Pathway and method of forest health assessment using remote sensing technology
-
摘要: 近年来，随着气候变化和人类活动的加剧，全球森林面积持续减少、质量下降，生态环境事件频发，森林健康的问题受到前所未有的关注，已经成为生态文明战略的重要组成部分。我国森林资源持续增长，但同时也面临一些问题，如：造林结构单一、林分质量不高、生态稳定性弱等，如何系统准确评价其森林健康状况仍然是当前难点。相对于传统地面调查方法，遥感技术具有实时获取、重复监测以及多时空尺度等优势，并且随着高分遥感和人工智能等技术的飞速发展，大幅提高了解决森林健康评价难题的能力。为系统评估新型遥感技术应用于评价森林健康的潜力，本文在文献分析基础上，指出了现有路径和方法，具体包括：（1）通过文献计量学方法分析并明确了森林健康评价的4大核心内容（活力、组织结构、抵抗力和恢复力）和4大关键问题（树种分类、林木活力、林业有害生物、干旱胁迫）。（2）从不同尺度（单木−林分−生态系统−景观）、不同平台（近地面遥感−航空遥感−卫星遥感）和不同传感器（包括RGB相机、多/高光谱相机、激光雷达、热红外相机、微波雷达和叶绿素荧光扫描仪）3个角度，系统梳理现有遥感技术的优缺点。（3）围绕4个关键问题，阐述近年来应用遥感技术评价森林健康的路径和方法。进一步，本文指出了包括多数据源融合分析、森林健康监测网络与近地面遥感、森林健康大数据应用在内的挑战与机遇，以期为我国森林资源智慧管理提供参考。Abstract: In recent years, with the aggravation of climate change and human activities, the global forest area continues to reduce, forest quality keeps declining, and the ecological and environmental events occur frequently. Thus, forest health issues have received unprecedented attention, and have become an important part of the ecological civilization strategy. China’s forest resources continue to grow, but it also faces some problems, such as single afforestation structure, low stand quality and weak ecological stability. How to evaluate forest health systematically and accurately is still a difficult problem. Compared with the traditional ground survey methods, remote sensing technology has the advantages of macroscopic, timeliness and economic efficiency. With the rapid development of high-resolution remote sensing and artificial intelligence technology, it is possible to overcome the problem of forest health assessment. In order to systematically evaluate the potential of new remote sensing technology, this paper points out the existing paths and methods on the basis of literature analysis, including: (1) through bibliometric analysis, four core contents of forest health assessment (vitality, organizational structure, resistance and resilience) and four key issues (tree species classification, forest vitality, forest pests, drought threat) were identified. (2) Systematically interpreting the advantages and disadvantages of existing remote sensing technologies from three angles, namely, different scales (single tree stand ecosystem landscape), different platforms (near ground remote sensing, aerial remote sensing satellite remote sensing) and different sensors (including RGB cameras, multi/hyperspectral cameras, lidar, thermal infrared cameras, microwave radars and chlorophyll fluorescence scanners). (3) Focusing on four key issues, this paper expounds the application path and method of remote sensing technology to evaluate forest health in recent years. Furthermore, this paper points out the challenges and opportunities, including multi-source fusion analysis, forest health monitoring network and near ground remote sensing, forest health big data application, in order to provide reference for the intelligent management of forest resources in China.
-
Keywords:
- forest health /
- remote sensing /
- sensor /
- VOR system /
- forestry pest
-
日本结缕草（Zoysia japonica）作为暖季性草坪草，具有耐践踏、耐粗放管理、节水等优良特性，被广泛应用[1]。但是由于日本结缕草在北方地区秋冬季节较早的出现枯黄现象，观赏价值受到了严重影响，在北方的使用受到了限制。温度是植物生长的重要因素。低温胁迫对植物生长发育产生抑制作用，加快衰老，影响各项生理指标，甚至导致植物死亡。在紫花苜蓿（Medicago sativa）根系呼吸的研究中发现，4 ℃的低温条件下，活性氧的大量积累对细胞造成了伤害，严重影响了根系的生长[2]。同时，在紫花苜蓿种子萌发的研究中发现，10 ℃低温条件下发芽率下降，长期低温胁迫出现幼苗死亡[3]。另外，植物响应低温胁迫产生一系列防御反应，例如抗氧化酶活性提升，渗透调节物质增加等。有结果显示，低温会影响扁蓿豆（Melissilus ruthenicus）抗氧化酶活性和渗透调节物质含量[4-5]。刘建秀老师课题组在日本结缕草抗寒性方面做了大量研究，其中在短期低温胁迫模拟试验中，通过转录组数据分析发现低温会诱导植物脱水和氧化反应，抑制光合作用和物质运输[6]。因此，低温可能是造成植物叶片褪绿的重要原因。近年来对叶绿素代谢的研究越来越丰富，例如，在小麦（Triticum aestivum）的研究中发现2-Cys过氧化还原蛋白通过影响叶绿素代谢从而适应热胁迫[7]；叶绿素分解相关基因的表达量与黑麦草（Lolium perenne）耐热性具有正相关性[8]；叶绿素合成相关基因过表达，可通过提高5-氨基乙酰丙酸(5-aminolevulinic acid，5-ALA)，增加玉簪（Hosta plantaginea）、烟草（Nicotiana tabacum）的叶绿素含量，从而改变叶色[9]。本研究从叶绿素研究角度出发，进行长时间的低温胁迫处理，通过对日本结缕草叶绿素代谢通路关键酶活性、基因表达量的测定，解释低温胁迫导致日本结缕草叶色变化的内源机理。
1. 材料与方法
本试验以日本结缕草“Chinese Common”为试验材料，采自南京农业大学白马科研基地。经分株、移栽、扩繁、养护管理，之后挑选出10盆长势较好且均一的日本结缕草，分为两组（自然生长组，低温处理组，各5盆）。试验分别在人工气候箱和低温培养箱进行52 d的自然生长处理和低温胁迫处理，当两个处理组叶片表型出现明显差异时停止处理。自然生长组（NT）在14 h光照（17600 lx，32 ℃，70% 相对湿度），10 h黑暗（28 ℃，70% 相对湿度）的循环条件下生长。低温处理组（CT）在14 h光照（17600 lx，6 ℃，70% 相对湿度），10 h黑暗（4 ℃，70% 相对湿度）的循环条件下生长。分别对两个处理组处理0 d和52 d的日本结缕草叶片进行采样，测定其叶绿素含量、单卟啉胆色素原（PBG）含量、ALAD活性、脱镁鳌合酶（MDCase）、红色叶绿素代谢产物还原酶（RCCR）、脱镁叶绿酸a加氧酶（PAO）、脱镁叶绿素酶（PPH）、叶绿素酶（CHLase）、PBGD活性以及叶绿素合成与降解相关基因表达量。
1.1 叶绿素含量
利用乙醇提取法[10]，使用微量分光光度计等实验仪器，对665、649、470 nm波长处的吸光度进行测定，根据以下公式计算[11]：
Ca=13.95A665−6.88A649 (1) Cb=24.96A649−7.32A665 (2) CL=Ca+Cb=6.63A665+18.08A649 (3) CChl=CL⋅V/m (4) 式中：Ca表示叶绿素a含量；Cb表示叶绿素b含量；CL表示叶绿素总含量；CChl表示叶绿素含量；V表示提取后体积；m表示称取叶片质量。
1.2 PBG含量
参照Bogorad[12]的方法进行测定，其中PBG含量的摩尔消光系数（MEC） = 6.1 × 104 L/（mol·cm）。
1.3 ALAD活性
参照Mauzerall[13]的方法，以不同样品中将δ-氨基乙酰丙 (ALA)转化为PBG含量的多少来表示ALAD活性，需保证向不同样品中加入的ALA等量且过量，活性单位为nmol /（mg·h），蛋白质浓度用布拉德福德方法定量，酶活性以每单位蛋白质为基础表达。
1.4 MDCase、RCCR、PAO、PPH、CHLase、PBGD活性
根据酶联免疫法使用ELISA试剂盒，由上海江莱生物科技有限公司进行测定，对所测定酶活性进行标准曲线矫正。根据双抗体夹心法，使用Rayto RT-6100酶标仪测定各样品450 nm处的吸光度（OD 值）并根据标准曲线计算相应的酶的含量。在本试验中，MDCase标准曲线为y = 67.792x − 6.617 7，R2 = 0.998 6；RCCR标准曲线为y = 690.68x − 77.746，R2 = 0.994 9；PAO标准曲线为y = 145.27x − 15.807，R2 = 0.995 3；PPH标准曲线为y = 119.76x − 9.375 5，R2 = 0.996；CHLase标准曲线为y = 504.14x − 49.954，R2 = 0.995 3；PBGD标准曲线为y = 45.704x − 5.320 9，R2 = 0.995 8。
1.5 基因表达量
使用TaKaRa MiniBEST Plant RNA Extraction Kit[14]试剂盒提取日本结缕草Total RNA。使用PrimeScriptTMRT reagent Kit with gDNA Eraser试剂盒[15]进行cDNA的合成，同时去除基因组DNA。根据Illumina HiSeq 测序的结果（由北京诺禾致源生物有限公司完成），由北京睿博兴科生物技术有限公司设计合成引物，引物序列如表1，使用TaKaRa公司TB GreenTM Premix Ex TaqTM（Tli RNaseH Plus）[16]试剂盒进行RT-PCR。基因表达量结果主要由两个处理组52 d处理后相对于各自处理组处理0 d的表达量进行比较，相对基因表达量计算结果大于1表示基因上调表达，小于1表示基因下调表达。
表 1 目的基因及引物序列Table 1. Target gene and primer sequence目的基因 Target gene 引物序列 Primer sequence Actin-F 5′-GGTGTTATGGTTGGGATGG-3′ Actin-R 5′-CAGTGAGCAGGACAGGGTG-3′ ZjALAD-F 5′-CAGCAGATGAGGCAGAAG-3′ ZjALAD-R 5′-GGCGAAGTAGGTCAGGAT-3′ ZjPBGD-F 5′-GAGTTCTTGGCAGTTCTT-3′ ZjPBGD-R 5′-GCTCCACTTCTTGATGTT-3‘ ZjHEMD-F 5′-TTGAAGGGTGGGTTGAGAGT-3′ ZjHEMD-R 5′-ACATACTAGTAGCTCCATCG-3′ ZjUro-D1-F 5′-GAAGGACACAGGAGTTGATG-3′ ZjUro-D1-R 5′-CCATTACCAAGGCGTCTC-3′ ZjUro-D2-F 5′-ATCCTTACACCACTTCCT-3′ ZjUro-D2-R 5′-CTCATCTCGCAACACATT-3′ ZjCPOX-F 5′-ATCATAGAACGGCGGAAG-3′ ZjCPOX-R 5′-GCAGTAAGTGGAAGAGACA-3′ ZjMgCH1-F 5′-GAACATCCACGCTCTTGA-3′ ZjMgCH1-R 5′-ACTCGCCATAGGTCTTGA-3′ ZjMgCH2-F 5′-CTACCACGCTCAATCCTAT-3′ ZjMgCH2-R 5′-GTCATCCTCCTCTTCATCTT-3′ ZjMgCH3-F 5′-TCTGAGTTGAATGTCGATGG-3′ ZjMgCH2-R 5′-TGACGGTAGCAATATCCTCC-3′ ZjCHLM-F 5′-GGAGAGCCTGAACGGGAGGT-3′ ZjCHLM-R 5′-GGGCGCGAAGCTGATCACCA-3′ ZjNADPH1-F 5′-GAGTCGGCGTTCTTGGGTGT-3′ ZjNADPH1-R 5′-GTCTGCGCACGGATAGCCAC-3′ ZjNADPH2-F 5′-CGCCGTTCCAGAAGTTCATA-3′ ZjNADPH2-R 5′-GAGTCCTTGTTCCAGCTCCA-3′ ZjOsCAO-F F:5′-GCAGCAAGAACGAATGAT-3′ ZjOsCAO-R 5′-CTGAAGAGTGGAAGGAGAA-3′ ZjHCAR-F 5′-CATAACTGGAAGCACAAGAG-3′ ZjHCAR-R 5′-GAGGACAGATTCAGGACTT-3′ ZjNYC1-F 5′-GACACAGCATCCTCAGTA-3′ ZjNYC1-R 5′-ATAGTCCAGCGAGTAACG-3′ ZjCHLase-F 5′-AAATCTGGCTGGTCTGGTCG-3′ ZjCHLase-R 5′-TATCTGAGATCACGCCTGCT-3′ ZjPAO-F 5′-CAGATACGAGCAGCATAC-3′ ZjPAO-R 5′-CAGCAGCACCTAACAATA-3′ ZjRCCR-F 5′-TCACCAACAGCAATCTCA-3′ ZjRCCR-R 5′-TATCTCAGTCTCCTCCATCT-3′ 1.6 数据统计与分析
使用Excel整理数据，SPSS 20进行单因素显著性方差分析，Origin 2018绘制图表。
2. 结果与分析
2.1 形态变化
两组处理的植株，经52 d处理生长形态（如图1）具有不同程度的变化。NT组略有衰老枯黄现象。CT组植株叶片出现严重枯黄且卷曲。
![]()
图 1 处理前后不同处理组间的形态变化NT.自然生长组；CT.低温处理组。 下同。NT, natural growth group; CT, low temperature growth group. The same below.Figure 1. Morphological changes between different treatment groups before and after treatment2.2 叶绿素的变化
由图2a可知，NT组52 d叶绿素含量变化不显著；CT组52 d后，叶绿素含量极显著下降（P < 0.01），由5.38 mg/g下降至2.01 mg/g，下降幅度为62.64%。由图2b、c可知，NT组52 d后，叶绿素a和叶绿素b含量没有显著变化；CT组52 d后，叶绿素a和叶绿素b的含量均显著（P < 0.05）下降，叶绿素a由3.08 mg/g下降至0.80 mg/g；叶绿素b由2.29 mg/g下降至1.21 mg/g，叶绿素a下降幅度大于叶绿素b分别为74.06%和47.30%。叶绿素a/b的值（图2d）显著（P < 0.05）下降，下降幅度为50.77%，因此，日本结缕草在受到低温胁迫时叶绿素a的降解速度大于叶绿素b。
![]()
图 2 处理前后不同处理组的叶绿素含量变化不同小写字母表示相同处理组处理前后差异显著（P＜0.05）。下同。Different lowercase letters indicate significant difference between before and after treatment in the same treatment group (P＜0.05). The same below.Figure 2. Changes of chlorophyll content between different treatment groups before and after treatment2.3 叶绿素合成关键酶的变化
由图3a可知，NT组52 d后ALAD活性略有上升，但变化不显著；CT组52 d后ALAD活性显著（P < 0.05）下降，由3.20 nmol/（mg·h）下降至2.32 nmol/（mg·h）。由图3b可知，NT组52 d后PBGD活性显著（P < 0.05）上升 ，由0.51 U/g上升至0.63 U/g；CT组52 d后PBGD活性略有上升但变化不显著。
![]()
图 3 处理前后不同处理组的ALAD、PBGD活性Figure 3. ALAD and PBGD activities among different treatment groups before and after treatment2.4 叶绿素合成途径关键物质的变化
由图4可知，NT组52 d后PBG含量显著（P < 0.05）下降，下降了9.10 µmol/g；CT组52 d后PBG含量也显著（P < 0.05）下降，下降了13.00 µmol/g，下降更显著。
![]()
图 4 处理前后不同处理组的PBG含量Figure 4. PBG contents in different treatment groups before and after treatment2.5 叶绿素降解途径关键酶的变化
由图5a可知，NT组52 d后，CHLase活性显著（P < 0.05）上升，上升了167.04 U/g；低温处理组52 d后，CHLase活性变化不显著。由图5b可知，NT组52 d后，MDase活性变化不显著；CT组52 d后，MDase活性显著上升（P < 0.05），上升了0.11 U/g。由图5c可知，自然生长组52 d后，PPH活性显著（P < 0.05）升高，升高了0.14 U/g；低温处理组52 d后，PPH活性没有显著变化。由图5d可知，52 d后，NT组与CT组PAO活性均略有上升且变化不显著。由图5e可知，自然生长组52 d后，RCCR活性显著（P < 0.05）升高，升高37%；低温处理组52 d后，RCCR活性显著（P < 0.05）升高，升高幅度为19.19%。因此，日本结缕草在低温条件下褪绿反应加速进行。
![]()
图 5 处理前后不同处理组的叶绿素降解酶活性Figure 5. Chlorophyll degrading enzyme activity in different treatment groups before and after treatment2.6 叶绿素代谢相关基因表达量的变化
2.6.1 提取总RNA质量浓度的检测
经超微量分光光度计的检测，前期提取出的所有处理组总RNA质量浓度均高于100 ng/μL，A260/A280均在2.0左右，A260/A230均在1.8左右。RNA质量浓度符合后续试验，提取纯度较高。
2.6.2 叶绿素代谢相关基因qRT-PCR及扩增产物验证
当样本引物片段扩增结束，对基因的溶解曲线进行观察，所有样本均具有溶解曲线并仅存在单一特征峰且特征峰初现位置和PCR反应退火反应所示的位置一致。因此，qRT-PCR产物荧光片段符合后期试验需求，试验结果可用于后续分析。
2.6.3 叶绿素合成相关基因表达
本试验对调控叶绿素合成的12个关键基因在两组处理中的表达量进行测定（图6）。结果表明，自然生长组52 d，ZjALAD、ZjPBGD、ZjHEMD、ZjMgCH1、ZjMgCH3、ZjNADPH1、ZjNADPH2基因表达量上升；ZjMgCH2基因表达量几乎没有变化；ZjURO-D1、ZjURO-D2、ZjCPOX、ZjCHLM基因表达量下降。
![]()
图 6 不同处理组叶绿素合成相关基因相对表达量基因相对表达量是处理后样品相对于处理前样品的比例。The relative gene expression is the proportion of samples after treatment to samples before treatment.Figure 6. Relative expression levels of genes related to chlorophyll synthesis in different treatment groups低温处理组52 d后，ZjPBGD、ZjURO-D1、ZjMgCH2基因表达量上升；ZjALAD、ZjHEMD、ZjURO-D2、ZjCPOX、ZjMgCH1、ZjMgCH3、ZjCHLM、ZjNADPH1、ZjNADPH2基因表达量下降。
2.6.4 叶绿素降解相关基因表达
本试验测定了两个处理组参与调控叶绿素降解及叶绿素循环的6个关键基因的相对表达量（图7）。自然生长组52 d后，ZjOsCAO基因表达量上升；ZjHCAR、ZjNYC1、ZjCHLase、ZjPAO、ZjRCCR基因表达量下降。低温处理组52 d后，ZjOsCAO、ZjHCAR、ZjCHLase、ZjPAO、ZjRCCR基因表达量上升；ZjNYC1基因表达量下降。
![]()
图 7 不同处理组叶绿素降解相关基因相对表达量Figure 7. Relative expression of chlorophyll degradation related genes in different treatment groups before and after treatment3. 讨 论
3.1 低温胁迫对叶绿素的影响
叶绿素浓度是衡量草坪草生长状况、受胁迫程度和观赏价值的重要指标。叶绿素a和叶绿素b作为叶绿素合成产物，对植物叶绿素循环起到重要作用，是植物吸收光能的主要色素物质。叶绿素a是光合作用中心色素分子，通过电子传递，将光能最终转化为化学能；叶绿素b具有吸收和传递光能的作用[17]。植物体内叶绿素a与叶绿素b含量比例的变化会影响其对不同波长光的吸收效率。叶绿素a/b值高，有利于CO2转化为光合产物，提高光合效率[18]。同时叶绿素a/b值还与抗性有关，在不同灌木抗旱性[19]，耐盐碱[20]比较的相关研究中发现，抗性强的灌木，叶绿素a/b的值越大。本试验结果显示日本结缕草在低温胁迫下叶绿素、叶绿素a、叶绿素b含量均显著（P < 0.05）下降且叶绿素a的降解速度大于叶绿素b，叶绿素a/b值明显降低。在草坪草的研究中已发现，这可能是由于植株叶片在胁迫作用下发生光化学反应，生成大量O2−·，O2−·在叶片大量积累直接加速叶绿素a的降解，导致叶绿素a/b降低[21]。
3.2 低温胁迫对叶绿素代谢通路中酶活性的影响
结果显示日本结缕草在低温胁迫下抑制了叶绿素的合成，同时促进了叶绿素降解。目前已经在很多种植物的研究中发现叶绿素损失会受到温度的影响，在对玉米低温胁迫的研究发现，低温会抑制叶绿素合成过程中ALA向PBG的转化以及Mg-原卟啉Ⅸ（Mg-proto Ⅸ）到镁原卟啉Ⅸ甲脂（Mpe）的反应到至叶绿素合成减少含量下降，除此之外低温胁迫还会抑制叶绿素 a向叶绿素b的转化[22]。在水稻中的研究发现，低温会造成水稻ALA、Mg-Proto Ⅸ、叶绿素合成受阻，尿卟啉原Ⅲ（Uro Ⅲ）和卟啉环侧连脱羧形成粪卟啉原Ⅲ（Coprogen Ⅲ）产生物质积累，导致叶绿素合成受到抑制[23]。本试验对ALAD活性，PBGD活性和PBG含量进行测定，其中，ALAD在植物叶绿素合成过程中催化ALA向PBG转化；PBGD在植物叶绿素合成过程中催化PBG向Hmb转化；PBG是植物叶绿素合成过程中形成吡咯环前的关键物质。结果显示，NT组ALAD（不显著）、PBGD活性均有上升，CT组ALAD、PBGD（不显著）活性均下降。虽然两个处理组PBG含量均有下降，但CT组的降低的程度更显著。因此低温抑制了日本结缕草叶绿素的合成。
关于叶绿素降解本试验测定了CHLase、MDCase、PPH、PAO、RCCR的活性。其中，CHLase催化叶绿素a转化为脱脂基叶绿素a，开启了叶绿素降解的第一步；MDCase是催化去Mg2+的关键酶；PPH是催化Pein a脱植醇转化为Pheide a的关键酶；PAO是催化Pheide向红色叶绿素代谢产物（RCC）转化的关键酶；RCCR作为叶绿素降解最后褪绿反应的关键酶，催化RCC产生无色、蓝色荧光产物pFCC。在关于银杏叶片[24]、青梅果实[25]叶绿素降解的研究中显示，MDCase是叶绿素降解途径中较为重要的因子。与本试验结果一致，MDCase活性的结果显示，CT组显著上升（NT组略有下降），低温加速了叶绿素降解。在芹菜和菠菜黄化的研究中发现随着叶绿素含量下降，脱植基叶绿素a含量增多，CHLase活性也增加[26-27]。但之后的一些报道显示CHLase不直接参与调控叶绿素降解，例，关于大豆叶片的结果表明低氧可抑制叶片黄化，延缓叶绿素含量降低，但不影响CHLase活性的变化[28]；一些果蔬衰老过程中叶绿素酶活性水平的研究显示，CHLase活性随着组织衰老而降低[29-30]。在本试验中结果显示CHLase、RCCR活性两个处理组均有升高，但NT组显著，CT组并不显著，这可能是由于低温胁迫诱导了日本结缕草的衰老从而减缓了CHLase、RCCR活性的升高。黄炳茹老师课题组在匍匐剪股颖热胁迫的研究中对叶绿素合成酶、叶绿素降解酶及相关基因表达量进行了测定，发现PPH是调节热胁迫下叶绿素降解的关键酶[31]。但本研究的结果显示，PPH的活性NT组显著上升，CT组略有下降但不显著。可能是由于PPH在不同植物中的不同胁迫响应过程中对叶绿素降解具有不同作用。
3.3 低温胁迫对叶绿素代谢相关基因表达量的影响
基因表达量结果显示，低温胁迫下叶绿素合成途径中相关基因多数下调表达，降低叶绿素的合成。ZjMgCH2基因呈现上调表达，可能是MgCH家族较为重要的基因，在植物受到严重胁迫或者叶绿素合成能力极低的情况下通过上调表达起到防御作用，以提高叶绿素的合成维持植物体生存生长。但由于植株受到低温胁迫影响，叶绿素合成途径中整体酶活性的降低，最终未达到提高叶绿素合成的效果。
有研究发现，叶绿素降解可能是先由叶绿素b向叶绿素a的转化[32-33]。叶绿素a的含量随着叶绿素降解的加速而快速下降，由于叶绿素循环反应中ZjNYC1下调表达，对叶绿素b向叶绿素a的转化造成了抑制，因此，Ca/Cb将进一步降低。叶绿素降解相关基因大多呈上调表达，加快叶绿素的降解。ZjCHLase和ZjRCCR虽表现为下调表达但由其编码的产物CHLase和RCCR酶活性上升的结果可知，叶绿素降解途径相关基因下调表达的调控机制在低温胁迫下的叶绿素降解过程中响应较为滞后。
4. 结 论
本试验主要研究低温胁迫对日本结缕草叶绿素代谢的影响，通过对叶绿素合成降解通路中关键酶活性，重要中间产物活性以及相关基因表达量的测定。结果显示，低温胁迫不仅使日本结缕草叶绿素合成的速率降低，同时使叶绿素降解的速率加快。二者合力导致低温胁迫下日本结缕草叶绿素、叶绿素a、叶绿素b的浓度以及叶绿素a与叶绿素b的比值均显著下降。除此之外，还发现MDCase是叶绿素降解过程中日本结缕草响应低温胁迫的关键调控因子。本研究为日本结缕草滞绿机理相关研究奠定了基础，对培育日本结缕草优势滞绿品种具有重要意义。
-
![]()
图 1 森林健康评价指标体系一级结构、准则层的词云图和词频分析
Figure 1. Word cloud and frequency analysis of primary structure and criterion layer of forest health assessment indicator system
![]()
图 2 多尺度、多平台遥感技术评价森林健康概念图
Figure 2. Concept map of multi-scale, multi-platform remote sensing assessment of forest health
表 1 不同传感器在森林健康评价中的技术优势和主要评价内容
Table 1 Advantages and application of different sensors in forest health assessment
传感器
Sensor技术优势
Technical advantage主要不足
Main shortcomings特征参量
Feature parameter评价内容
Assessment contentRGB高分相机
RGB high-resolution camera低成本、高空间分辨率
Low cost, high spatial resolution成像质量受光照条件影响大、光谱信息有限
Image quality is influenced by illumination conditions, limited spectral informationRGB图像、纹理
RGB image, texture普适性好，一般作为遥感底图或摄影测量数据
Widely used as RS base map or photogrammetric data多光谱成像仪
Multispectral sensor几个波段的反射率
Reflectance of several bands异物同谱、同物异谱问题使数据解译困难
Difficult data interpretation due to synonyms spectrum多光谱图像、多通道反射率
Multispectral image, multi-channel reflectance树种分类、主要生化组分、物候、胁迫
Tree species classification, main biochemical components, phenology, stress高光谱成像仪
Hyperspectral sensor上百个窄波段的反射率
Reflectance of hundreds of narrow bands观测条件严格、数据量大、分析难度大
Strict observation condition and difficult data analysis图谱立方体
Hyperspectral cube树种分类、多种生化组分、光合作用、物候、早期胁迫
Tree species classification, photosynthesis, various biochemical components, phenology, early stress热红外相机
Thermal camera全天候、穿透性、精细温差
All weather, penetrating, fine temperature difference温度变化易受周围环境影响
Temperature variation is easily affected by the surrounding environment方向亮度温度、热红外图像
Directional brightness temperature, thermal infrared image森林火灾、森林干旱、光合作用、早期胁迫
Forest fire, drought, photosynthesis, early stress激光雷达
LiDAR穿透性、精细的地形和森林三维信息
Penetrating, fine terrain and 3D forest information缺少光谱、纹理信息
Lack of spectral and texture information点云廓线、波形
Point cloud profile, waveform地形、位置、生物量、蓄积量、冠层结构、叶面积指数
Topography, location, biomass, volume, canopy structure, LAI微波雷达
Microwave radar全天候，一定穿透性，可达地下
All weather, limited penetrating, reaching underground斑点噪声大、受地形影响
严重
Speckle noise and seriously affected by terrain后向散射、干涉系数、极化雷达图像
Backscattering, interference coefficient, polarimetric radar image地形、生物量、蓄积量、水分、土壤
Topography, biomass, volume, moisture, soil叶绿素荧光扫描仪
Chlorophyll fluorescence scanner获取日光诱导的叶绿素信息
Capturing chlorophyll information induced by sunlight受大气影响大、时空连续性有限
Affected by atmosphere, limited space-time continuity荧光参数、光化学效率
Fluorescence parameters, photochemical efficiency初级生产力、碳循环、物候、早期胁迫
Primary productivity, carbon cycle, phenology, early stress
下载: 导出CSV
-
[1] 许勤. 绿色就是固碳造林等于减排:记国家林业局造林司司长魏殿生[J]. 林业经济, 2003(2):10−11. Xu Q. Green means carbon sequestration and afforestation equals emission reduction: Wei Diansheng, director of afforestation department of State Forestry Administration[J]. Forestry Economy, 2003(2): 10−11.
[2] 高均凯. 深入研究森林健康积极探索中国森林健康之路[J]. 北京林业管理干部学院学报, 2005(1):22−25. Gao J K. Research on the implementation of forest health strategy in China[J]. State Academy of Forestry Administration Journal, 2005(1): 22−25.
[3] 王云霖. 我国人工林发展研究[J]. 林业资源管理, 2019(1):6−11. Wang Y L. Research on plantation development in China[J]. Forestry Resource Management, 2019(1): 6−11.
[4] 胡爽, 徐誉远, 王本洋. 我国森林健康评价方法综述[J]. 林业与环境科学, 2017, 33(1):90−96. doi: 10.3969/j.issn.1006-4427.2017.01.017 Hu S, Xu Y Y, Wang B Y. Review of forest health monitoring and assessment in China[J]. Forestry and Environmental Science, 2017, 33(1): 90−96. doi: 10.3969/j.issn.1006-4427.2017.01.017
[5] 王秋燕, 陈鹏飞, 李学东, 等. 森林健康评价方法综述[J]. 南京林业大学学报(自然科学版), 2018, 42(2):177−183. Wang Q Y, Chen P F, Li X D, et al. Review of forest health assessment methods[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2018, 42(2): 177−183.
[6] Pause M, Schweitzer C, Rosenthal M, et al. In situ/remote sensing integration to assess forest health: a review[J/OL]. Remote Sensing, 2016, 8(6): 471 [2021−01−12]. https://doi.org/10.3390/rs8060471.
[7] 金瑞华, 张宏名, 林培. 浅谈生物灾害遥感监测[J]. 遥感信息, 1991(3):35−37. Jin R H, Zhang H M, Lin P. Discussion on remote sensing monitoring of biological disasters[J]. Remote Sensing Information, 1991(3): 35−37.
[8] 高广磊, 信忠保, 丁国栋, 等. 基于遥感技术的森林健康研究综述[J]. 生态学报, 2013, 33(6):1675−1689. doi: 10.5846/stxb201112011838 Gao G L, Xin Z B, Ding G D, et al. Forest health studies based on remote sensing: a review[J]. Acta Ecologica Sinica, 2013, 33(6): 1675−1689. doi: 10.5846/stxb201112011838
[9] 何兴元, 任春颖, 陈琳, 等. 森林生态系统遥感监测技术研究进展[J]. 地理科学, 2018, 38(7):997−1011. He X Y, Ren C Y, Chen L, et al. The progress of forest ecosystems monitoring with remote sensing techniques [J]. Scientia Geographica Sinica, 2018, 38(7): 997−1011.
[10] 王彦辉, 肖文发, 张星耀. 森林健康监测与评价的国内外现状和发展趋势[J]. 林业科学, 2007, 43(7):84−91. Wang Y H, Xiao W F, Zhang X Y. Current status and development tendency of forest health monitoring and evaluation[J]. Scientia Silvae Sinicae, 2007, 43(7): 84−91.
[11] 马有国, 杜学惠. 森林健康评价的遥感技术研究[J]. 森林工程, 2019, 35(2):37−44. Ma Y G, Du X H. Remote sensing technology for forest health assessment[J]. Forest Engineering, 2019, 35(2): 37−44.
[12] Lausch A, Borg E, Bumberger J, et al. Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches[J/OL]. Remote Sensing, 2018, 10(7): 1120 [2021−01−15]. https://doi.org/10.3390/rs10071120.
[13] Lausch A, Stefan E, Douglas K, et al. Understanding forest health with remote sensing (I): a review of spectral traits, processes and remote sensing characteristics[J/OL]. Remote Sensing, 2016, 8(10): 1029 [2020−12−19]. https://doi.org/10.3390/rs8121029.
[14] Lausch A, Stefan E, Douglas K, et al. Understanding forest health with remote sensing (II): a review of approaches and data models[J/OL]. Remote Sensing, 2017, 9(2): 129 [2020−12−28]. https://doi.org/10.3390/rs10071120.
[15] 高均凯. 森林健康基本理论及评价方法研究[D]. 北京: 北京林业大学, 2007. Gao J K. Basic theory and evaluation method of forest health[D]. Beijing: Beijing Forestry University, 2007.
[16] Costanza R. Toward an operational definition of ecosystem health [M]//Costanza R, Norton B G, Haskell B D, eds. Ecosystem health: new goals for environmental management. Washington D C: Island Press, 1992: 239−256.
[17] Senf C, Seidl R, Hostert P. Remote sensing of forest insect disturbances: current state and future directions[J]. International Journal of Applied Earth Observation & Geoinformation, 2017, 60: 49−60.
[18] Felbermeier B, Hahn A, Schneider T. Study on user requirements for remote sensing applications in forestry[C/OL]. ISPRS TC VII Symposium – 100 Years ISPRS. 2010 [2021−02−13]. https://www.researchgate.net/profile/Thomas-Schneider-10/publication/266412426_STUDY_ON_USER_REQUIREMENTS_FOR_REMOTE_SENSING_APPLICATIONS_IN_FORESTRY/links/55b6566a08ae092e9655d92c/STUDY-ON-USER-REQUIREMENTS-FOR-REMOTE-SENSING-APPLICATIONS-IN-FORESTRY.pdf.
[19] Fabian E, Hooman L, Krzysztof S, et al. Review of studies on tree species classification from remotely sensed data[J]. Remote Sensing of Environment, 2016, 186: 64−97. doi: 10.1016/j.rse.2016.08.013
[20] 孔嘉鑫, 张昭臣, 张健. 基于多源遥感数据的植物物种分类与识别: 研究进展与展望[J]. 生物多样性, 2019, 27(7):796−812. doi: 10.17520/biods.2019197 Kong J X, Zhang Z C, Zhang J. Classification and identification of plant species based on multi-source remote sensing data: research progress and prospect[J]. Biodiversity Science, 2019, 27(7): 796−812. doi: 10.17520/biods.2019197
[21] Ferreira M, Zortea M, Zanotta D, et al. Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data[J]. Remote Sensing of Environment, 2016, 179: 66−78. doi: 10.1016/j.rse.2016.03.021
[22] Asner G. Biophysical and biochemical sources of variability in canopy reflectance[J]. Remote Sensing of Environment, 1998, 64(3): 234−253. doi: 10.1016/S0034-4257(98)00014-5
[23] Clark M, Roberts D, Clark B. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales[J]. Remote Sensing of Environment, 2005, 96(3−4): 375−398. doi: 10.1016/j.rse.2005.03.009
[24] Leckie D, Tinis S, Nelson T, et al. Issues in species classification of trees in old growth conifer stands[J]. Canadian Journal of Remote Sensing, 2005, 31(2): 175−190. doi: 10.5589/m05-004
[25] 马鸿伟, 刘海, 姚顺彬, 等. 基于林业遥感的树种分类应用分析与展望[J]. 林业资源管理, 2020(3):118−121. Ma H W, Liu H, Yao S B, et al. Application and prospect of tree species classification based on forest remote sensing[J]. Forestry Resource Management, 2020(3): 118−121.
[26] Cao J, Leng W, Liu K, et al. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models[J/OL]. Remote Sensing, 2018, 10(2): 89 [2021−01−16]. https://doi.org/10.3390/rs10010089.
[27] Franklin S, Hall R, Moskal L, et al. Incorporating texture into classification of forest species composition from airborne multispectral images[J]. International Journal of Remote Sensing, 2000, 21(1): 61−79. doi: 10.1080/014311600210993
[28] Sayn-Wittgenstein L. Recognition of tree species on aerial photographs[J/OL]. Forest Management Institute Information Report, 1978, FMR-X-118 [2021−01−19]. https://cfs.nrcan.gc.ca/pubwarehouse/pdfs/29443.pdf.
[29] Mallinis G, Koutsias N, Tsakiri-Strati M, et al. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2008, 63(2): 237−250.
[30] Chuine I, Beaubien E. Phenology is a major determinant of tree species range[J]. Ecology Letters, 2001, 4: 500−510. doi: 10.1046/j.1461-0248.2001.00261.x
[31] Gärtner P, Förster M, Kleinschmit B. The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring[J]. Remote Sensing of Environment, 2016, 177: 237−247. doi: 10.1016/j.rse.2016.01.028
[32] Signoroni A, Savardi M, Baronio A, et al. Deep learning meets hyperspectral image analysis: a multidisciplinary review[J/OL]. Journal of Imaging, 2019, 5(5): 52 [2021−02−13]. https://doi.org/10.3390/jimaging5050052.
[33] 徐岩, 张聪伶, 降瑞娇, 等. 无人机高光谱影像与冠层树种多样性监测[J]. 生物多样性, 2021, 29(5):647−660. doi: 10.17520/biods.2021013 Xu Y, Zhang C L, Xiang R J, et al. UAV-based hyperspectral images and monitoring of canopy tree diversity[J]. Biodiversity Science, 2021, 29(5): 647−660. doi: 10.17520/biods.2021013
[34] Asner G, Knapp D, Kennedy-Bowdoin T, et al. Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR[J]. Remote Sensing of Environment, 2008, 112(5): 1942−1955. doi: 10.1016/j.rse.2007.11.016
[35] Riaño D, Chuvieco E, Condés S. Generation of crown bulk density for Pinus sylvestris L. from lidar[J]. Remote Sensing of Environment, 2004, 92: 345−352. doi: 10.1016/j.rse.2003.12.014
[36] Coops N, Hilker T, Wulder M, et al. Estimating canopy structure of douglas-fir forest stands from discrete-return LiDAR[J]. Trees, 2007, 21: 295−310. doi: 10.1007/s00468-006-0119-6
[37] Suratno A, Seielstad C, Queen L. Tree species identification in mixed coniferous forest using airborne laser scanning[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2009, 64(6): 683−693.
[38] Korpela I, Orka H, Hyyppae J, et al. Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2010, 65(4): 369−379.
[39] Kim S, McGaughey R, Andersen H, et al. Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data[J]. Remote Sensing of Environment, 2009, 113(8): 1575−1586. doi: 10.1016/j.rse.2009.03.017
[40] 黄华国. 激光雷达技术在林业科学研究中的进展分析[J]. 北京林业大学学报, 2013, 35(3):134−143. Huang H G. Progress analysis of LiDAR research on forestry science studies[J]. Journal of Beijing Forestry University, 2013, 35(3): 134−143.
[41] 郭庆华, 刘瑾, 李玉美, 等. 生物多样性近地面遥感监测: 应用现状与前景展望[J]. 生物多样性, 2016, 24(11):1249−1266. doi: 10.17520/biods.2016059 Guo Q H, Liu J, Li Y M, et al. A near-surface remote sensing platform for biodiversity monitoring: perspectives and prospects[J]. Biodiversity Science, 2016, 24(11): 1249−1266. doi: 10.17520/biods.2016059
[42] Michez A, Piegay H, Lisein J, et al. Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system[J/OL]. Environmental Monitoring & Assessment, 2016, 188: 146 [2021−01−19]. https://link.springer.com/article/10.1007/s10661-015-4996-2.
[43] Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance[J]. International Journal of Remote Sensing, 2007, 28: 823−870. doi: 10.1080/01431160600746456
[44] Cheng J, Bo Y, Zhu Y, et al. A novel method for assessing the segmentation quality of high-spatial resolution remote-sensing images[J]. International Journal of Remote Sensing, 2014, 35: 3816−3839. doi: 10.1080/01431161.2014.919678
[45] Aguirre-Gutiérrez J, Seijmonsbergen A, Duivenvoorden J. Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico[J]. Applied Geography, 2012, 34: 29−37. doi: 10.1016/j.apgeog.2011.10.010
[46] Larsen M, Eriksson M, Descombes X, et al. Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions[J]. International Journal of Remote Sensing, 2011, 32(20): 5827−5852. doi: 10.1080/01431161.2010.507790
[47] 肖风劲, 欧阳华, 孙江华, 等. 森林生态系统健康评价指标与方法[J]. 林业资源管理, 2004(1):27−30. doi: 10.3969/j.issn.1002-6622.2004.01.007 Xiao F J, Ouyang H, Sun J H, et al. Forest ecosystem health assessment indicators and methods[J]. Forest Resources Management, 2004(1): 27−30. doi: 10.3969/j.issn.1002-6622.2004.01.007
[48] Schlemmer M, Gitelson A, Schepers J, et al. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 25: 47−54.
[49] Inoue Y, Sakaiya E, Zhu Y, et al. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements[J]. Remote Sensing of Environment, 2012, 126: 210−221. doi: 10.1016/j.rse.2012.08.026
[50] Xie Q, Huang W, Liang D, et al. Leaf area index estimation using vegetation indices derived from airborne hyperspectral images in winter wheat[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3586−3594. doi: 10.1109/JSTARS.2014.2342291
[51] 黄华国. 林业定量遥感研究进展和展望[J]. 北京林业大学学报, 2019, 41(12):1−14. doi: 10.12171/j.1000-1522.20190326 Huang H G. Progress and prospect of quantitative remote sensing in forestry[J]. Journal of Beijing Forestry University, 2019, 41(12): 1−14. doi: 10.12171/j.1000-1522.20190326
[52] 黄华国. 三维遥感机理模型RAPID原理及其应用[J]. 遥感技术与应用, 2019, 34(5):901−913. Huang H G. Principles and applications of the three-dimensional remote sensing mechanism model RAPID[J]. Remote Sensing Technology and Application, 2019, 34(5): 901−913.
[53] Paul J. Remote sensing of foliar chemistry[J]. Remote Sensing of Environment, 1989, 30(3): 271−278. doi: 10.1016/0034-4257(89)90069-2
[54] Daughtry C, Walthall C, Kim M, et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J]. Remote Sensing of Environment, 2000, 74: 229−239. doi: 10.1016/S0034-4257(00)00113-9
[55] Datt B. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves[J]. Remote Sensing of Environment, 1998, 66: 111−121. doi: 10.1016/S0034-4257(98)00046-7
[56] Asner G, Martin R. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests[J]. Frontiers in Ecology and the Environment, 2009, 7: 269−276. doi: 10.1890/070152
[57] Ustin S, Gitelson A, Jacquemoud S, et al. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy[J]. Remote Sensing of Environment, 2009, 113: 67−77. doi: 10.1016/j.rse.2008.10.019
[58] Malenovský Z, Homolová L, Zurita-Milla R, et al. Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer[J]. Remote Sensing of Environment, 2013, 131: 85−102. doi: 10.1016/j.rse.2012.12.015
[59] McManus K, Asner G, Martin R, et al. Phylogenetic structure of foliar spectral traits in tropical forest canopies[J/OL]. Remote Sensing, 2016, 8(3): 196 [2021−01−18]. https://doi.org/10.3390/rs8030196.
[60] Serrano L, Penuelas J, Ustin S. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals[J]. Remote Sensing of Environment, 2002, 81: 355−364. doi: 10.1016/S0034-4257(02)00011-1
[61] Thenkabail P, Lyon J, Huete A. Hyperspectral remote sensing of vegetation[M]. Boca Raton: CRC Press, 2016.
[62] Assal T, Anderson P, Sibold J. Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem[J]. Forest Ecology and Management, 2016, 365: 137−151. doi: 10.1016/j.foreco.2016.01.017
[63] Klosterman S, Hufkens K, Gray J, et al. Gray J, et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery[J]. Biogeosciences, 2014, 11: 4305−4320. doi: 10.5194/bg-11-4305-2014
[64] van Wittenberghe S, Verrelst J, Rivera J, et al. Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset[J]. Journal of Photochemistry and Photobiology B-Biology, 2014, 134: 37−48. doi: 10.1016/j.jphotobiol.2014.03.010
[65] Asner G, Knapp D, Anderson C, et al. Large-scale climatic and geophysical controls on the leaf economics spectrum[J]. PNAS, 2016, 113: 4043−4051. doi: 10.1073/pnas.1604863113
[66] Schlerf M, Atzberger C, Hill J, et al. Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies, L. Karst.) using imaging spectroscopy[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12: 17−26. doi: 10.1016/j.jag.2009.08.006
[67] 章钊颖, 王松寒, 邱博, 等. 日光诱导叶绿素荧光遥感反演及碳循环应用进展[J]. 遥感学报, 2019, 23(1):37−52. Zhang Z Y, Wang S H, Qiu B, et al. Remote sensing retrieval of sunlight induced chlorophyll fluorescence and application of carbon cycle[J]. Acta Remote Sensing, 2019, 23(1): 37−52.
[68] Potter C, Randerson J, Field C, et al. Terrestrial ecosystem production-A process model-based on global satellite and surface data[J]. Global Biogeochemical Cycles, 1993, 7: 811−841. doi: 10.1029/93GB02725
[69] Li X, Xiao J, He B, et al. Chlorophyll fluorescence observed by OCO-2 is strongly related to gross primary productivity estimated from flux towers in temperate forests[J]. Remote Sensing of Environment, 2018, 204: 659−671. doi: 10.1016/j.rse.2017.09.034
[70] 郭庆华, 胡天宇, 马勤, 等. 新一代遥感技术助力生态系统生态学研究[J]. 植物生态学报, 2020, 44(4):418−435. doi: 10.17521/cjpe.2019.0206 Guo Q H, Hu T Y, Ma Q, et al. Advances for the new remote sensing technology in ecosystem ecology research[J]. Chinese Journal of Plant Ecology, 2020, 44(4): 418−435. doi: 10.17521/cjpe.2019.0206
[71] Frydenvang J, Maarschalkerweerd M, Carstensen A, et al. Sensitive detection of phosphorus deficiency in plants using chlorophyll a fluorescence[J]. Plant Physiology, 2015, 169: 353−361. doi: 10.1104/pp.15.00823
[72] Sun Y, Frankenberg C, Wood J, et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence[J]. Science, 2017, 358: 747.
[73] Wolanin A, Rozanov V, Dinter T, et al. Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results[J]. Remote Sensing of Environment, 2015, 166: 243−261. doi: 10.1016/j.rse.2015.05.018
[74] Köhler P, Guanter L, Joiner J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data[J]. Atmospheric Measurement Techniques, 2015, 8(6): 2589−2608. doi: 10.5194/amt-8-2589-2015
[75] Mcroberts R, Næsset E, Gobakken T. Inference for lidar-assisted estimation of forest growing stock volume[J]. Remote Sensing of Environment, 2013, 128: 268−275. doi: 10.1016/j.rse.2012.10.007
[76] Cartus O, Kellndorfer J, Rombach M. Mapping canopy height and growing stock volume using airborne lidar, ALOS PALSAR and Landsat ETM+[J]. Remote Sensing, 2012, 4: 3320−3345. doi: 10.3390/rs4113320
[77] 李增元, 赵磊, 李堃, 等. 合成孔径雷达森林资源监测技术研究综述[J]. 南京信息工程大学学报, 2020, 12(2):150−158. Li Z Y, Zhao L, Li K, et al. A survey of developments on forest resources monitoring technology of synthetic aperture radar[J]. Journal of Nanjing University of Information Technology, 2020, 12(2): 150−158.
[78] 李亚东, 冯仲科, 明海军, 等. 无人机航测技术在森林蓄积量估测中的应用[J]. 测绘通报, 2017(4):63−66. Li Y D, Feng Z K, Ming H J, et al. Application of UAV aerial survey technology in forest volume estimation[J]. Surveying and Mapping Bulletin, 2017(4): 63−66.
[79] 黄麟, 张晓丽, 石韧. 森林病虫害遥感监测技术研究的现状与问题[J]. 遥感信息, 2006(2):71−75. doi: 10.3969/j.issn.1000-3177.2006.02.020 Huang L, Zhang X L, Shi R. Current status and problems in monitoring forest damage caused by diseases and insects based on remote sensing[J]. Remote Sensing Information, 2006(2): 71−75. doi: 10.3969/j.issn.1000-3177.2006.02.020
[80] 王蕾, 骆有庆, 张晓丽, 等. 遥感技术在森林病虫害监测中的应用研究进展[J]. 世界林业研究, 2008, 21(5):37−43. Wang L, Luo Y Q, Zhang X L, et al. Application development of remote sensing technology in the assessment of forest pest disaster[J]. World Forestry Research, 2008, 21(5): 37−43.
[81] Stone C, Mohammed C. Application of remote sensing technologies for assessing planted forests damaged by insect pests and fungal pathogens: a review[J]. Current Forestry Reports, 2017, 3(2): 1−18.
[82] Hall R J, van der Sanden J J, Freeburn J T, et al. Remote sensing of natural disturbance caused by insect defoliation and dieback: a review[R]. Edmonton: Natural Resources Canada, 2016.
[83] Coops N, Johnson M, Wulder M, et al. Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation[J]. Remote Sensing of Environment, 2006, 103(1): 67−80. doi: 10.1016/j.rse.2006.03.012
[84] Bright B, Hudak A, McGaughey R, et al. Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar[J]. Canadian Journal of Remote Sensing, 2013, 39: 99−111. doi: 10.5589/m13-027
[85] Carter G, Knapp A. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration[J]. American Journal of Botany, 2001, 88: 677−684. doi: 10.2307/2657068
[86] Ustin S, Roberts D, Gamon J, et al. Using imaging spectroscopy to study ecosystem processes and properties[J]. Bioscience, 2004, 54: 523−534. doi: 10.1641/0006-3568(2004)054[0523:UISTSE]2.0.CO;2
[87] Niemann K, Quinn G, Stephen R, et al. Hyperspectral remote sensing of mountain pine beetle with an emphasis on previsual assessment[J]. Canadian Journal of Remote Sensing, 2015, 41(3): 191−202. doi: 10.1080/07038992.2015.1065707
[88] Hernández-Clemente R, Hornero A, Mottus M, et al. Early diagnosis of vegetation health from high-resolution hyperspectral and thermal imagery: lessons learned from empirical relationships and radiative transfer modelling[J]. Current Forestry Reports, 2019, 5: 169−183. doi: 10.1007/s40725-019-00096-1
[89] Abdullah H, Darvishzadeh R, Skidmore A, et al. European spruce bark beetle (Ips typographus L.) green attack affects foliar reflectance and biochemical properties[J]. International Journal of Applied Earth Observation & Geoinformation, 2018, 64: 199−209.
[90] Peterson D, Aber J, Matson P, et al. Remote sensing of forest canopy and leaf biochemical contents[J]. Remote Sensing of Environment, 1988, 24: 85−108. doi: 10.1016/0034-4257(88)90007-7
[91] 徐华潮, 骆有庆, 张廷廷, 等. 松材线虫自然侵染后松树不同感病阶段针叶光谱特征变化[J]. 光谱学与光谱分析, 2011, 31(5):1352−1356. doi: 10.3964/j.issn.1000-0593(2011)05-1352-05 Xu H C, Luo Y Q, Zhang T T, et al. Changes of reflectance spectra of pine needles in different stage after being infected by pine wood nematode[J]. Spectroscopy and Spectral Analysis, 2011, 31(5): 1352−1356. doi: 10.3964/j.issn.1000-0593(2011)05-1352-05
[92] Liu Y J, Zhan Z Y, Ren L L, et al. Hyperspectral evidence of early-stage pine shoot beetle attack in Yunnan pine[J]. Forest Ecology and Management, 2021, 497: 1118−1127.
[93] Yu L, Zhan Z, Ren L, et al. Evaluating the potential of worldview-3 data to classify different shoot damage ratios of Pinus yunnanensis[J/OL]. Forests, 2020, 11: 417 [2021−01−14]. https://doi.org/10.3390/f11040417.
[94] Zhan Z, Yu L, Ren L, et al. Combining GF-2 and Sentinel-2 images to detect tree mortality caused by red turpentine beetle during the early outbreak stage in north China[J]. Forests, 2020, 11(2): 172. doi: 10.3390/f11020172
[95] Senf C, Pflugmacher D, Hostert P, et al. Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130: 453−463. doi: 10.1016/j.isprsjprs.2017.07.004
[96] Yu L, Huang J, Zong S, et al. Detecting shoot beetle damage on Yunnan pine using Landsat time-series data[J/OL]. Forests, 2018, 9: 39 [2021−01−19]. https://doi.org/10.3390/f9010039.
[97] Gašparović M, Jurjević L. Gimbal influence on the stability of exterior orientation parameters of UAV acquired images[J/OL]. Sensors, 2017, 17(2): 401 [2021−01−15]. https://doi.org/10.3390/s17020401.
[98] Näsi R, Honkavaara E, Paivi L, et al. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level[J]. Remote Sensing, 2015, 7: 15467−15493. doi: 10.3390/rs71115467
[99] Yu R, Luo Y Q, Zhou Q, et al. A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level[J/OL]. International Journal of Applied Earth Observation and Geoinformation, 2021, 101(1): 102363 [2021−01−21]. https://doi.org/10.1016/j.jag.2021.102363.
[100] 鲁芮伶, 杜莹, 晏黎明, 等. 森林树木死亡的判定方法及其应用综述[J]. 科学通报, 2019, 64(23):2395−2409. Lu R L, Du Y, Yan L M, et al. A review of forest tree death determination methods and their application[J]. Scientific Bulletin, 2019, 64(23): 2395−2409.
[101] Parker J, Patton R. Effects of drought and defoliation on some metabolites in roots of black oak seedlings[J]. Canadian Journal of Forest Research, 1975, 5: 457−463. doi: 10.1139/x75-063
[102] Hartmann H, Ziegler W, Trumbore S. Lethal drought leads to reduction in nonstructural carbohydrates in Norway spruce tree roots but not in the canopy[J]. Functional Ecology, 2013, 27: 413−427. doi: 10.1111/1365-2435.12046
[103] Adams H, Zeppel M, Anderegg W, et al. A multi-species synthesis of physiological mechanisms in drought-induced tree mortality[J]. Nature Ecology and Evolution, 2017, 1: 1285−1291. doi: 10.1038/s41559-017-0248-x
[104] 周国逸, 李琳, 吴安驰. 气候变暖下干旱对森林生态系统的影响[J]. 南京信息工程大学学报(自然科学版), 2020, 12(1):81−88. Zhou G Y, Li L, Wu A C. Effect of drought on forest ecosystem under warming climate[J]. Journal of Nanjing University of Information Technology (Nature Science Edition), 2020, 12(1): 81−88.
[105] Bowman W. The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves[J]. Remote Sensing of Environment, 1989, 30(3): 249−255. doi: 10.1016/0034-4257(89)90066-7
[106] 郝增超, 侯爱中, 张璇, 等. 干旱监测与预报研究进展与展望[J]. 水利水电技术, 2020, 51(11):30−40. Hao Z C, Hou A Z, Zhang X, et al. Study progresses and prospects of drought monitoring and prediction[J]. Water Conservancy and Hydropower Technology, 2020, 51(11): 30−40.
[107] 周磊, 武建军, 张洁. 以遥感为基础的干旱监测方法研究进展[J]. 地理科学, 2015, 35(5):630−636. Zhou L, Wu J J, Zhang J. Remote sensing-based drought monitoring approach and research progress[J]. Scientia Geographica Sinica, 2015, 35(5): 630−636.
[108] Li X, Xiao J, He B. Higher absorbed solar radiation partly offset the negative effects of water stress on the photosynthesis of Amazon forests during the 2015 drought[J/OL]. Environmental Research Letters, 2018, 13(4): 044005 [2020−10−12]. https://iopscience.iop.org/article/10.1088/1748-9326/aab0b1.
[109] Reddy A, Chaitanya K, Vivekanandan M. Drought-induced responses of photosynthesis and antioxidant metabolism in higher plants[J]. Journal of Plant Physiology, 2004, 161(11): 1189−1202. doi: 10.1016/j.jplph.2004.01.013
[110] Lambers H, Chapin F, Chapin F, et al. Plant water relations in plant physiological ecology[M]. New York: Springer, 2008: 163−217.
[111] Breshears D, Cobb N, Rich P, et al. Regional vegetation die-off in response to global change-type drought[J]. PNAS, 2005, 102(42): 15144−15148. doi: 10.1073/pnas.0505734102
[112] Running S, Nemani R, Heinsch F, et al. A continuous satellite-derived measure of global terrestrial primary production[J]. Bioscience, 2004, 54(6): 547−560. doi: 10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
[113] Huang C, Asner G, Barger N, et al. Regional aboveground live carbon losses due to drought-induced tree dieback in Pinyon-Juniper ecosystems[J]. Remote Sensing of Environment, 2010, 114(7): 1471−1479. doi: 10.1016/j.rse.2010.02.003
[114] Clifford M, Cobb N, Buenemann M. Long-term tree cover dynamics in a Pinyon-Juniper woodland: climate-change-type drought resets successional clock[J]. Ecosystems, 2011, 14(6): 949−962. doi: 10.1007/s10021-011-9458-2
[115] Suárez L, Zarco-Tejada P, Sepulcre-Cantó G, et al. Assessing canopy PRI for water stress detection with diurnal airborne imagery[J]. Remote Sensing of Environment, 2008, 112(2): 560−575. doi: 10.1016/j.rse.2007.05.009
[116] 黄华国. 森林病虫害定量遥感模型进展[C]//第一届植被病虫害遥感大会. 北京: 中国科学院空天信息创新研究院, 2020. Huang H G. Advances in quantitative remote sensing models of forest diseases and insect pests[C]//The first congress on remote sensing of plant diseases and insect pests. Beijing: Aerospace Information Research Institute, Chinese Academy of Sciences, 2020.
[117] 李树涛, 李聪妤, 康旭东. 多源遥感图像融合发展现状与未来展望[J]. 遥感学报, 2021, 25(1):148−166. Li S T, Li C Y, Kang X D. Development status and future prospects of multi-source remote sensing image fusion[J]. National Remote Sensing Bulletin, 2021, 25(1): 148−166.
[118] 武士翔. “科技蓝 + 森林绿”助力雄安生态格局: 基于雄安新区植树造林项目的研究[J]. 城市建筑, 2019, 16(26):149−150. doi: 10.3969/j.issn.1673-0232.2019.26.049 Wu S X. “Science and technology blue + forest green” helps Xiong’an ecological pattern based on the afforestation project in Xiong’an new district[J]. Urban Architecture, 2019, 16(26): 149−150. doi: 10.3969/j.issn.1673-0232.2019.26.049
[119] Gorelick N, Hancher M, Dixon M, et al. Google earth engine: planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202: 18−27. doi: 10.1016/j.rse.2017.06.031
计量
- 文章访问数: 3474
- HTML全文浏览量: 799
- PDF下载量: 631
