges to infer selective-logging areas in the Amazon forest. Acta Amazonica 45: 35-44.
CrossRef | Gscholar
(32)
Grecchi RC, Beuchle R, Shimabukuro YE, Aragão LEOC, Arai E, Simonetti D, Achard F (2017)
An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: a case study in northern Mato Grosso, Brazilian Amazon. International Journal of Applied Earth Observation and Geoinformation 61: 70-80.
CrossRef | Gscholar
(33)
Guitet S, Pithon S, Brunaux O, Jubelin G, Gond V (2012)
Impacts of logging on the canopy and the consequences for forest management in French Guiana. Forest Ecology and Management 277: 124-131.
CrossRef | Gscholar
(34)
Hamunyela E, Verbesselt J, Herold M (2016)
Using spatial context to improve early detection of deforestation from Landsat time series. Remote Sensing of Environment 172: 126-138.
CrossRef | Gscholar
(35)
Heiskanen J, Korhonen L, Hietanen J, Pellikka PKE (2015)
Use of airborne lidar for estimating canopy gap fraction and leaf area index of tropical montane forests. International Journal of Remote Sensing 36: 2569-2583.
CrossRef | Gscholar
(36)
Hernández-Gómez IU, Cerdan-Cabrera CR, Navarro-Martínez A, Vazquez-Luna D, Armenta-Montero S, Ellis EA (2019)
Assessment of the CLASlite forest monitoring system in detecting disturbance from selective logging in the Selva Maya, Mexico. Silva Fennica 53: 1.
CrossRef | Gscholar
(37)
Hethcoat MG, Edwards DP, Carreiras JMB, Bryant RG, França FM, Quegan S (2018)
A machine learning approach to map tropical selective logging. Remote Sensing of Environment 221: 569-582.
CrossRef | Gscholar
(38)
Hirschmugl MG, Steinegger M, Gallaun H, Schardt M (2014)
Mapping forest degradation due to selective logging by means of time series analysis: case studies in Central Africa. Remote Sensing 6: 756-775.
CrossRef | Gscholar
(39)
Hirschmugl MG, Gallaun H, Dees M, Datta P, Deutscher J, Koutsias N, Schardt M (2017)
Methods for mapping forest disturbance and degradation from optical earth observation data: a review. Current Forestry Reports 3: 32-45.
CrossRef | Gscholar
(40)
Kankeu RS, Sonwa DJ, Atyi R, Nkal N (2016)
Quantifying post logging biomass loss using satellite images and ground measurements in Southeast Cameroon. Journal of Forestry Research 27.
CrossRef | Gscholar
(41)
Karlson M, Ostwald M (2015)
Remote sensing of vegetation in the Sudano-Sahelian zone: a literature review from 1975 to 2014. Journal of Arid Environments 124: 257-269.
CrossRef | Gscholar
(42)
Kent R, Lindsell J, Laurin G, Valentini R, Coomes D (2015)
Airborne lidar detects selectively logged tropical forest even in an advanced stage of recovery. Remote Sensing 7: 8348-8367.
CrossRef | Gscholar
(43)
Kleinschroth F, Healey JR, Sist P, Mortier F, Gourlet-Fleury S (2016)
How persistent are the impacts of logging roads on Central African forest vegetation? Journal of Applied Ecology 53 (4): 1127-1137.
CrossRef | Gscholar
(44)
Koltunov A, Ustin SL, Asner GP, Fung I (2009)
Selective logging changes forest phenology in the Brazilian Amazon: evidence from MODIS images time series analysis. Remote Sensing of Environment 113: 2431-2440.
CrossRef | Gscholar
(45)
Kuemmerle T, Erb K-H, Meyfroidt P, Müller D, Verburg P, Estel S, Haberl H, Hostert P, Jepsen M, Kastner T, Levers C, Lindner M, Plutzar C, Verkerk H, Zanden E, Reenberg A (2013)
Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environmental Sustainability 5: 484-493.
CrossRef | Gscholar
(46)
Langner A, Miettinen J, Kukkonen M, Vancutsem C, Simonetti D, Vieilledent G, Verhegghen A, Gallego J, Stibig H-J (2018)
Towards operational monitoring of forest canopy disturbance in evergreen rain forests: a test case in continental Southeast Asia. Remote Sensing 10: 544.
CrossRef | Gscholar
(47)
Laporte NT, Lin TS (2003)
Monitoring logging in the tropical forest of Republic of Congo with Landsat imagery. In: Proceedings of the “IEEE International Geoscience and Remote Sensing Symposium (IGARSS)”. Toulouse (France) 21-25 July 2003. IEEE Institute of Electrical and Electronics Engineers, Piscataway, NJ, USA, pp. 2565-2567.
CrossRef | Gscholar
(48)
Lei Y, Treuhaft R, Keller M, Dos-Santos M, Gonçalves F, Neumann M (2018)
Quantification of selective logging in tropical forest with spaceborne SAR Interferometry. Remote Sensing of Environment 211: 167-183.
CrossRef | Gscholar
(49)
UW Madison Labraries (2019)
Top 10 Databases. University of Wisconsin-Madison Libraries, WI, USA, web site.
Online | Gscholar
(50)
Lima TA, Beuchle R, Langner A, Grecchi R, Griess VC, Achard F (2019)
Comparing Sentinel-2 MSI and Landsat 8 OLI imagery for monitoring selective logging in the Brazilian Amazon. Remote Sensing 11: 961.
CrossRef | Gscholar
(51)
Matricardi EAT, Skole DL, Cochrane MA, Qi J, Chomentowski W (2005)
Monitoring selective logging in tropical evergreen forests using Landsat: multitemporal regional analyses in Mato Grosso, Brazil. Earth Interactions 9: 1-24.
CrossRef | Gscholar
(52)
Matricardi EAT, Skole DL, Cochrane MA, Pedlowski M, Chomentowski W (2007)
Multi-temporal assessment of selective logging in the Brazilian Amazon using Landsat data. International Journal of Remote Sensing 28: 63-82.
CrossRef | Gscholar
(53)
Matricardi EAT, Skole DL, Pedlowski MA, Chomentowski W, Fernandes LC (2010)
Assessment of tropical forest degradation by selective logging and fire using Landsat imagery. Remote Sensing of Environment 114: 1117-1129.
CrossRef | Gscholar
(54)
Matricardi EAT, Skole DL, Pedlowski MA, Chemontowski W (2013)
Assessment of forest disturbances by selective logging and forest fires in the Brazilian Amazon using Landsat data. International Journal of Remote Sensing 34: 1057-1086.
CrossRef | Gscholar
(55)
Melendy L, Hagen SC, Sullivan FB, Pearson TRH, Walker SM, Ellis P, Kustiyo Sambodo AK, Roswintiarti O, Hanson MA, Klassen AW, Palace MW, Braswell BH, Delgado GM (2018)
Automated method for measuring the extent of selective logging damage with airborne lidar data. ISPRS Journal of Photogrammetry and Remote Sensing 139: 228-240.
CrossRef | Gscholar
(56)
Mermoz S, Réjou-Méchain M, Villard L, Le Toan T, Rossi V, Gourlet-Fleury S (2015)
Decrease of L-band SAR backscatter with biomass of dense forests. Remote Sensing of Environment 159: 307-317.
CrossRef | Gscholar
(57)
Miettinen J, Stibig HJ, Achard F (2014)
Remote sensing of forest degradation in Southeast Asia: aiming for a regional view through 5-30 m satellite data. Global Ecology and Conservation 2: 24-36.
CrossRef | Gscholar
(58)
Mitchard ETA (2016)
A review of earth observation methods for detecting and measuring forest change in the tropics. Ecometrica, Edinburgh, UK, pp. 2-4.
Gscholar
(59)
Mitchell AL, Rosenqvist A, Mora B (2017)
Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+. Carbon Balance Management 12: 9.
CrossRef | Gscholar
(60)
Monteiro A, Souza CM, Barreto P (2003)
Detection of logging in Amazonian transition forests using spectral mixture models. International Journal of Remote Sensing 24: 151-159.
CrossRef | Gscholar
(61)
Morales-Barquero L, Lyons MB, Phinn SR, Roelfsema CM (2019)
Trends in remote sensing accuracy assessment approaches in the context of natural resources. Remote Sensing 11: 2305.
CrossRef | Gscholar
(62)
Neba GS, Kanninen M, Atyi R, Sonwa D (2014)
Assessment and prediction of above-ground biomass in selectively logged forest concessions using field measurements and remote sensing data: case study in South East Cameroon. Forest Ecology and Management 329: 177-185.
CrossRef | Gscholar
(63)
Nellemann C (2012)
Green carbon, black trade. Illegal logging, tax fraud and laundering in the World’s tropical forests. A rapid response assessment. INTERPOL Environmental Crime Programme, Arendal , United Nations Environment Programme and GRID-Arendal, Birkeland Trykkeri AS, Spelefjellveien 1, 4760 Birkeland, Norway, pp. 1.
Online | Gscholar
(64)
Olofsson P, Giles MF, Herold M, Stehman S, Curtis EW, Wulder M (2014)
Good practices for assessing accuracy and estimating area of land change. Remote Sensing of Environment. 148: 42-57.
CrossRef | Gscholar
(65)
Ota T, Ahmed OS, Minn ST, Khai TC, Mizoue N, Yoshida S (2019)
Estimating selective logging impacts on aboveground biomass in tropical forests using digital aerial photography obtained before and after a logging event from an unmanned aerial vehicle. Forest Ecology and Management 433: 162-169.
CrossRef | Gscholar
(66)
Pinagé ER, Matricardi EAT, Leal FA, Pedlowski MA (2016)
Estimates of selective logging impacts in tropical forest canopy cover using RapidEye imagery and field data. iForest 9: 461-468.
CrossRef | Gscholar
(67)
Pinagé ER, Keller M, Duffy P, Longo M, Dos-Santos MN, Morton DC (2019)
Long-term impacts of selective logging on Amazon forest dynamics from multi-temporal airborne lidar. Remote Sensing 11: 709.
CrossRef | Gscholar
(68)
Pithon S, Jubelin G, Guitet S, Gond V (2013)
A statistical method for detecting logging-related canopy gaps using high-resolution optical remote sensing. International Journal of Remote Sensing 34: 700-711.
CrossRef | Gscholar
(69)
Poudyal BH, Maraseni T, Cockfield G (2018)
Evolutionary dynamics of selective logging in the tropics: a systematic review of impact studies and their effectiveness in sustainable forest management. Forest Ecology and Management 430: 166-175.
CrossRef | Gscholar
(70)
Qi J, Wang C, Matricardi E, Skole D (2002)
Improved selective logging detection with Landsat images in tropical regions. In: Proceedings of the “IEEE International Geoscience and Remote Sensing Symposium (IGARSS)”. Toronto (Ontario, Canada) 24-28 June 2002. IEEE Institute of Electrical and Electronics Engineers, Piscataway, NJ, USA, pp. 2078-2080.
CrossRef | Gscholar
(71)
Qu Y, Shaker A, Silva AC, Klauberg C, Pinagé ER (2018)
Remote sensing of leaf area index from lidar height percentile metrics and comparison with MODIS product in a selectively logged tropical forest area in eastern Amazonia. Remote Sensing 10: 970.
CrossRef | Gscholar
(72)
Rahm M, Van Wolvelaer J, Vrieling A, Mertens B (2013)
Detecting forest degradation in the Congo Basin by optical remote sensing. In: Proceedings of the “ESA Living Planet Symposium”. Edinburgh (Scotland, UK) 9-13 Sept 2013. ESA, Noordwijk, The Netherlands, pp. 19.
Gscholar
(73)
Rauste R, Antropov O, Häme T, Ramminger G, Gomez S, Seifert FM (2013)
Mapping selective logging in tropical forest with space-borne SAR data. In: Proceedings of the “ESA Living Planet Symposium”. Edinburgh (UK) 9-13 Sept 2013. ESA, Noordwijk, The Netherlands, pp. 168-173.
Gscholar
(74)
Read JM, Clark DB, Venticinque EM, Moreira MP (2003)
Application of merged 1-m and 4-m resolution satellite data to research and management in tropical forests. Journal of Applied Ecology 40: 592-600.
CrossRef | Gscholar
(75)
Senf C, Wulder M, Campbell E, Hostert P (2016)
Using Landsat to assess the relationship between spatiotemporal patterns of western spruce budworm outbreaks and regional-scale weather variability. Canadian Journal of Remote Sensing 42: 706-718.
CrossRef | Gscholar
(76)
Shimabukuro YE, Beuchle R, Grecchi RC, Achard F (2014)
Assessment of forest degradation in Brazilian Amazon due to selective logging and fires using time series of fraction images derived from Landsat ETM+ images. Remote Sensing Letters 5: 773-782.
CrossRef | Gscholar
(77)
Shimizu K, Ahmed OS, Ponce-Hernandez R, Ota T, Win ZC, Mizoue N, Yoshida S (2017)
Attribution of disturbance agents to forest change using a Landsat time series in tropical seasonal forests in the Bago Mountains, Myanmar. Forests 8: 218.
CrossRef | Gscholar
(78)
Sofan P, Vetrita Y, Yulianto F, Khomarudin MR (2016)
Multi-temporal remote sensing data and spectral indices analysis for detection tropical rainforest degradation: case study in Kapuas Hulu and Sintang districts, west Kalimantan, Indonesia. Natural Hazards 80: 1279-1301.
CrossRef | Gscholar
(79)
Solberg R, Malnes E, Amlien J, Danks F, Haarpaintner J, Hgda K-A, Johansen BE, Karlsen SR, Koren H (2008)
State of the art for tropical forest monitoring by remote sensing: a review carried out for the Ministry for the Environment of Norway and the Norwegian Space Centre. Norwegian Computing Centre, Oslo, Norway, pp. 11.
Gscholar
(80)
Souza CM, Barreto P (2000)
An alternative approach for detecting and monitoring selectively logged forests in the Amazon. International Journal of Remote Sensing 21: 173-179.
CrossRef | Gscholar
(81)
Souza CM, Firestone L, Silva LM, Roberts D (2003)
Mapping forest degradation in the eastern Amazon from SPOT 4 through spectral mixture models. Remote Sensing of Environment 87: 494-506.
CrossRef | Gscholar
(82)
Souza CM, Roberts D (2005)
Mapping forest degradation in the Amazon region with Ikonos images. International Journal of Remote Sensing 26 (3): 425-429.
CrossRef | Gscholar
(83)
Souza CM, Roberts D, Cochrane MA (2005)
Combining spectral and spatial information to map canopy damage from selective logging and forest fires. Remote Sensing of Environment 98: 329-343.
CrossRef | Gscholar
(84)
Souza CM, Cochrane MA, Sales M, Monteiro AL, Mollicone D (2009)
Integrating forest transects and remote sensing data to quantify carbon loss due to forest degradation in the Brazilian Amazon. In: Proceedings of the Workshop “Assessment and monitoring of forest degradation”. Rome (Italy) 8-10 Sept 2009. Forestry Department, Food and Agriculture Organization of the United Nations, Rome, Italy, pp. 1-17.
Gscholar
(85)
Souza CM, Siqueira JV, Sales MH, Fonseca AV, Ribeiro JG, Numata I, Cochrane MA, Barber CP, Roberts DA, Barlow J (2013)
Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sensing 5: 5493-5513.
CrossRef | Gscholar
(86)
Stone TA, Lefebvre P (1998)
Using multi-temporal satellite data to evaluate selective logging in Pará, Brazil. International Journal of Remote Sensing 19: 2517-2526.
CrossRef | Gscholar
(87)
Tangki H, Chappell NA (2008)
Biomass variation across selectively logged forest within a 225-km2 region of Borneo and its prediction by Landsat TM. Forest Ecology and Management 256: 1960-1970.
CrossRef | Gscholar
(88)
Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010)
Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment 114: 106-115.
CrossRef | Gscholar
(89)
Verhegghen A, Eva H, Achard F (2015)
Assessing forest degradation from selective logging using time series of fine spatial resolution imagery in Republic of Congo. In: Proceedings of the “IEEE International Geoscience and Remote Sensing Symposium (IGARSS)”. Milan (Italy) 26-31 July 2015. IEEE Institute of Electrical and Electronics Engineers, Piscataway, NJ, USA, pp. 2044-2047.
CrossRef | Gscholar
(90)
Wang C, Qi J, Cochrane M (2005)
Assessment of tropical forest degradation with canopy fractional cover from Landsat ETM+ and IKONOS imagery. Earth Interactions 9: 1-18.
CrossRef | Gscholar
(91)
Wang Y, Ziv G, Adami M, Mitchard E, Batterman SA, Buermann W, Marimon BS, Marimon-Junior BH, Reis S, Rodrigues D, Galbraith D (2018)
Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery. Remote Sensing of Environment 221: 474-488.
CrossRef | Gscholar
(92)
Watrin OS, Rocha AMA (1992)
Levantamento da vegetação natural e do uso da terra no município de Paragominas (PA) utilizando imagens TM/Landsat [Survey of natural vegetation and land use in the municipality of Paragominas (PA) using TM/Landsat]. Boletim de Pesquisa 124, EMBRAPA/CPATU, Bele´m, PA, Brazil, pp. 40.
Gscholar
(93)
Wedeux BMM, Coomes DA (2015)
Landscape-scale changes in forest canopy structure across a partially logged tropical peat swamp. Biogeosciences 12: 6707-6719.
CrossRef | Gscholar
(94)
Whittle M, Quegan S, Uryu Y, Stüewe M, Yulianto K (2012)
Detection of tropical deforestation using ALOS-PALSAR: a Sumatran case study. Remote Sensing of Environment 124: 83-98.
CrossRef | Gscholar
(95)
Win RN, Suzuki R, Takeda S (2012)
Remote sensing analysis of forest damage by selection logging in the Kabaung Reserved Forest, Bago Mountains, Myanmar. Journal of Forest Research 17: 121-128.
CrossRef | Gscholar
(96)
Wulder MA, White JC, Coops NC, Butson CR (2008)
Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sensing of Environment 112: 2729-2740.
CrossRef | Gscholar
 

This website uses cookies to ensure you get the best experience on our website. More info